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The best AI tool your sales team built this quarter is the reason you’re going to miss your number.

It’s slick. It works. It saves four hours a week. It’s also a sales director doing rev ops work instead of being on customer calls, which is the only thing they were actually hired to do. Adrian Rosenkranz, the CRO at Webflow, calls this the efficiency trap. After spending an hour with him on the podcast last week, I’m convinced it’s the single biggest threat to GTM productivity right now, and almost nobody is naming it.

Adrian ran global sales at Tableau before Webflow, scaling a multi-billion-dollar org across enterprise, SMB, and self-serve. He’s now sixteen months into the CRO seat at Webflow, supporting 300,000 customers and watching two simultaneous rewires of go-to-market play out in real time. One outside the building, one inside it.

The outside view: 20% of traffic across Webflow’s customer base is now AI bots. Sixteen months ago, that number was less than 1%. Forty-three percent of that bot traffic is agents pulling live answers for a human buyer who will never click your link. AEO has quietly replaced SEO as the discovery layer for B2B software. Most revenue leaders haven’t updated a single dashboard to reflect it.

The inside view: every revenue team is now buried under well-meaning AI hacks. Sales managers building coaching tools. Reps wiring up call-prep agents. Marketing building its own version of the same workflow rev ops is also building. Each project feels like a win. Almost none of them move the actual outcome.

Adrian’s diagnostic for whether your AI investments are working is the most useful question I’ve heard a CRO ask in years, and I’ve already stolen it.

Six takeaways from a conversation that genuinely changed how I’m thinking about the next twelve months.

1. The Efficiency Trap Is a Leadership Failure

Adrian told me a story I keep replaying.

A sales director on his team walked him through an AI coaching tool he’d built. Cool architecture. Real engineering. The kind of side project that makes a manager look forward-thinking in a 1:1. Adrian listened, nodded, and asked one question.

“Are you on more customer calls?”

The director paused. “What?”

“Yeah. This is nice. Are you on more calls?”

That’s the cleanest articulation of the efficiency trap I’ve heard. Anyone who gets really good at responding to emails ends up getting more emails. Anyone who gets really good at building AI workflows ends up building more AI workflows. Productivity at the input has almost nothing to do with productivity at the outcome.

Stuart Butterfield (Slack Founder) has a phrase I’ve stolen for moments like this: hyper-realistic work-like activity. The actions that look like work, feel like work, and produce nothing real. They’re seductive because the dopamine hit of finishing a task is real. The business value is the part that’s missing.

The instinct in modern GTM is to give every rep their own AI stack. Build your own call-prep agent. Build your own follow-up automation. Build your own coaching loop. The thinking is that decentralization equals empowerment. The reality is that you’ve taken your highest-paid customer-facing employees and turned them into part-time rev ops engineers. Their tools usually work. They also take hours to build, hours to maintain, and ship a fraction of what your applied AI team would deliver in the same week.

Adrian’s reorg is the cleanest answer I’ve seen. He collapsed marketing ops, sales ops, post-sales ops, and rev ops into one GTM engineering team. Their job is the connective tissue: the workflows that cross departments, the agents that share context, the eval frameworks that measure whether the system is working. Customer-facing reps are not on this team. They’re with customers.

This is Goodhart’s Law in modern dress. When a measure becomes a target, it stops being a good measure. Track customer minutes. Everything else is theatre.

A few months ago I wrote a Substack arguing for centralized AI deployment in GTM. It became my most popular post by 3X. People felt it. They were watching their best people drift away from customers and into config files, and they didn’t have clean language for why it was bad. Adrian gave it to me.

Every AI investment in GTM should be measured by one question. Are reps spending more time with humans? If the answer is no, the strategy is the problem.

2. The Narrative Funnel Beats Feature Enablement

Your product team is shipping faster than your enablement team can possibly keep up. Reps can’t memorize what shipped last week. Customers ask about features that didn’t exist last quarter. The default response is more enablement: more decks, more videos, more SKO sessions, more Slack channels nobody reads.

Adrian’s argument is that the entire frame is wrong. Reps learn the narrative first. Features fit inside it.

He uses an analogy I’m going to ruin in dozens of 1:1s. Imagine your manager surprise-Slacks the team on a Tuesday: “Drop everything, we’re doing a cold blitz right now.” Cue eye roll. Same manager, same activity, but on a standing Monday at 9am: “It’s Monday, we’re doing the blitz.” Suddenly it’s the run of the business. Same activity. Different frame. Different meaning.

A great narrative is the Monday cold blitz of go-to-market. When you have a clear story about how the world is changing and what your customer should do about it, every new feature becomes a slot in that story instead of a separate thing to memorize. Webflow’s narrative is that web traffic is fundamentally shifting from human-only to human-plus-agent. Every feature they ship plugs into that story. When Markdown for Agents launched, reps didn’t have to learn a new pitch. They just connected the dots.

Andy Raskin has been arguing for years that strategic narrative is the highest-leverage piece of GTM content a company can produce. Most companies have positioning. Very few have narrative. The difference becomes obvious the moment your PMs start shipping faster than your enablement team can keep up.

There’s a second move Adrian makes that’s worth stealing. He categorizes every new feature into one of two buckets. Reason-to-call features get a proactive motion: an agent scans his Slack channels daily, matches new features to the ICP, and surfaces which open deals should hear about it. Ambient features just get added to the knowledge base and surfaced in Slack via a bot when a rep asks.

Most teams treat every release as a reason to call. That’s why your reps are tired and your buyers are tuning out.

If your narrative is strong, features get easier. If your narrative is weak, every release is a fire drill.

3. 20% of Your Traffic Is Now Non-Human

The numbers will hit you.

Sixteen months ago, less than 1% of traffic across Webflow’s 300,000-customer base came from AI bots. Today it’s nearly 20%. And that 20% isn’t a homogenous blob of model-training crawlers. Forty-three percent of it is live agents fetching answers in real time for a human buyer.

When ChatGPT changed how it surfaced citations last August, moving from inline links to aggregated source lists, Webflow saw a 30 to 40% drop in referrals overnight. The traffic didn’t disappear. It moved. Buyers started getting their answers inside the LLM and typing webflow.com directly into their browser, skipping the click entirely.

This is the single biggest shift in B2B discovery since SEO became a discipline. SEO competes for a click. AEO (agent experience optimization) competes for a citation, which is to say, for whether the LLM mentions you at all when a buyer asks a question.

The good news: SEO and AEO overlap. Authority, freshness, and structured content help both. The new work is mostly architectural. Schema markup so agents can interpret the page (per the schema.org standard). Markdown versions of every page so agents can extract clean content fast. Metadata that tells an LLM what your page actually is.

Adrian uses a sharp visual to explain the gap. Look at any beautifully designed Webflow site, like Lando Norris’s. Animations, gradients, micro-interactions, the works. Then right-click and inspect. That stripped-down code is the only thing the agent sees. The animations are for humans. The markdown is for everyone else.

If you’re a CRO, the takeaway is brutal in its simplicity. You need a dashboard that tracks your share of LLM citations the way you track your share of search results. You need someone whose job is AEO. And you need to start thinking about your website as two simultaneous experiences: the one your buyer sees and the one their agent reads on their behalf.

The 6x conversion lift Webflow sees on referred traffic versus typed-in URL traffic is the lagging signal. The leading signal is whether you show up in the LLM at all.

4. Shape, or Be Shaped

Adrian was in France last month, sitting across from a customer, when he realized something he couldn’t unsee.

The customer was asking the LLM questions in real time. While Adrian was talking. About what Adrian was saying.

“Is this pricing fair?” “What are alternatives?” “What questions should I be asking him?”

He told me about it half-laughing, half-horrified. The exact thing CROs have been worried about in the abstract for two years is now happening in conference rooms in Paris. Your customer has a second analyst in the room and they can’t see it. You can’t either.

Adrian’s framing of this is the line I’ve been turning over in my head since we recorded.

“Are we going to step up and shape it, or are we going to be shaped by it?”

There is no third option. Either your messaging, your positioning, your competitive narrative, your pricing rationale, all of it, is structured cleanly enough that an LLM can fetch it and represent you accurately, or the LLM will improvise on your behalf using whatever it finds on G2, Reddit, and your competitor’s blog. It will not check with you first.

This is the part of the AEO conversation that goes well past schema markup. AEO is a content strategy. A messaging discipline. A competitive intelligence function. The companies that will win are the ones who decide right now that every objection, every alternative, every pricing question, every fairness comparison gets a clear, sourceable answer that an LLM can find and use.

If your strategy for AI buyer discovery is “we’ll just keep doing good marketing,” your LLM is improvising. And it’s not on your team.

5. Knowledge Bases Are No Longer Documents

Every revenue org has a knowledge base. Most of them are graveyards.

The onboarding deck from 2023. The win-loss report from Q2. The ICP doc your previous CMO commissioned three exec teams ago. They’re sitting in Notion or Drive or Guru, technically findable, functionally dead.

Adrian has rebuilt this entire concept and it’s the most copyable idea in our episode. His ICP doc isn’t a doc. It’s a markdown file that updates itself every week via a cron job. It pulls from closed-won deal data, customer call transcripts, Slack channels where the team is talking about deals, and external best practices he’s curated. Every Monday it produces a fresh version, and you can see how the ICP has drifted week over week.

The actual file contains anti-ICP patterns, ICP patterns, customer quotes that validate segmentation, jobs-to-be-done frameworks, and competitor data. Then every other AI agent in his stack consumes it as ground truth. The just-in-time call-script agent reads it. The deal-coaching agent reads it. The market-fit analysis agent reads it.

Why this matters: the value of any AI workflow is capped by the quality of the context it can access. A static ICP doc is technical debt. A self-updating one is leverage that compounds every week.

I’ve been building toward the same pattern. My VP of rev ops built something we call the Gardener. It scrapes Slack channels, Notion docs, and call recordings to keep a master business-context file alive across our org. Anything any agent in our stack does, it does with that file as the ground truth.

The future of GTM enablement isn’t going to be courses and decks. It’s going to be living context files that the entire team, human and agent, reads from. If your knowledge base doesn’t update itself, you’re already operating on stale information.

6. Context Is the New Moat. Walled Gardens Are Dead.

Legacy revenue tools sold you a story for the better part of a decade. “Our intelligence is the moat. Our data is the lock-in. The longer you use us, the more we know.”

That story is over.

Adrian said something in our conversation that perfectly captures where we are:

“I work really hard sometimes, it’s pretty funny how hard I work to make sure my Gong calls are not in Gong.”

I cut Gong off our stack two years ago for the same reason. Their walled-garden approach to call data was incompatible with how I wanted to use the intelligence. We moved to Momentum, which lets me push call context anywhere I want, run my own prompts on top of it, and feed it into whatever agent or workflow needs it. Same data, dramatically different leverage.

Adrian’s prediction: every tool that holds your context hostage is going to lose. The new moat is the opposite. It’s how much context the platform can feed into agents acting on your behalf. The vendors that thrive over the next three years will sound like, “here’s all our data, here’s all our APIs, here’s a clean way to plug us into your stack.” The ones that resist this are signaling weakness, not strength.

There’s a corollary for build-versus-buy worth pulling out. Adrian’s framework: buy systems you can build with AI on top of. Don’t rebuild governance, roles, or permissions. Don’t rebuild any infrastructure with a 1,000-day shelf life. Do build short-lived, throwaway tooling for things that need to exist for a quarter. The longevity of the workflow is the test.

If a vendor can’t tell you how an agent reads from and writes to their system, they’re not a long-term partner. They’re a short-term dependency.

What Actually Matters

Two things are happening at once and they’re related.

Outside your building, AI agents are inserting themselves between you and your buyers. Twenty percent of traffic. Forty-three percent of that, live queries. A buyer in a conference room in Paris quietly fact-checking the CRO across the table. The discovery layer for B2B software has fundamentally moved, and most revenue leaders haven’t updated a single dashboard to reflect it.

Inside your building, every revenue team is drowning in well-meaning AI hacks while the actual unlock sits one org redesign away. Centralize the engineering. Build living context files. Make sure every reorg ends with reps spending more time with customers, not less.

The CROs I’m watching pull ahead in 2026 have one thing in common. They’re using their own context, their own thinking, their own taste, to multiply their team. They’re modeling the way themselves. They’re getting their hands dirty.

If you take one thing from this conversation, take Adrian’s question. Walk into your next 1:1 with whoever’s been telling you about their AI projects and ask it.

“Are you on more customer calls?”

Listen to what comes back.

If this conversation hit, do me a favor and rate the podcast. Five stars on Apple or Spotify is the easiest way to make sure more conversations like this one happen. Adrian was generous with the playbooks. The least we can do is get more people listening.

Sales hiring has a charisma problem.

We’ve been running case studies and mock calls in interviews for decades. Every sales org does it. Nobody questions it. At Owner, we decided to question it. We pulled the data on case study scores and compared them to actual on-the-job performance.

The correlation was basically nonexistent.

That finding kicked off a much bigger reckoning with how we hire, how we assess talent, and what we actually value in a sales org. I sat down with Chris Balestras on the Vibescaling podcast recently and we went deep on all of it: why I moved on from nearly half the team I inherited, why I insist on uniform language across my org, why I invested in rev ops and enablement way earlier than conventional wisdom says you should, and why company selection in 2026 might be the highest-stakes career decision most sales leaders will ever make.

The thread connecting all of it? First principles thinking. Questioning the stuff we’ve always done because we’ve always done it. Running the data. And being willing to look stupid when the answer contradicts what everybody assumed.

Here’s what I shared.

Talent Assessment Is a Day-One Decision

When I joined Owner, the product was early. The team was small. And like most Series A sales orgs, there were a handful of reps doing all sorts of random stuff with no real process underneath them.

The decision to hire someone at my level meant the founders were all in. Adam and Dean had the vision. My job was to bring the intensity to match it. Everything about how we went to market had to come up multiple levels.

So I did what most new sales leaders avoid: I was hyper transparent about exactly who I am and what working in my org looks like. Tons of career opportunity. Coaching and training investment that’s hard to find anywhere else. But demanding. Career-focused. Not for the faint of heart.

One person opted out. We moved on from others. Nearly half the team turned over.

That sounds brutal, and I won’t pretend it wasn’t. But every single person who stayed is still here four years later, doing the best work of their careers.

The lesson I keep coming back to is that transparency is actually the kindest thing you can do in these situations. When I’m hiring at the director level, I send candidates a mutual reference list. Every person who has directly reported to me over the last ten-plus years. The ones that worked, the ones that didn’t work, my marketing and CS peers. I tell them: go nuts. Some of these people have written me LinkedIn recommendations, so you probably don’t need to bother with them. But I want you to really understand what it’s like to work in my organization.

Because everybody’s team is different. You’re optimizing for fit above all else. You could take a brilliant, capable leader from one environment, drop them into another, and watch them struggle. Not because they’re bad. Because their strengths, values, and operating style just don’t match.

I always joke that I’m a polarizing guy to work with. But I think everybody is polarizing. Nobody has a perfect Q rating. There’s always a tradeoff.

Sales Hiring Has a Charisma Problem

Here’s the uncomfortable part about how most of us hire salespeople: we’ve never actually checked whether our methods work.

We think coachability matters. Has anyone compared coachability scores from interviews to how reps actually perform six months in? We think case studies reveal selling ability. Has anyone correlated those scores with quota attainment?

At Owner, we started doing these retros. We pulled interview scorecards and compared them to actual performance data. The case study scores? Barely correlated with on-the-job success.

This tracks with what Daniel Kahneman wrote about in Thinking, Fast and Slow. He called it the “illusion of validity,” the feeling that you can predict someone’s future performance from a conversation when the data says you can’t. Frank Schmidt and John Hunter’s meta-analysis in the Psychological Bulletin found that unstructured interviews explain roughly 14% of the variance in job performance. That means 86% of the signal is noise you’re pattern-matching on.

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I’ll be honest: I spent years thinking I was performing at my best while my nervous system was actively sabotaging me.

I was in a near-constant flight response and had no idea. My heart rate was up. Cortisol was running. And my brain, rather than helping me think clearly through high-stakes meetings, was literally routing resources away from my prefrontal cortex, the part responsible for strategic thinking, decision-making, and emotional regulation. Yale neuroscientist Amy Arnsten’s research calls this the stress response “taking the prefrontal cortex offline.” Your brain is making you dumber at the exact moments you need it most. And most of us are letting it happen all day, every day.

This is why I was so excited to sit down with Kevin Bailey. Kevin is the co-founder and CEO of Dreamfuel, where he and his team coach founders, executives, and sales teams on mental performance using applied neuroscience. He’s a three-time Inc 500 founder who built his first company to $12M ARR and 100 employees by age 27, nearly burned out doing it, then went and got a master’s in applied neuroscience from King’s College London to understand why. He’s coached over a thousand leaders at companies like Greenlight Guru, LinkSquare, Smartsheet, and Owner.com. His team includes former coaches of UFC fighters, fighter pilots, Green Berets, and hedge fund managers. People who understand what it takes to perform when the stakes are real.

What Kevin laid out in our conversation is a framework I think every revenue leader needs to hear: a neuroscience-backed model for understanding why you perform inconsistently, and a practical set of protocols to fix it. We covered the performance chain, the four states of your nervous system, specific warm-up and cool-down techniques for meetings, and the concept that elite performance isn’t about grinding harder. It’s about training your physiology to stop working against you.

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The Performance Chain: Fix Your Physiology, Fix Your Results

Most leaders try to fix performance from the top down. They coach behaviors. They set goals. They run pipeline reviews. And when results don’t change, they coach harder.

Kevin flipped this on its head with what he calls the performance chain: physiology → emotions → feelings → cognition → behaviors → results. It’s a chain that runs in both directions, but the unlock is at the bottom. Fix your physiology, and everything above it shifts.

Here’s the study that sold me on this. Researchers found that if you injected someone with cortisol and adrenaline, even on the best day of their life, they would automatically enter a flight response. Their thoughts would turn negative. They’d start scanning for threats and looking for reasons to escape the situation. It didn’t matter how good things actually were. The physiology hijacked the cognition.

Kevin put it bluntly: “A human in the flight response, their thoughts are pinned down by their feelings, their emotions, and their physiology.”

This tracks with Amy Arnsten’s work at Yale, published in Nature Reviews Neuroscience, showing that even mild, uncontrollable stress rapidly impairs prefrontal cortex function while simultaneously strengthening amygdala responses. Your brain shifts from deliberate, rational thinking to reactive, habitual responding. You’re not making strategic decisions in that state. You’re surviving.

And here’s what most people miss. The vast majority of your thoughts are happening reflexively, automatically, in the nervous system. Aaron Beck, the founder of Cognitive Behavioral Therapy, built his entire framework around the concept of automatic negative thoughts (NATs), habitual cognitive patterns that are distorted, repetitive, and largely invisible until you’re trained to notice them. Researchers estimate thousands of these fire per day, and in untrained minds, the majority skew negative. Your brain isn’t neutral. It’s actively working against you unless you intervene at the physiological level.

The implication for leaders is uncomfortable but clarifying. You can’t think your way out of a bad physiological state. Telling yourself to “calm down” in a high-stakes meeting is like telling someone who just got a shot of adrenaline to relax. The body doesn’t listen to the mind in that direction. But the mind absolutely listens to the body. Change your breathing, change your heart rate, and the thoughts follow.

Kevin’s phrase for this stuck with me: “You got to win the battle with yourself before you win the battle outside.”

The Four States: A Map of Your Nervous System

Kevin introduced a framework I haven’t been able to stop thinking about since we recorded. It’s a simple quadrant with two axes: heart rate (high vs. low) and emotional tonality (negative vs. positive, driven by the neurochemicals running your system at any given moment).

Flight. High heart rate, negative emotional tonality. Cortisol and adrenaline. This is the “I need to get out of here” state. Your blood is routing to your limbs, away from your prefrontal cortex. Your cognitive capacity drops. You’re scanning for threats. Kevin’s estimate of how often executives live here? “A lot.” Mine is closer to 110% of the time. This is the default state of a stressed-out leader with back-to-back meetings and a number to hit.

Freeze. Low heart rate, negative emotional tonality. Cortisol and acetylcholine. This is the “I don’t want to get out of bed” state. Procrastination. Avoidance. Not wanting to sit down and do the hard thing you know will be painful. Kevin connected this to everything from avoiding a tough conversation to a polar bear walking through your door. The continuum matters. You don’t have to be catatonic to be in a freeze response. You just have to be dragging.

Fight. High heart rate, positive emotional tonality. Dopamine and noradrenaline. This one gets mischaracterized. Kevin was clear: a fight response can be highly adaptive. “The leader who says ‘we’re going to go take that hill,’ that’s a fight response.” It’s the inspired, energized, dopamine-fueled state where you want to go win. The catch is you can’t live there. It’s still sympathetic nervous system activation, and it burns energy.

Focus. Low heart rate, positive emotional tonality. Serotonin. This is the flow state. Deep work. Losing track of time on a spreadsheet or a piece of writing. The default mode network quiets down, self-referential thinking drops off, and you’re just locked in. Judson Brewer’s research at Brown, published in PNAS, showed that experienced meditators have measurably lower default mode network activity. Less mind-wandering, less self-critical chatter. That’s what focus feels like when you can access it consistently.

The goal Kevin’s team coaches toward isn’t permanently parking in one quadrant. It’s equanimity. Dead center of the map. A zeroed-out, neutral state from which you can intentionally move into fight (rallying a team) or focus (deep solo work) depending on what the moment requires. The problem is most executives don’t even know which quadrant they’re in. They walk into meetings in a flight response and wonder why they botched it.

Kevin’s point: “My physiology was off, which impacted my emotions, which impacted my thinking, which impacted my behaviors.” The chain again. Always the chain.

Warm Up, Hold Form, Cool Down: Treat Every Meeting Like a Game

Kevin reframed something that should be obvious but somehow isn’t: for executives, meetings are the performance arena. That’s where the rubber meets the road. Whether you’re closing a deal, kicking off a quarter, or having a difficult conversation with a direct report, the meeting is the game. And yet almost nobody warms up for it.

Kevin breaks meeting performance into three phases.

Warming up. Two tools, five minutes. First, mental reset breathing: 30 of the deepest breaths you can take (fast, forceful, belly relaxed like a gorilla), then on the 30th breath, exhale completely and hold for 15 seconds. You’ll feel your nervous system shift. Kevin calls it mental reset breathing and it works on the Y-axis of the quadrant, bringing your heart rate down and pulling you out of whatever flight or freeze state you walked in with.

Then, visualization. And I know how that sounds. But the academic term is rehearsal imagery, and the evidence is staggering. Alvaro Pascual-Leone’s 1995 study at Harvard found that people who mentally practiced a piano exercise for five days showed nearly identical cortical motor map expansion as those who physically practiced. Ranganathan et al. found that mental imagery of muscle contractions increased finger strength by 35% over 12 weeks. Your nervous system genuinely struggles to distinguish between a vividly imagined experience and a real one.

Kevin uses two flavors of visualization. Creative visualization: imagine the best possible outcome. He used to picture clients giving glowing case studies before hopping on sales calls. The confidence was real because, to his nervous system, the success was already a memory. Rehearsal visualization: imagine everything that could go wrong and see yourself responding well. This is the Michael Phelps approach. Phelps visualized his goggles breaking, his cap ripping, every disaster scenario, then mentally rehearsed staying composed through all of it.

I experienced this firsthand. Kevin’s team put me through a session that combined 20 minutes of somatic breathwork (way more intense than the 30-breath warmup) followed by a deeply personalized creative visualization. My whole body was vibrating. My hands tingled for hours. My wife came downstairs to check if I was okay. And the vision they installed stuck with me for weeks. It carried me through a grind period where we needed every ounce of resilience the team could find. I sent the team through the same session and got messages like “the Kyle number isn’t a question of if, but how soon.” That momentum was real and it lasted.

Steve Jobs did visualization sessions with Marc Benioff about the future of Salesforce. Navy SEALs do box breathing before operations. This isn’t woo-woo. A 2023 Stanford study (Balban, Huberman et al., published in Cell Reports Medicine) found that just five minutes of daily cyclic sighing outperformed mindfulness meditation, box breathing, and hyperventilation for reducing stress. The protocols work. The science is clear. The only barrier is the voice in your head calling it silly.

Holding form. You’re in the meeting. Someone says something that would normally throw you off. Two tools here. First, relax your stomach. Sounds too simple. Kevin explained that a tight stomach is a protective posture, and relaxing it activates the vagus nerve and shifts you toward parasympathetic dominance. Just loosening your gut can interrupt a cortisol spike.

Second, cognitive reframes. Kevin’s favorite: “Best thing that could have happened.” Say it to yourself immediately after the disruption. Your brain, wired to complete patterns (we are basically biological LLMs in this regard), will automatically start generating reasons why this is actually good news. A deal gets punted by private equity? Best thing that could have happened. Now you’re thinking “great, the PE firm knows about us, when this works we get the whole portfolio” instead of spiraling into damage control. My version of this is amor fati, the Stoic phrase meaning “love your fate.” I keep a Ryan Holiday coin on my desk. Same mechanism, different packaging.

And if the reframe doesn’t cut it, the physiological sigh: a deep inhale, a second quick inhale stacked on top, then a long exhale. That same Stanford study found it’s the most effective real-time stress reduction technique they tested. I use this one most often with my kids, honestly. They’re losing their minds, I do the double-inhale exhale, and suddenly I can be the parent I want to be instead of the one who matches their energy.

Cooling down. This one surprised me. Kevin recommends anger journaling. After a tough meeting, before you walk into the next one, take two minutes and vent into a journal. Unfiltered. Profanity encouraged. “What the hell was that?” energy. Then read it back. Some of it will be cognitive distortion you can laugh at. Some of it will be legitimate and worth acknowledging. Either way, it’s out of your system instead of suppressed.

I’m a suppressor. If something bad happens, my instinct is amor fati, move on, close that browser tab in my mind. Kevin pushed back on this: “You can block up the nervous system over time. If you want real mental freedom, you got to learn how to express the negative stuff too. Just in a healthy way.” Gratitude journaling gets all the press. Taking out the head trash deserves equal billing.

ACT: Awareness, Choose, Transition

If someone asked me “where do I start with all of this?” I’d point them to Kevin’s ACT framework.

Awareness. You have to be able to notice what state you’re in before you can change it. This is the whole game and it is genuinely hard. Your mind doesn’t want you to have this skill. It wants to stay in control. If you’re in a freeze response, your thoughts will feed you reasons to stay frozen. They won’t helpfully announce “hey, you’re in a freeze response right now.”

The body, though, always tells the truth. Your heart rate, your stomach, the tension in your shoulders. Kevin’s argument for cold plunging, breathwork, and meditation all came back to this one thing: they build somatic awareness. They get you out of your head and into your body, where the real data lives.

I want to spend some time on cold plunging because it has genuinely changed the way I operate and I think it’s the most underrated awareness tool available to executives right now.

I use a Plunge Air at my house. Most mornings. It’s become as non-negotiable as brushing my teeth. And the thing that makes it so valuable isn’t just the cold exposure itself. It’s the feedback loop.

The Plunge app tracks your heart rate during each session. And when you can actually watch your heart rate data in real time and review it after, something clicks. Your first bunch of plunges, your heart rate spikes to 100+ the second you get in. Your body panics. You do the fast, shallow breathing that feels instinctive but is actually your flight response firing on all cylinders. You white-knuckle it for 45 seconds and get out.

But here’s what happens over weeks of practice. You learn to fight that panic response. You breathe in through your nose. You slow the exhale. You force yourself into a calm, controlled breathing pattern while your body is screaming at you to get out. And you watch your heart rate respond. I can now sit in the plunge and bring my heart rate down into the low 60s. During cold exposure. That’s not a slow heart rate compared to sitting on your couch, but when your body is submerged in cold water, getting your heart rate that low is a serious physiological achievement.

The science backs this up. A 2000 study published in the European Journal of Applied Physiology (Srámek et al.) found that cold water immersion at 57°F increased dopamine levels by 250% and norepinephrine by 530%. That dopamine boost is why you feel like you can run through a wall afterward. It converts to noradrenaline, which is the energy molecule Kevin described in the fight response. So cold plunging doesn’t just build awareness. It resets your neurochemistry into a state that’s actually useful for performing.

But the awareness piece is what I keep coming back to. When you can watch your heart rate drop on a screen while you’re actively uncomfortable, you’re training a skill that transfers everywhere. You start to feel your heart rate in a board meeting the same way you feel it in the plunge. You recognize the tight stomach, the shallow breathing, the cortisol spike. And because you’ve practiced hundreds of times in the cold water, you know what to do about it. Slow the exhale. Relax the gut. Ride it out.

Kevin framed it as physiological resilience correlating with psychological resilience. I’d go further. The plunge is a daily laboratory for state management. Three minutes every morning where you practice exactly the skill set that makes you better in every high-stakes interaction for the rest of the day. If I had to pick one protocol to recommend to a fellow executive, it would be this one, paired with the data feedback. The app matters. Seeing the number drop is what teaches your brain that you have agency over your own physiology. Without the feedback loop, you’re just suffering in cold water. With it, you’re training.

Kevin also mentioned that cold plunging hits three bases simultaneously: resilience (you’re bouncing back from voluntary adversity every morning), energy (the dopamine and noradrenaline release kicks your day off in a fight response instead of a flight response), and somatic awareness (you’re forced out of your executive head and into your body). One practice, three returns. Hard to find a better ROI on five minutes.

Meditation is the other foundational practice for building awareness. Kevin said the biggest investment he’s made in his life is his meditation practice, and that if he told people the total hours they’d roll their eyes. The book that flipped the switch for me was Search Inside Yourself by Chade-Meng Tan, the engineer who built Google’s mindfulness program. He explained that every time your mind wanders during meditation and you pull it back, that’s one bicep curl. You’re not failing when your mind wanders. You’re doing a rep. Over time, you catch the wandering faster, pull back more easily, and eventually the default mode network starts to quiet on its own. Brewer’s research at Brown confirmed this neurologically: experienced meditators show measurably lower DMN activity, less self-referential chatter, even at rest.

Kevin distinguished between two types of meditation worth knowing about. Focal meditation is where you start: focus on your breath, a mantra, a word. Transcendental meditation falls into this category, and it’s what Kevin credits for his own foundation. The second type, open monitoring meditation, is the advanced practice. You focus on nothing. You let everything arise and pass without attaching to any of it. That’s where real awareness lives, and it connects to the non-duality research Kevin is doing for his master’s dissertation at King’s College London.

For someone starting from zero, my practical recommendation: get a cold plunge with biometric tracking and use it most mornings. Download Insight Timer or Calm and start with 10 minutes of breath-focused meditation. Do both for 30 days. You will feel different. You’ll start noticing your state in real time during meetings, conversations, even arguments with your kids. That noticing is everything. Because you can’t choose a different state if you don’t know which one you’re in.

Choose. Once you’re aware of your state, choose a different one. I’m in a freeze response. I need to be in a fight response for this meeting. That conscious decision is the bridge.

Transition. Use a specific technique to move. Kevin’s team teaches 52 of them (they’ve written two books on it and are launching a YouTube channel). But even the handful we covered in this conversation are enough to get started. Breathwork to move on the Y-axis. Visualization, music, or comedy (I used to listen to standup on my walk into work) to move on the X-axis. The physiological sigh for emergencies. And cold plunging as the daily full-system reset.

Pick one technique and practice it for a month. That’s the starting point.

Drop the Dopamine Wasters

Kevin saved maybe the most practical advice for the end of our conversation. If you want to upgrade your entire operating system, stop wasting dopamine on things that don’t matter.

Anna Lembke, Chief of Stanford’s Addiction Medicine Clinic, wrote about this extensively in Dopamine Nation. The brain runs on a pleasure-pain balance. Every time you get a hit of easy dopamine (scrolling Instagram, sports gambling, a third glass of wine), your brain downregulates its dopamine receptors to compensate. Over time, you need more stimulation to feel the same motivation. The hedonic treadmill speeds up. Your baseline drops. And the things that actually matter, the hard work, the deep focus sessions, the difficult conversations, feel even harder because your reward system is fried from cheap hits.

Kevin’s prescription: drop one dopamine waster. His family goes screen-free for all of Lent. He said the reset for their entire family’s nervous system is dramatic. “Most people suck at recovery,” he told me. “They stay home and look at social media all day. You didn’t recover at all.”

This connected to something I’ve been telling people for years. Delete the negative stuff from your Instagram. Unfollow the sarcastic accounts. Your brain is an LLM (I say this half-jokingly, but the parallels with neural networks are real). If you flood it with garbage content, it’ll produce garbage outputs. Negativity bias already has you seeking out the worst stuff on the internet because your ancient brain craves threat detection. You have to actively curate what goes in.

My starter list for anyone who wants to begin this journey: a daily gratitude practice (doesn’t need to be written, just three things when you wake up while your brain is still in a programmable state), a meditation practice (start with breath-focused, 10 minutes), regular physical discomfort (cold plunge, a hard run, a misogi with a 50% chance of failure), and now I’m adding Kevin’s contribution: identify your biggest dopamine waster and cut it for 30 days. See what happens to your motivation.

The Choice

Here’s the binary that Kevin’s conversation forced me to confront: you’re either training your nervous system or you’re letting it run you. There’s no neutral.

Every executive reading this has experienced the flight response in a meeting they needed to crush. The racing heart. The scattered thinking. The moment where you said something reactive instead of strategic. Most of us chalk that up to “a bad day” or “high pressure.” Kevin’s framework makes it clear that it’s a physiology problem with a physiology solution. And the solution is trainable.

The leaders who will separate themselves over the next decade aren’t the ones who grind the hardest or log the most hours. They’re the ones who develop the tightest bound of performance. Rain or shine, good quarter or bad, they show up the same. Kevin’s word for it is consistency. Michael Jordan playing lights-out with the flu. That’s the standard.

We didn’t even get to how you scale this across an organization (that’s part two with Kevin, coming soon). But Kevin made the starting point clear: it begins with you. A leader can’t prescribe mental fitness to a team while ignoring their own nervous system. You have to model it. You have to live it. And honestly, this is something I weigh heavily in hiring and promotion decisions. If someone can’t regulate their own state, I have a hard time trusting them with the states of the people who report to them.

Start with ACT. Build awareness through meditation and cold exposure. Choose your state before your next important meeting instead of walking in on autopilot. Use one transition technique, even if it’s just 30 deep breaths and a 15-second hold. See what happens.

Kevin’s team at Dreamfuel is launching a YouTube channel with more of these protocols. His co-founder Anna Rohr (now Anna Heine), a Notre Dame neuroscience grad and eight-time All-American athlete, built the curriculum alongside him. If you want to go deeper, that’s where I’d point you. And if you’re curious about non-duality (the far end of the mental performance continuum, which Kevin is researching for his master’s dissertation), pick up The Greatest Secret by Rhonda Byrne or look up David Bingham’s work on YouTube.

If this episode gave you something useful, do me a favor and leave a rating on the podcast. It helps more than you’d think. And send this to one leader in your life who needs to hear that their brain is working against them. They probably already feel it. They just don’t have the framework yet.

And the clock is already running.

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AI can now research your prospect, write your email, take your call notes, fill your CRM, and draft your follow-up. It can do basically everything a salesperson does in a day. And somehow, that makes human salespeople more valuable, not less.

Eric Janssen put that question to me, Daniel Pink, and Asad Zaman in separate interviews for the same episode of Sales Reframed. The fact that we all landed in the same place is worth paying attention to.

For context: Eric is an entrepreneur turned sales professor at Ivey Business School. He’s built Sales Reframed around this idea that sales is a life skill, not just a job function. For the final episode of the season, he wanted to tackle the future of sales head-on, so he talked to three people coming at it from very different angles. Daniel Pink wrote the literal book on modern selling (To Sell Is Human). Asad Zaman is the CEO of Sales Talent Agency and has spent 15+ years matching sales talent with companies. And I’m in the weeds every day as CRO at Owner.com, building an AI-native sales org approaching $100M ARR.

We recorded separately. None of us heard each other’s answers. And yet the throughline was almost identical: AI is changing the homework of sales, not the hard work. The research, the data entry, the note-taking, the follow-up drafting. All of that is getting automated. But the judgment calls, the relationship building, the ability to read a room and adapt in real time? That’s getting more valuable by the day.

Here’s what stuck with me from the full conversation.

“Where Do I Start?” Is the Wrong Question

Every conference I speak at, someone asks me: “Where should I start with AI?” They want me to say “AI SDR” or “AI forecasting” or “conversational intelligence.” A specific tool they can buy, implement, and check a box.

But that framing assumes AI adoption is linear. Step one, step two, step three, done. In reality, the transformation is organic and recursive. There are feedback loops. What you learn from structuring your call data changes how you think about prospecting, which changes how you train reps, which changes what data you collect. It compounds.

So I’ve started reframing the question entirely. Stop asking “where do I start?” and start asking “how do I transform my company to be AI native?” One implies a project with an end date. The other implies a permanent shift in how you operate.

The answer starts with data foundations. Nothing else works until you have good first-party and third-party data. AI is a data model. Feed it garbage and you get garbage back, just faster. At Owner.com, we use Momentum to take unstructured call transcripts and pull out structured insights: what competitors did the prospect mention? What’s their current tech stack? What POS platform are they on? That information flows straight into Salesforce fields. Now every subsequent tool in the stack has something real to work with.

McKinsey’s 2025 State of AI report found that 78% of organizations now use AI in at least one business function, up from 55% just a year prior. But adoption and value creation are different things. Gartner’s research predicts that by 2028, AI agents will outnumber sellers by 10x, yet fewer than 40% of sellers will report that those agents actually improved their productivity. The gap between “using AI” and “getting value from AI” is almost entirely a data and transformation problem.

The other piece nobody wants to hear: this falls on the leader. You can’t outsource your AI strategy to McKinsey and expect it to hold up for more than six months. The technology moves too fast. Your leadership team needs to build real fluency, not just approve a vendor contract. Consultants can fill capability gaps, but only inside a vision you actually own and understand.

The Homework and the Hard Work

Eric Janssen used a framing during the episode that I keep coming back to. He broke the sales process into two categories: homework and hard work.

Homework is the research, the data scraping, the note-taking, the CRM updates, the follow-up email drafting. All the stuff that used to eat 60% of a rep’s day and had nothing to do with actually selling. AI is eating that whole category alive, and it should.

Hard work is judgment. Reading a room. Building trust over a 9-month enterprise deal cycle. Knowing when to push and when to back off. Deciding that the deal on paper looks great but something feels off about the champion’s commitment level. That category is getting more valuable, not less.

Here’s where it gets interesting. Daniel Pink made a point during his segment about information parity that connects directly. For basically all of human civilization until about 15 years ago, sellers had more information than buyers. That asymmetry is what made the sleazy salesperson possible. Now buyers have as much information as sellers, tons of choices, and every platform in the world to talk back on. Pink calls it the shift from “buyer beware” to “seller beware.”

In that world, the homework matters less because buyers are doing their own homework anyway. A 2025 G2 survey found that half of B2B software buyers now start their buying journey in an AI chatbot instead of a Google search. That number jumped 71% in just four months. Your buyers are walking into calls pre-researched. They’ve already asked Claude or ChatGPT about your product, your competitors, and your pricing.

So the hard work, the human work, becomes the entire ballgame. Can you add perspective they didn’t find in a chatbot? Can you connect their problem to a solution they hadn’t considered? Can you build enough trust that they choose you over the five other vendors whose features look identical on paper?

The Expertise Filter

Asad Zaman brought up a study during his segment that I think deserves way more attention than it’s getting. Researchers gave generative AI tools to material science researchers at a major lab. For the top third of researchers, AI doubled their productivity. For the bottom third? It did basically nothing.

The difference was expertise. The best researchers had the taste, judgment, and pattern recognition to know which AI-generated suggestions were good and which were garbage. They used AI as an accelerant for instincts they’d already developed. The weaker researchers couldn’t filter signal from noise because they didn’t have the signal in the first place.

This maps directly to what I see in sales orgs every day. Give your best AE an AI research tool and they’ll pull insights that completely reshape their discovery calls. Give that same tool to a rep who doesn’t understand the buyer’s world, and they’ll just copy-paste whatever the model spits out. Same tool, wildly different outcomes.

Gartner’s data backs this up: sellers who effectively partner with AI are 3.7x more likely to hit quota than those who don’t. But Gartner also predicts that by 2028, fewer than 40% of sellers will report AI agents actually improved their productivity. The gap is expertise. AI is a multiplier, and multiplying zero still gives you zero.

This is why I push so hard on universities leaning into AI rather than restricting it. Your employer doesn’t care if ChatGPT or Claude cowrote that email. They want great work product. If schools are teaching students to avoid AI, they’re training them for a world that doesn’t exist anymore. Day one that somebody starts with me, I’m not criticizing their use of AI. If anything, I’m wondering why they’re not using it more.

The SDR Isn’t Dead. The Lazy SDR Is.

One of the most discussed questions in revenue leadership right now is whether AI kills the SDR/BDR role. Asad had a surprisingly grounded take on this. He actually went and talked to the founders building AI SDR products. Their answer was more honest than you’d expect from people selling the replacement.

Here’s the thing nobody talks about: the FCC ruled in February 2024 that AI-generated voice calls fall under the TCPA. That means AI can’t cold call someone who hasn’t given prior express consent. And even when there is consent, the AI has to disclose upfront that it’s artificial. As Asad put it, “Hi, I’m cold calling you from X company, and I am AI.” What happens? Everyone hangs up.

So the AI SDR becomes a copilot, not a replacement. The emailing component gets faster, higher volume, better targeted. But someone still has to pick up the phone. Someone still has to multithread into an account. Someone still has to read the room when a prospect gives a half-interested “maybe” and figure out what’s actually behind it.

Asad’s team is proving this in real time. While most outreach has devolved into short, vague, mass-blasted garbage, Sales Talent Agency went the opposite direction: long-form, deeply personalized messages where every word shows they did the work. The result? 60-70% response rates on LinkedIn InMails, in a world where most recruiters and sellers celebrate 15%. Some responses were people saying “this is the best message I’ve ever received from a recruiter.”

That’s the playbook. When everyone zigs toward automation and volume, you zag toward depth and personalization. AI handles the research that makes depth possible. The human delivers it.

Upheaval, Not Apocalypse

Daniel Pink made a point during his interview that I keep thinking about. He’s old enough to remember when people said Google would destroy every sales function because buyers could find anything themselves. Before that, spreadsheets were supposed to replace accountants. He brought up James Bessen’s famous research on ATMs and bank tellers: between 1988 and 2004, ATMs cut the number of tellers per branch from 20 to 13. But that efficiency made branches cheaper to operate, so banks opened 43% more of them. Total teller employment actually grew.

The job didn’t disappear. It transformed. Tellers went from counting cash to relationship banking, selling high-margin financial products. The routine work got automated. The human work got more valuable.

Pink sees the same pattern with AI and sales. His framework from To Sell Is Human holds up remarkably well 13 years later. The old ABCs of selling were “Always Be Closing.” Pink’s version is Attunement (can you see the world from your buyer’s perspective?), Buoyancy (can you stay afloat in an ocean of rejection?), and Clarity (can you surface problems your buyer hasn’t identified yet?). AI can help with all three. It can prep you to be more attuned, give you data to maintain buoyancy when the pipeline looks thin, and surface patterns that sharpen your clarity. But it can’t replace any of them.

Pink’s word for what’s happening is “upheaval,” and I think that’s exactly right. Not apocalypse. Upheaval. The job changes shape. Some roles shrink. New ones emerge. But the core of what makes a great salesperson, the taste and judgment and ability to connect with another human being, that’s not going anywhere.

There’s a useful caveat to the ATM story, though. After 2010, mobile banking finally did what ATMs never could. Teller employment dropped 50% in twelve years because mobile didn’t just automate tasks within the existing system, it created an entirely new paradigm that made branches less necessary. The lesson: partial automation transforms jobs, but paradigm shifts can eliminate them. We’re in the partial automation phase of AI in sales right now. Whether it stays there is something none of us can predict.

What This Means for You

The three of us came at this question from completely different angles. I’m running a sales org in the middle of this transformation every day. Asad is watching the talent market shift in real time. Daniel has spent a decade studying the underlying psychology of why humans buy from humans.

We all landed in the same place: the future belongs to practitioners who use AI to do better human work, not to replace it.

If you’re a revenue leader, the move is straightforward. Fix your data foundations. Build AI fluency on your leadership team instead of outsourcing it. Push your reps to use AI constantly, not as a crutch but as a research engine that raises the bar on every interaction. And invest in the hard work skills that no model can replicate: reading a room, building trust over time, and having the judgment to know when the data says one thing but your gut says another.

If you’re early in your career, the message is even simpler. Become the person whose expertise makes AI useful. Build the filter. The reps who treat AI like a magic email writer will wash out. The ones who use it to walk into every call sharper, more prepared, and more curious than the person on the other end of the line expected? Those are the people I want to hire.

The episode is worth a listen. Eric Janssen did a great job weaving together three very different perspectives into something cohesive. Check out Sales Reframed wherever you get your podcasts.

There’s a strain of sales leadership that treats prospecting like a rite of passage. You did it, so they should too. Eat what you kill. Pound the phones. It feels like discipline, but it’s actually nostalgia cosplaying as strategy. Aviv Canaani flipped Datarails from 90% outbound to 90% inbound and watched his AEs start closing in a quarter what competitors close in a year. Turns out, when you stop making closers do sourcers’ jobs, closers close more.

Aviv’s path to the CRO seat is unusual. He started as VP of Marketing at Datarails when it was a few million in ARR and burning through cold calls with 20-something SDRs. Four and a half years later, the company has raised a $70 million Series C, grew 70% last year, and Aviv projected new ARR within a 5% margin of error three out of four quarters. He’s never carried a quota.

That last part matters. Because the way Aviv thinks about revenue — as a system to be architected, not a number to be hunted — is what makes the rest of this conversation worth paying attention to.

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“You’re Not a Real AE If You Don’t Prospect”

This is one of those beliefs that survives in sales because it sounds tough, and tough sounds respectable. Aviv’s LinkedIn post arguing that AEs should never prospect went viral, and the comments section played out exactly how you’d expect. “You’re not a real AE.” “I want to count only on myself.” The sales ego activated.

But here’s what Aviv actually found when he started interviewing AEs from other companies: most of them admitted that 80-90% of their closed revenue came from inbound. They were required to prospect, sure. But the prospecting wasn’t producing. It was theater.

Salesforce’s State of Sales report backs this up. Reps spend only 28% of their week actually selling. The rest goes to admin, internal meetings, research, and yes, prospecting that often goes nowhere. When you’re paying someone $250-300K in OTE to close deals, having them spend a third of their time doing a job a BDR could do at a fraction of the cost is a failure of resource allocation dressed up as culture.

Aviv put it plainly: “I don’t care if in the past you earned your right by prospecting. If it’s not the best use of your time, I don’t want you to do it.”

And there’s a harder truth underneath that. AEs are never going to be as good at prospecting as BDRs. They’re not getting the repetitions. They don’t actually want to do it. The number one goal of a BDR is to stop being a BDR. That motivation produces a different kind of effort than an AE grudgingly working an outbound list between demos.

The Inbound Flip: From 90% Outbound to 90% Inbound

Aviv didn’t inherit an inbound machine. He built one from scratch, starting with three agencies running paid campaigns across LinkedIn, Google, and, counterintuitively, Facebook. He’s selling FP&A software to CFOs. Facebook and Instagram seem like the wrong channels. But the product-market fit was there, and the leads started converting.

The 2022 tech meltdown accelerated the shift. Outbound leads stopped converting almost entirely. Inbound held up because of intent. That’s the difference people miss when they talk about inbound vs. outbound in the abstract. It’s not just about cost, though HubSpot’s data shows inbound leads cost 61% less. It’s about signal quality. Someone who found you, researched you, and raised their hand is a fundamentally different buyer than someone you interrupted during lunch.

6sense research makes this stark: 83% of the time, it’s the buyer who initiates first contact. Gartner reports that buyers who self-navigate their purchase process complete a “high-quality deal” 65% of the time, compared to just 24% in sales-rep-led purchases. The data keeps pointing the same direction. Buyers who come to you close better than buyers you drag to the table.

Aviv built the organic engine in parallel. CFO memes on LinkedIn (which the board initially questioned), a niche FP&A podcast that now has over 600,000 downloads, and a brand presence that even makes outbound easier. He told a story about listening to a cold call where the prospect said: “Oh yeah, I’ve been seeing your ads. I love your content.” That’s brand working as air cover for every other channel.

Quota Fiction vs. Productivity Truth

Here’s where Aviv really diverges from conventional CRO thinking. Most leaders set quotas by starting with the board’s revenue target and dividing backwards: target divided by reps, plus a stretch factor, and hope the pipeline materializes. Aviv does it the other way.

“I don’t really care that much about the quota,” he said. “I care about how much I think they actually can produce. Knowing the real productivity is what matters.”

This sounds like semantics until you look at the results. RepVue’s Cloud Sales Index pegged average quota attainment at just 43% in Q4 2024 across 238 companies. The Bridge Group’s SaaS AE Metrics Report found roughly 58% of reps hitting quota. Either way, the majority of SaaS reps are missing their number. That’s not a performance problem. That’s a target-setting problem.

Locke and Latham’s goal-setting research (the foundational academic work on this) found a linear relationship between goal difficulty and performance, but only up to a point. Once goals exceed someone’s actual ability, performance drops. People disengage. The quota becomes fiction that everyone acknowledges privately but nobody challenges publicly.

Aviv’s model works differently. He knows his cost per meeting, his conversion rate at every stage, his sales cycle length (30-45 days), and how many meetings an AE can handle before quality drops. He only hires new AEs when he knows he has the pipeline to fill their calendars. Compare that to the more common approach: hire AEs first, then tell them to prospect because their calendars are empty. One is a system. The other is a prayer.

Your Inbound Machine Is a Recruiting Weapon

This was the insight that hit hardest in our conversation. Aviv’s AEs close in a quarter what competitors’ AEs close in a year. Read that again. That’s not a marginal advantage. That’s a completely different job.

When reps at other companies hear that, they want in. Aviv described it as a self-reinforcing cycle: build the inbound engine, which creates better unit economics, which allows you to hire better reps, which improves close rates, which makes the economics even better.

RepVue’s platform explicitly ranks companies on inbound lead flow as a core dimension, and they’ve found a strong correlation between lead flow sentiment and quota attainment. Reps research this before accepting offers. They know which companies feed their AEs and which ones hand them a phone list and wish them luck.

We see the same thing at Owner. Our per-rep productivity is three to four times most competitors because our AEs aren’t spending their days sourcing. Our OTE attainment runs around 138%. About 80% of reps hit target, and many blow past it. The OTEs aren’t inflated. They’re fair for the segment. But when you have that kind of efficiency, you can pay top of market, invest in RevOps, data teams, enablement, and BDRs — and the whole machine keeps compounding.

The alternative is the traditional model: high OTEs that only half the team actually earns, reps leaving after a year because the number was never realistic, and a perpetual recruiting treadmill. That’s expensive in ways that don’t show up on any dashboard.

Revenue Architecture Over Sales Craft

Aviv said something toward the end of our conversation that crystallized his whole philosophy: “Sometimes I love what I’m doing at work. And I feel it’s like when I was a kid playing video games. You need to understand your goal, what you’re trying to manipulate, what kind of numbers you want to beat. You have different levers you can pull.”

That’s the marketer’s brain applied to the CRO role. Not “how do I coach this rep through a tough deal?” but “how does the entire system produce revenue?” Both skills matter. But in an AI-accelerated world, the systems thinker has a widening advantage.

The value of great sales craft — reading a room, handling objections, building rapport — is relatively constant. It was valuable five years ago and it’s valuable now. But the value of being systems-oriented and architecturally minded has compounded massively. AI gives you more leverage on process, automation, data visibility, and pipeline orchestration than ever before. If you’re still leading revenue with gut instinct and a Rolodex, the gap between you and leaders like Aviv is growing every quarter.

Aviv doesn’t exempt brand from the system, by the way. His VP of Brand is the one person in the org without a number. No MQL targets, no pipeline attribution requirements. “I want brand to do crazy fun stuff,” he said. “I don’t want them to think about MQLs or opportunities. Because then you can’t really be creative.” That’s a deliberate architectural choice — protect the creative function from the metrics machine so it can do the long-term work that makes everything else easier.

Where to Start If Your Motion Isn’t Predictable

Aviv’s advice for leaders who want to get here is practical. Expect your directors and VPs to build predictable models. Every function should know their conversion rates, their capacity constraints, and their cost per output. Growth should own the top of funnel math. RevOps should own the instrumentation. Sales should know exactly how many meetings they’re getting this quarter and what they need to convert.

And stop defaulting to “AEs need to prospect because they always have.” As Aviv said: “I’m sure salespeople in the past had to meet people in person and drive around the country. That doesn’t happen anymore.”

The playbook is clear: build the system first, staff it second, and let the math do the recruiting for you.

If this episode hit for you, the best way to support the show is to leave a rating on Spotify or Apple Podcasts. It takes 30 seconds and it genuinely makes a difference in getting this content in front of more revenue leaders.

Every conversation I have with revenue leaders right now circles back to the same question: how do we implement AI in a way that moves the needle fast? In these conversations, I’ve noticed two distinct approaches emerge.

The first camp takes a centralized approach. A small group of people owns the AI transformation for the entire customer journey, from prospecting through post-sales. They build, optimize, and deploy AI capabilities from the center out. The reps don’t manage agents or run their own tools. They just receive outputs that have been refined and tested by specialists in the workflows that they’re already used to.

The second camp is bottom-up. Leaders encourage their teams to explore their own tools, maybe give them a budget to put on their corporate card, or deploy something they like and let people try things out. People build their own custom GPTs and Gems. They experiment and some of it sticks.

This bottom-up approach has its merits but at Owner, we’ve chosen a centralized approach. Here’s why I think it’s the right call for most revenue organizations.

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How we got here

Our centralized model was more emergent than pre-planned. When myself, our head of RevOps, and our head of BizOps started pushing AI into everything we could, nobody else outside of EPD was thinking about it yet. We were living in a different information ecosystem, largely on X and YouTube, where we were just consuming everything about what was possible. It felt like it would be incredibly slow to try to pull everybody along when we could just build the capabilities ourselves and push them into people’s workflows. Moving fast ourselves just seemed like the obvious thing to do in order to get a head start on our competition.

With the benefit of hindsight, I can see that we were very fortunate to take this approach.

What the decentralized model does well

The decentralized approach has real strengths worth acknowledging.

You get way more people coming up with interesting ideas. You’re putting tools in the hands of the people who actually have the problems you’re trying to solve. They’re close to those problems. They have intuition about them, and interesting things will certainly emerge from that.

You also better distribute AI literacy across the organization. When people are building their own things from the bottom up, more of your team develops their AI muscle. There is probably a long-term benefit that we’re missing out on here, but I’m OK asking our sales people to be great at sales and asking our tech people to be great at tech.

Where the decentralized model falls short

That said, what I’ve found with the decentralized approach is that while the outputs are interesting, it’s almost never production-grade. Someone creates a custom GPT or a Gem and they’re cool but very low power compared to what you can build with more sophisticated approaches.

Think about the sophistication ladder: there’s basic chat, then there’s custom GPTs and Gems, then you get into n8n or Make, then Claude Code, then actually building your own AI applications with proper prompt & context engineering, evals, etc. The decentralized model rarely gets past the second rung.

At Owner, our applied AI team is testing multiple prompt variants with the same context, looking for the same output, with evals built for that. They’re connecting Snowflake databases, pulling in Notion folders, doing context engineering at a level that’s just not possible with a custom GPT. And no IC is building evals for their custom GPT and running hundreds of simulations.

That means our AI team can create tens of thousands of emails at a higher quality threshold than any individual rep could have produced, and at a volume that’s probably ten to fifty times what they would have done on their own. Every single person who fits our exact criteria gets enriched in a very specific way, with context pulled in from multiple sources, and they’re getting multiple emails. That’s uniquely unlocked by a centralized model.

The other big win with a centralized approach is that nothing happens in a silo. One of the biggest headaches in sales is losing context. A customer tells the sales rep something important on a call four months ago. By the time the CSM is prepping for a QBR, that’s buried in a transcript nobody remembers exists. Or it’s sitting in a custom Gem and never makes its way into a centralized database. Or the original CSM left and the new person is starting from scratch. A centralized approach solves this by design. When we build something for sales, we’re already thinking about how that same context flows to onboarding, to the CSM, to how we track product usage.

What this means for jobs

Jordan Crawford, CEO at Blueprint GTM, sparked this idea for me recently: a job is really just a bundle of tasks. You can look at any job description and pull those tasks apart, then ask what’s best done by people and what’s best done by machines.

With a centralized model, we’re not asking reps to “manage agents” or run their own AI workflows. Instead, we just do certain parts of the job centrally, and those tasks are no longer in the job description at all. The rep receives outputs as inputs into their actual work. Lead research, account enrichment and pre-call research are good examples of tasks that used to be in the sales JD that shouldn’t be moving forward because they can be done so much better with centralized tools.

Researching accounts is in most BDR job descriptions, but it’s not part of the job at Owner. Finding contacts, getting phone numbers, and building lists with a hypothesis of need is all done centrally now. The rep gets a list with everything already there. They’re not bouncing between Salesforce and Slack and their inbox and whatever other sales tools. They can spend their time on the stuff humans are actually best at: getting on the phone, building relationships, figuring out creative ways to move open pipeline.

Same thing with keeping your CRM up to date. That’s a task we’ve largely taken out of the reps requirements with the help of Momentum, which captures and updates everything for them. It should just be done by AI, with the rep reviewing the output.

Some people get nervous when they hear about jobs being “unbundled.” But the tasks AI is best at are mostly things people don’t actually want to do anyway. Nobody got into sales because they love grinding through lead research databases. Nobody became a CSM because they were passionate about sending check-in emails and updating Salesforce fields. People want to be on the phone with customers. They want to solve problems. And when reps aren’t drowning in admin work, they can manage way bigger books.

Why this matters even more for post-sales

Think about what percentage of a CSM’s job is actually generating value. In my experience, maybe 40%. The rest is manual work: checking dashboards, sending emails, logging activities, chasing meetings.

Post-sales teams have a big advantage here: they’ve got the first-party data. Product usage, support tickets, billing history and all the context that sales teams have to scrape and enrich from the outside, post-sales already has and then some. When you combine customer data with product data in a centralized system, you can do things that aren’t possible when everyone’s running their own tools.

I was chatting with Tom Richards, who runs Trig, about this recently. He made a point that I keep thinking about: most CSMs can only really be effective across thirty, maybe fifty accounts. After that, it’s incredibly difficult for any one rep to be proactive with the accounts they manage. Onboarding slows down, they miss the signals that someone’s about to churn, expansion conversations happen too late or not at all. The default answer has always been to throw more people at the problem, but that math doesn’t really work anymore. His take is that “if you have an AI system like Trig doing the account watching, nudging, and messaging centrally, you can scale the benefits of post-sales in a way that wasn’t previously possible”.

The CSM doesn’t have to spend half their week on check-in emails and CRM updates, because the AI can now handle that side of things automatically. Not only that, but it can do it at a scale and precision that can reach every customer, rather than just the ones you have bandwidth for. The direct impact of that can translate into more customers retained and grown. But indirectly, it also means your revenue org can become significantly more efficient and effective, as reps become fully focused on revenue generating activities (like actually talking to customers who need help). Or in Tom’s words: “Ultimately a centralized AI-first approach - that’s deployed correctly - in post-sales, can translate into a rep covering hundreds if not thousands of accounts.” We’ve started this process at Owner with the Trig team but pushing for the same centralization as sales in CS is a big opportunity.

How to actually do this

If you’re bought into the centralized model, the next question is how to actually build it. Most companies run into two problems.

The first is talent. Most companies don’t have somebody to do this work. These applied AI people are valuable and hard to attract unless you’ve got a top-tier employer brand and a lot of cash. If you have one of these brands and an exciting growth story then certainly hunt for A++ talent. I find most of these folks to be highly entrepreneurial, so make sure that they have the freedom to experiment and explore. More importantly, make sure they know that, and that you won’t bury them in bureaucracy and boring projects. Applied AI talent is still an emerging category so I don’t have any silver bullet advice on finding it. Look for Rev Ops/Growth/Data people that are building cool things on X and LinkedIn, pull someone out of an AI-native Growth consultant, or find GTM AI founders that are struggling with PMF.

If you don’t have one of these brands, someone on your team has to become that person. Find the most technical person in RevOps or Growth who’s already building interesting AI workflows, and let them loose. Tell them that this is their thing now and they can go buck wild on building the future of GTM at your company. Give them the runway to go deep and spend a couple hundred hours learning on X and YouTube, building stuff in Claude Code, making a bunch of things that don’t work until they start to figure it out. Anyone smart and eager from a systems background can learn this. They just need permission and time.

Let. Them. Cook.

Or you can buy the capability. We’ve been working with Trig on the post-sales side, and what’s interesting is they’ve productized this kind of centralized AI architecture: persistent context across the customer journey, pattern detection, automated interventions. Most companies underestimate how long it takes to build this internally. It can pull engineering away from your core product for years, and you still might not get it right. There are tools out there that can help bridge the technical gap for you but you still need to dedicate the resources to manage it.

Once you start looking for it, you’ll quickly see that GTM AI tools fall pretty distinctly into one of the two camps. We have pretty consistently chosen centralized tools like Trig, Momentum, Datalane, Avarra etc. They’re managed by a few experts who then push the outputs into the surfaces that those teams already use (Salesforce, Salesloft, Slack etc). One of the big benefits of this approach is that you don’t get even more tool/surface creep than already exists. If anything I’m looking for opportunities to collapse surfaces, not expand them.

The second challenge is orchestration. Even if you have the right person, how do you keep all of it organized? You might have three or four different agents engaging with customers across different tools: your website chat, your outbound motion, your post-sales AI, maybe something internal. They all need to share context and stay coordinated, but right now there’s no clean answer for how to unify them. Do you collapse them together so you have fewer agents? Do they need some orchestration layer on top? Even people on the cutting edge are still figuring this out. You hear this problem described as the context or knowledge graph and it’s a big conversation on AI X. If you’ve solved this at your organization, please hit me up so we can share notes!

Where this leaves us

The decentralized approach can work, but if you want production-grade results, speed, and the kind of quality and volume that actually moves revenue, you need to centralize (in my humble opinion).

The centralized model lets you unbundle jobs into their component tasks, reassign those tasks to machines where it makes sense, and let humans focus on the parts they’re actually good at and enjoy doing. I think the gap between companies that figure out the centralized approach and those that don’t is going to widen very fast.

Revenue leaders love frameworks. MEDDIC, BANT, Challenger, SPICED; we collect them like trading cards and deploy them like cheat codes. But frameworks are pattern-matching tools, and pattern matching fails when the pattern changes.

Product leaders operate differently. They start from scratch every time. First principles. No inherited assumptions. And it turns out that mindset is exactly what revenue orgs need right now, because the patterns are changing faster than any framework can keep up.

Usha Iyer has seen both worlds from the inside. She started her career writing C++ algorithms for demand planning, eventually ran a $350 million product portfolio at Honeywell, and now serves as Chief Customer and Growth Officer at Hivebright, where she owns GTM, customer success, revenue strategy, and the US market. She went from building product to selling it to being accountable for the value it delivers post-sale.

That full-journey perspective is rare. Most revenue leaders have never sat in a product review, and most product leaders have never had to explain a forecast miss to the board. Usha has done both. And the gap between how these two disciplines think about problems is where this episode gets interesting.

We talked about what happens when you bring a product mindset to the customer journey: how to do real discovery, how to run experiments that don’t blow up in your face, and how to connect daily activity to outcomes that actually move the business. Here are five things revenue leaders can steal from the product playbook.

1. Your Frameworks Are Making You Lazy

Usha’s first piece of advice for revenue leaders sounds almost too simple: think from first principles. When a problem lands on your desk, resist the urge to reach for a framework. Instead, try to understand what’s actually happening as if you know nothing about the field.

This is hard for experienced leaders. Daniel Kahneman’s work on cognitive heuristics in Thinking, Fast and Slow explains why. Our brains default to System 1 thinking — fast pattern recognition built from years of experience. It’s efficient, and it’s often right. But when conditions shift (new market, new buyer, new competitive dynamic), those patterns become liabilities. You’re solving last year’s problem with last year’s playbook.

Product leaders are trained to fight this instinct. Usha described it bluntly:

“When something comes to you as a problem, you don’t try to apply patterns right away or frameworks all the time. Try to understand what is the underlying problem as if you’re a complete layman.”

She gave three specifics on what this looks like in practice. First, go deeper in discovery. Don’t stop at “they want to run events.” Ask how long the event runs, how many attendees show up, what the agenda structure looks like. Second, be an expert in your own product — know what it can and can’t do, and be honest about both. Third, don’t try to “yes” your way through a deal. Saying no, or admitting you don’t know, builds more trust than bluffing.

I think the deeper lesson here is about intellectual humility. Product leaders think in hypotheses. Revenue leaders think in decisions. A hypothesis can be wrong and that’s fine, because the process is designed for iteration. A decision carries ego, and unwinding it feels like failure. I’ve watched leaders run at an unsuccessful initiative for two or three quarters because reversing course felt like admitting they were wrong. Product teams kill experiments after two weeks and nobody blinks.

Paul Nutt’s research at Ohio State found that over half of organizational decisions fail, and the leading cause was premature commitment to a single solution before the problem was properly understood. Revenue leaders aren’t immune to this. We’re probably more susceptible, because we’re rewarded for speed and decisiveness.

2. Product Is Input-Driven. GTM Is Output-Driven. The Best Leaders Are Both.

This was the most interesting framing in the conversation. Usha’s CTO, Tharek, puts it this way: product is input-driven and GTM is output-driven. They’re constantly searching for equilibrium between the two.

Here’s what that means. A product team starts with a broad problem — say, “increase user engagement.” They don’t know what the solution will be. They run customer interviews, study the competition, analyze usage data, form a few hypotheses, build a proof of concept, test it with a small group, and let the output emerge from the process. They trust their inputs.

A revenue team starts with the output. Hit $10M this quarter. Work backward. How many deals, at what ACV, at what conversion rate, with how many reps? The entire operating rhythm is reverse-engineered from the number.

Neither approach is wrong. But Usha made a distinction that stuck with me: outcomes and outputs aren’t the same thing. The outcome she wanted was increased engagement. The output was a specific feature. You can reach the same outcome through different outputs, but only if you’re willing to let the process guide you there instead of locking in the answer before you start.

Teresa Torres’ Opportunity Solution Tree is the best visualization of this I’ve seen. You start with a desired outcome at the top, branch into opportunity spaces, then branch again into potential solutions. It forces you to map the full problem space before committing to any single path.

Revenue leaders can apply this directly. Take churn. The output-driven instinct says: churn is up, deploy a save playbook, run retention incentives, do more QBRs. The input-driven approach says: what data do I have on these churning customers? What patterns exist across use case, company maturity, champion engagement, integration adoption? Which segments retain best, and can I find customers who looked identical but churned anyway?

Usha nailed the practical version of this:

“Take your successful customers. Then go look at customers who look just like them but did not succeed. It becomes really easy to figure out what you need to double down on.”

That’s a control group. It’s how product teams test features. And revenue leaders almost never think this way.

3. The Metric That Doesn’t Show Up in Your Dashboard

Usha made an observation that I keep coming back to: product teams can measure every adoption signal. Feature usage, login frequency, NPS, support tickets. What they can’t see is friction. And by the time friction shows up in a metric you’re tracking, the customer is already gone.

She framed it as a tax:

“Friction is like tax. You have to decide how much tax you want to pay for the money you make. At some point, it’s not worth it.”

Customers will tolerate friction as long as the value exceeds the cost. But that calculation is happening in their heads constantly, and you have zero visibility into it until they tip.

Robert Sutton and Huggy Rao at Stanford spent seven years researching this for their book The Friction Project. They found something that should worry every operator: humans are wired to solve problems by adding complexity rather than subtracting it. We instinctively add processes, steps, approvals, and check-ins. We almost never remove them. They call it “addition sickness,” and it means the friction in your customer journey is almost certainly growing, even if nobody is doing it on purpose.

Richard Thaler and Cass Sunstein coined a term for this: sludge. It’s the dark side of the nudge. One-click to subscribe, phone call during business hours to cancel. Every extra step between your customer and the value they’re trying to get is sludge, and it compounds silently.

Revenue leaders tend to think about friction in terms of sales process — too many steps in the buying journey, too many approvals, too long a procurement cycle. But Usha’s point is broader. Friction exists across the entire customer lifecycle, and the post-sale friction is what kills retention. She gave an example from Hivebright: customers running in-person events had to manually upload attendee lists through APIs after the event. Some were hiring temps to do the data entry. The product team couldn’t see this in any dashboard. It took direct customer conversation to uncover it.

Once they found it, they built a simple QR code check-in feature. Small fix. Massive friction reduction. And it opened an entirely new product surface area they hadn’t planned for.

Product leaders sometimes dismiss these as “quality of life improvements” — nice to have, low priority. I’ve been on the other side of that conversation and it drives me crazy. When you’re sitting across from a customer and they’re describing the workaround they’ve built just to use your product, that’s not a nice-to-have. That’s a retention risk wearing a polite face.

4. Your Worst Instinct Is to Roll It Out to Everyone

Here’s what usually happens. A revenue leader identifies a problem, develops a conviction about the fix, and deploys it across the entire team on Monday. New talk track? Every rep gets it. New qualification framework? Updated in the CRM by end of week. New tool? Org-wide rollout, mandatory adoption, let’s go.

Product teams don’t work this way. And there’s hard data on why they’re right.

Stefan Thomke at Harvard Business School studied experimentation culture across major companies. At Microsoft, when teams run experiments based on reasonable, well-thought-out hypotheses, only about one-third produce positive results. Another third are neutral. The final third actively make things worse. That means even smart, well-reasoned ideas fail two-thirds of the time.

If those odds apply to your revenue org (and they do), rolling out every initiative to every rep is essentially gambling your team’s productivity on a coin flip. Worse than a coin flip, actually.

Usha described the alternative: small team, manual process, prove it works, then operationalize.

“Take a hypothesis. Pilot it. Set up a SWAT team if you need to. Pull out your best sellers or your best account managers. Let them run with that experiment. Do it manually. If you have to run things in spreadsheets, it’s okay. Then learn from that and operationalize it at scale.”

We did exactly this at Owner. We wanted to test an AI-powered outbound workflow for BDRs. Instead of pushing it to 50 reps and hoping, we picked four. Our head of GTM AI and a biz ops lead flew to Toronto and sat next to them. They ran iteration loops hourly. Try 30 calls, rebuild the prompts, try 30 more. Within two days they’d improved per-rep output by 85%. The rest of the BDR team watched the booking numbers climb and started asking when they could get access. We rolled it out from a position of proof, not hope.

Usha had an even bigger example. When Hivebright acquired Orbit, she didn’t hand it to the sales team and say “go sell this too.” She stood up a three-person SWAT team, pitched the first deal herself, ran six months as an overlay team, picked up the first million in revenue, and only then rolled it into the broader org.

She also shared an experiment that didn’t work, which I think is equally instructive. She built a Level 2 support team to handle technical escalations. Three or four months in, she realized she’d just created more friction between Level 1 and Level 2. The real fix was upskilling the core team. She dismantled it and moved on.

That’s the whole point. Small experiments give you the permission to be wrong quickly and cheaply, instead of being wrong expensively across the whole org.

5. You’re Pitching Your Product Team Wrong

I asked Usha what advice she’d give revenue leaders who feel like their product team never listens to them. Her answer was immediate:

“Don’t come with the solution. Come with the problem you’re looking to solve. What is the pain point? What is the impact of not solving that problem? What is the opportunity cost? If you can articulate that clearly, your product leader will listen.”

This hits a nerve because I’ve been on both sides of it. Revenue leaders walk into product reviews with fully formed feature requests: “We need a Salesforce integration. Customers keep asking for it. Build it.” And then they’re surprised when the product team pushes back or deprioritizes it.

Thomas Wedell-Wedellsborg surveyed 106 C-suite executives across 91 companies and found that 85% agreed their organizations were bad at problem diagnosis, and 87% said that flaw carried significant costs. The issue isn’t that leaders can’t solve problems. It’s that they solve the wrong ones because they never properly defined the right one.

Usha put it simply: “You’re not the solutioning person. Be the problem person.”

She flipped it to make the point stick. Imagine a product leader walking into your forecast call and telling you how to pitch a deal. You’d be annoyed. You’d dismiss it. That’s exactly how product leaders feel when you show up with a spec.

The better approach: frame the customer pain, quantify the impact, estimate the opportunity cost of inaction, and then let the product team figure out how to solve it. Come with ideas, sure. But hold them loosely. More often than not, the product team will find a better, cheaper, faster solution than what you had in mind, because that’s what they’re trained to do.

Dwayne Spradlin’s research at InnoCentive backs this up across thousands of problem-solving challenges: “The rigor with which a problem is defined is the most important factor in finding a good solution.” The better the problem statement, the better the solutions it attracts. Vague asks get vague results. Precise problem framing gets creative, targeted answers.

This applies beyond the product relationship, by the way. It’s how revenue leaders should approach any cross-functional ask — finance, marketing, ops. Lead with the problem and the impact. Let the domain expert own the how.

The Real Unlock

Every one of these ideas comes back to one shift: slow down the diagnosis so you can speed up the execution.

Revenue leaders are built for action. We’re hired for it. The instinct to move fast, commit to a direction, and rally the team behind it is what makes great sales orgs great. But that same instinct, applied to the wrong problem or deployed without evidence, is how you end up three quarters deep into an initiative that never had a chance.

Product leaders aren’t smarter than revenue leaders. They just have a different default. They assume they don’t know the answer yet. They build small before they build big. They instrument everything so they can learn from what happened, not just celebrate or mourn the result.

You don’t need to become a product person. But borrowing these five habits will make you a sharper operator: think from first principles before reaching for a framework, trust your inputs as much as your outputs, go find the friction your dashboards can’t see, pilot before you deploy, and lead every cross-functional conversation with the problem, not the solution.

The leaders who figure out how to blend product discipline with revenue urgency are going to be very difficult to compete against. And the ones who keep pattern-matching their way through a market that’s changing underneath them are going to wonder why the old playbooks stopped working.

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“I’ve never been strategic about my career. It’s a weird thing to admit as a CRO.”

That’s Michelle Donnelly, who spent a decade at Salesforce, then jumped to a pre-revenue AI chip startup that most people said would fail. It sold to Nvidia for $20 billion. Now she’s running revenue at Crescendo, a company that hit $100M ARR in under two years. Her secret? She followed the chills.

Not metaphorical chills. Literal ones. Every conversation with Groq’s founder Jonathan Ross gave her goosebumps. “Half the time I had no idea what he was saying. The guy is a genius. But I would get the chills.” So she jumped. Hardware. Pre-revenue. No product-market fit. Her brother worked at Switch, one of the largest data centers in the country, building infrastructure for OpenAI and Microsoft. His response: “Why are you going to do that? None of these startups win.”

He was almost right. What Michelle learned at Groq about building, breaking, and rebuilding nearly ended her. It also made her one of the most battle-tested revenue leaders in AI.

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I wanted to talk to Michelle because she’s lived through contrasts most of us only read about: pre-AI and post-AI selling, big public company and pre-revenue startup, hardware and software, Salesforce’s legendary go-to-market machine and the chaos of creating a category from nothing. What’s changed? What hasn’t? And what do you actually do when the market moves faster than your org chart?

Here are five lessons from our conversation.

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1. Your Network Is Your Oracle

Michelle didn’t find Groq on a job board. She found it through two phone calls.

The first was to Jessica, a friend who worked as the right hand to Deloitte’s CEO. “What’s next?” Michelle asked. Jessica’s answer: “Come to Deloitte. But if you don’t come to Deloitte, you need to go to Nvidia.” This was 2022. Pre-ChatGPT. Jessica saw what Deloitte’s largest customers were doing with GPUs and knew the wave was coming.

The second call was to Carla Stratfold, who had run global sales at AWS. Carla had just turned down the CRO role at Groq because she needed a break after a decade of hypergrowth. “You should talk to them.”

Two calls. Two people who thought about the future professionally. That’s it.

“This is the power of your network,” Michelle told me. “The future thinkers, the ones who really have a tap on what’s next.”

Mike Maples Jr., the legendary seed investor behind Twitter, Twitch, and Lyft, has a framework for this in his book Pattern Breakers. He argues that breakthrough startups are built on insights: non-obvious truths about how emerging inflections can be harnessed to change human behavior. The key word is non-obvious. If everyone sees it, the opportunity is already priced in. The path to outlier outcomes is being non-consensus and right.

But here’s the problem: insights don’t come from market research or trend reports. They come from people who are “living in the future”—immersed in a space deeply enough to see what’s missing before anyone else notices. Jessica wasn’t guessing about AI. She was watching Deloitte’s biggest enterprise customers pour money into GPU infrastructure. Carla wasn’t speculating about Groq. She’d spent a decade scaling AWS and knew what real compute demand looked like.

Most people use their network to find jobs. Michelle uses hers to find insights. There’s a difference. Job-seeking networks optimize for openings. Insight-seeking networks optimize for non-obvious truths about where the world is going.

Company picking has never mattered more. CB Insights data shows 42% of startups fail because they build something the market doesn’t need. The divergence between good and great outcomes is widening. Zero to $100M in two years is becoming common for the winners. For everyone else, the math is brutal.

Michelle’s filter combined intuition with sourced insight: “Every time I would talk to someone at Groq, I would get the chills. The people were incredible. The board was incredible. There’s something really special here.” Chills aren’t a strategy. But chills informed by people who live in the future? That’s a leading indicator worth following.

2. Product-Market Fit Is Binary (And Painful)

At Groq, Michelle built the enterprise sales organization from scratch. Public sector team. Commercial team. Finance and telecom verticals. She knew CX would be a huge market for real-time voice AI. She hired transformational leaders, experts in their crafts.

Then the market moved.

Meta released Llama. ChatGPT launched. Suddenly Groq could show side-by-side demos of how much faster their chip ran open-source models. Developers flooded in. The company pivoted to a developer-led motion overnight.

“Everything I built was gone,” Michelle said. “I actually left because I’m like, you guys don’t need me. You need an inside sales leader to do this developer stuff.”

This is the part of startup life nobody romanticizes. You can hire brilliantly, build systematically, and execute with precision. If product-market fit shifts, none of it matters. Your org chart becomes a memorial to a market that no longer exists.

Michelle calls it the hardest lesson of her career. And it nearly broke her. But it also gave her a non-negotiable for what came next: “If you didn’t have product-market fit and you couldn’t show me the number of customers that were buying and how customers were increasing their spend with you, I didn’t want to work with you.”

The research backs her paranoia. According to CB Insights, lack of product-market fit is the single biggest startup killer. Failory’s analysis of 80+ failed startups found 34% died because they built something the market didn’t want. And here’s the cruel part: 65% of failed startups built their product before validating the market. They got the sequence backwards.

Groq eventually found its fit. The developer motion exploded. Nvidia bought the company’s assets for $20 billion in December 2025. But Michelle wasn’t there to see it. She’d already moved to Crescendo, armed with scar tissue and a new rule: product-market fit first, everything else second.

3. Simplicity Is the Hardest Go-to-Market Problem

Michelle walked the floor at NRF, the largest retail conference in the country. Every booth was screaming about AI agents. Every pitch sounded identical. “It’s so noisy. It’s so confusing. It’s really hard to differentiate.”

She felt what her customers feel: overwhelmed, skeptical, and unsure who to trust.

Crescendo’s answer isn’t a better feature list. It’s radical clarity. “If you can’t understand who we are in 30 seconds, we have hashtag failed.”

Getting there has been a huge, focused effort. They’re on their fifth pitch deck in six months. They’ve tested messaging with BDRs doing 400 calls a day, run it past existing customers who told them the pricing was too complex, brought in outside consultants (some helped, some didn’t), and ripped apart their positioning in real-time workshops. “It took 20 minutes for customers to get the aha moment,” Michelle said. “That’s way too long.”

The research confirms what Michelle learned the hard way. Gartner found that B2B buyers spend two-thirds of any buying journey “gathering, processing, and de-conflicting information.” The buying process has become, in Gartner’s words, “nearly unnavigable.” What customers actually value? Suppliers who make the purchase process easier through the right information, through the right channels.

Simple sounds easy. It’s the opposite. “Simplicity is actually really hard to get to,” Michelle admitted. “I came from Salesforce. The messaging was amazing. But this is complex.”

Her forcing function: three priorities. Not four. Not five. “Everybody in my org can repeat to you on call, what are the three things we’re doing this quarter?” When your CEO has a thousand ideas a minute (Michelle’s does—he uses Whisper to track them all), someone has to be the filter. That’s the CRO’s job. Understand the vision, translate it to a number, simplify it for the team.

4. Show, Don’t Demo

In a low-trust market, everyone claims to be better. Michelle’s response: stop claiming, start proving.

When a gaming company called on a Monday needing help before the Super Bowl, Crescendo didn’t schedule a demo. Their head of product built a custom AI agent using the company’s publicly available knowledge base. Within 24 hours, they could show the prospect how their specific use case would work—voice and text—on their actual content.

“Wouldn’t you rather see an experience customized to how I’m going to solve your pain at Owner versus seeing a demo for the restaurant industry?” Michelle asked me. “You want to see how it works for you.”

This is only possible because AI has collapsed the build time. What used to take weeks of custom development now takes hours. Crescendo uses that speed advantage not just in their product, but in their sales motion.

They’ve extended the “show me” philosophy further. Michelle tells prospects to go become customers of Crescendo’s existing clients. “Go to Lovepop and buy a card. See how they upsell you to buy more cards for Mother’s Day and Father’s Day. Go check out Doc Martens and see how they increase your shopping basket.” One prospect went to test a client’s experience and hated it—the AI didn’t authenticate her as a gold member. Michelle’s team turned it into a teaching moment: “They chose not to do that. If you want to be authenticated, let us show you how.”

The underlying principle: in noisy markets, tangibility beats promises. Generic demos create generic trust. Custom proof creates specific confidence.

5. Talent Density Beats Headcount

“All of my BDRs are athletes. Every single one of them.”

Michelle hires grinders. Crew. Swimming. The sports that just suck. “We go after people who are grinders in this type of company.”

This isn’t random pattern-matching. It’s a deliberate bet on talent density over headcount. Reed Hastings coined the term at Netflix after the dot-com crash forced them to lay off a third of their workforce. What happened next surprised everyone: the remaining employees became more engaged and more productive. Hastings realized a small team of high performers will almost always outperform a larger team of average hires.

McKinsey’s research supports the bet: high performers can be 400% more productive than average employees. In complex roles like software development, that number climbs to 800%. Netflix now generates almost $3M in revenue per employee—twice Google’s rate, ten times Disney’s.

Michelle’s version of talent density goes beyond hiring. It’s about focus. “I don’t want to give them four priorities, I don’t want to give them five. They know that everybody in my org can repeat to you, what are the three things we’re doing this quarter?”

She also uses AI to compress ramp time rather than reduce headcount. The typical sales rep takes 11.2 months to reach full productivity, according to the Sales Management Association. Michelle’s goal: cut that from five months to three using Crescendo’s own AI assistant, Harmony, which her reps use to instantly pull competitor intel, find lookalike customers, and access the full knowledge base.

“Nine out of ten companies don’t hire enough capacity,” she said. But capacity without density is just headcount. And headcount without focus is chaos.

The Builder’s Throughline

Michelle’s career looks like a series of wild bets: architecture to furniture sales, Salesforce to pre-revenue hardware, a $20B exit to another hyper-growth startup. But there’s a throughline she named early in our conversation: “The thing that’s been consistent throughout my entire career is that I love to build. And I love sales.”

Builders and caretakers approach the same job differently. Caretakers optimize existing systems. Builders tear them down and reconstruct them when the market demands it. Michelle built an enterprise sales org at Groq, watched it become irrelevant overnight, and walked away. Then she built again at Crescendo—with the scar tissue to know that what she’s building today might not be what wins tomorrow.

The fundamentals she keeps coming back to aren’t complicated: Tap people who live in the future. Demand product-market fit before you commit. Simplify relentlessly in noisy markets. Prove value instead of promising it. Hire gritty people and give them focus, not chaos.

None of this is new. But in a world moving this fast, the basics aren’t basic. They’re the difference between scaffolding and structure.

Michelle ended our conversation with advice for new sales leaders: “Be really clear on what your goals are. Stay focused. Make sure your customers are really happy. Listen to them. Make sure you’ve got the right talent. And don’t be afraid to make changes quickly.”

Simple. Hard. And apparently, timeless.

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Everyone’s chasing AI tools. The top-performing CROs are chasing something else entirely.

Jeremy Donovan has spent four years advising 500 B2B SaaS companies at Insight Partners. He just surveyed 150 CROs on what’s actually working with AI in sales. The answer surprised me: it has almost nothing to do with which tools you’re using.

The differentiator is disciplined execution. Deal reviews. Pipeline rigor. ICP focus. The boring stuff.

Jeremy’s background makes him worth listening to on this. He started as a semiconductor engineer, got a master’s in data science from UVA, an MBA from Booth, picked up a CFA. Then he went operator side: nearly four years at SalesLoft running solutions engineering, rev ops, and revenue strategy. Before that, almost two decades at Gartner and GLG.

Engineer’s rigor. Operator’s scars. Investor’s pattern recognition. He’s watching AI play out across hundreds of companies in real time, and he’s got the data to back up his takes.

Here’s what the data says: productivity gains are running 5-15%. Modest. Full automation keeps failing. But augmentation is working quietly in companies that were already executing well. AI amplifies what’s there. It doesn’t create discipline from nothing.

We got into it all. Where AI actually delivers. Why the SDR role is getting absorbed, not eliminated. What “build vs. buy” looks like when vibe coding changed the math. And why 2026 might be the year this moves from bottoms-up experiments to top-down systems transformation.

Jeremy also dropped what I think is the most important insight of the conversation: we’ve been optimizing existing processes with AI. The real unlock is rethinking the processes entirely. Unbundle the job. Figure out what’s an AI task and what’s a human task. Then rebuild.

That’s a bigger swing. And nobody’s really doing it yet.

Disciplined Execution Beats AI Tooling

I asked Jeremy what separates companies getting value from AI versus the ones spinning their wheels. His answer was blunt: there’s no silver bullet.

Top performers and average performers are adopting the same use cases in roughly the same order. The difference? The top performers were already disciplined before AI showed up.

“If I were to go back into an operating role and could only run one play, it would be incredibly disciplined weekly deal reviews.”

He’s not wrong. When I talk to struggling sales orgs, the pattern is obvious. They want the new tool to fix a process problem. But tools don’t fix process. They expose it.

Jeremy does a lot of CRO interviewing for Insight’s portfolio companies. The number one thing he screens for: operating rhythm. How disciplined is this person? Do they stick to it? He doesn’t just ask the candidate. He back-channels their former teams to find out what it was actually like to work for them.

This tracks with research on expertise. K. Anders Ericsson’s work on deliberate practice shows that feedback-rich environments with tight iteration loops produce mastery faster than anything else. The CROs who run tight deal reviews, who hold reps accountable to pipeline generation time, who actually inspect what they expect? They’re building that feedback loop into the system.

AI accelerates the loop. But you need the loop first.

One CRO Jeremy interviewed writes his own prompts. You could argue that’s not the best use of a CRO’s time at a bigger company. But at his scale, it means he deeply understands the system. He can train his team because he’s trained himself. That kind of hands-on ownership compounds.

The takeaway for me: stop asking “which AI tool should I buy?” Start asking “how tight is my operating rhythm?” If your deal reviews are sloppy, if your pipeline inspections are sporadic, if your reps don’t know whether they’re hitting their activity targets until month-end, AI won’t save you. It’ll just make the mess visible faster.

2026 Is the Year of Top-Down AI

For the past two years, AI adoption in sales has been mostly bottoms-up. Reps tinkering with ChatGPT. Managers building prompt libraries. RevOps experimenting with enrichment tools.

Jeremy thinks that era is ending.

“There’s only so much you can do in the foundation LLMs,” he told me. The real value requires orchestration across systems. Snowflake. Salesforce. Your data warehouse. Your security layer. And the moment you need that kind of access, a random AE can’t just point an agent at those systems and start mining data.

Security gets involved. Governance gets involved. IT gets involved.

The companies moving fastest have appointed what Jeremy calls an “AI czar.” Someone with organizational influence who owns internal AI optimization. These people are coming out of RevOps, PMOs, finance. They showed passion early, and now they’re project-managing the complex integrations that bottoms-up tinkering can’t touch.

This matches what I’ve seen at Owner. We made aggressive early investments in AI across GTM, but the work is being run out of our data team. We now have applied AI folks embedded in that org. Because the real leverage comes from combining Salesforce data with Snowflake with external research with internal playbooks. You can’t do that in a chat window with limited MCP access. You need infrastructure.

Gartner’s hype cycle framework is useful here. Early 2025 was peak inflated expectations. Everyone was promising astronomical improvements in win rates. By late 2025, we landed in the trough of disillusionment. LinkedIn was full of “AI isn’t working in sales yet” takes.

The climb out of that trough requires systems-level thinking. Top-down investment. And probably a lot less hype about individual tools.

For CROs, this means: if you don’t have someone who owns AI orchestration internally, you’re already behind. Find that person. Give them authority. Let them partner with security and IT rather than fighting them. The bottoms-up era gave us useful experiments. The top-down era is where the real productivity gains live.

The Build vs. Buy Pendulum Is Swinging

Jeremy dropped a stat that surprised me: roughly 50% of companies are now building their own RFP response tools instead of buying off-the-shelf solutions.

A year ago, that would have been crazy. RFP software was a mature category. Vendors had spent years building integrations, compliance features, knowledge bases.

What changed? Vibe coding.

“It’s just so freakishly fast,” Jeremy said. He told me a story about needing to convert a calculator from JavaScript to a Microsoft Power App. His engineering team quoted a month with outsourced developers. Jeremy opened Cursor in agentic mode, described what he needed, and had a working app in 60 seconds.

The math on build vs. buy has shifted. When you can prototype something in an afternoon that used to take a quarter, the calculus changes. You build exactly what you need. Less friction. Better fit.

But Jeremy was careful to note the other side. Historically, most internal builds stop working within a year. Nobody maintains them. The world moves on. We’re going to see a lot of shelfware from this era of rapid building.

The smart framework here: prototype internally, validate the use case, then decide whether to productize or buy. Jeremy did this at SalesLoft. He’d build something scrappy, run it for six months, and if it stuck, he’d go find a vendor who could do it better at scale.

There’s also a complexity threshold to watch. Simple use cases favor building. But when you need predictive ML combined with generative AI combined with workflow automation combined with security compliance? That’s where platforms start winning again.

Jeremy gave a good example from customer success. Predicting churn is a traditional ML problem. But once you identify an at-risk account, you need to generate a workflow, draft messages, maybe trigger an escalation. That heterogeneity of AI types gets complicated fast.

My take: build for speed and learning, but don’t fall in love with your prototypes. The companies trying to run production workloads on duct-taped internal tools are going to hit a wall. Know when to graduate to real infrastructure.

AI Makes Humans More Valuable, Not Less

Here’s a stat that should reframe how you think about AI in sales: 70% of outbound-sourced opportunities are still booked via phone.

Not email. Not LinkedIn. Not AI-generated sequences. Phone.

Jeremy’s been in the SDR game for 15+ years. He’s watched cold outbound go from shockingly effective to genuinely difficult. Response rates cratered. Touches per opportunity went from 200-400 five years ago to 1,000-1,400 today. Email is essentially destroyed as a channel for cold outreach.

And yet. The phone endures.

Why? Robert Cialdini’s work on reciprocity gives us the answer. We respond when another human puts genuine effort into engaging us. The reverse is also true. If you phone it in with obvious AI-generated outreach, people don’t just ignore you. They flag you as spam and hope your emails never arrive again.

This is why Jeremy sees augmentation working where automation fails. The winning formula is human + AI, not AI replacing humans.

The SDR role isn’t dying. It’s shape-shifting. Jeremy expects outbound SDR responsibilities to get absorbed back into AEs, especially as AI handles research and personalization. Inbound SDRs are disappearing faster because routing and qualification can be automated when prospects have already opted in.

But here’s the part that gave me pause: one CRO Jeremy works with ripped out an AI tool that auto-extracted MEDDIC fields from call transcripts. The tool worked perfectly. The problem? “The AEs were getting stupid.” They stopped thinking critically about their deals because the AI was doing the synthesis for them.

She removed the tool to protect her team’s judgment.

That story is a warning. AI that removes cognitive load can also remove cognitive development. The best implementations keep humans in the loop not because the AI can’t do the task, but because the human needs to stay sharp.

The Unlock Is Rethinking Roles, Not Optimizing Tasks

This was the insight that stuck with me most.

Jeremy put it simply: “So far, we’ve tried to optimize the jobs to be done within the existing role structure and within existing processes. The unlock is to say, maybe we don’t have the roles right.”

We’ve been using AI to make the current system faster. Write emails quicker. Research prospects in less time. Summarize calls automatically. Those are real gains. But they’re incremental.

The bigger swing is unbundling roles entirely.

I talked about this with Jordan Crawford on a recent episode. His frame: look at a role as a basket of tasks. Unbundle it completely. Which tasks are AI tasks? Which tasks are human tasks? Once you’ve sorted that across your SDR, AE, and SE roles, you might find the optimal grouping looks nothing like what you have today.

Jeremy sees this happening with sales engineers already. The low-end SE work is getting absorbed into AEs as AI handles more technical Q&A in real-time. The high-end is evolving toward forward-deployed engineers who embed deeply with customers post-sale. The middle is getting squeezed.

This requires real creativity. You’re not optimizing a process. You’re redesigning how work gets done. Most companies aren’t there yet. They’re still in the “add AI to existing workflows” phase.

But the productivity gains everyone expected from AI? They’re probably hiding on the other side of that redesign. The 5-15% lift CROs report today is the optimizing-existing-tasks version. The 30%+ lift requires rethinking the tasks themselves.

The Clock Is Running

Jeremy and I covered a lot of ground. The through-line: AI rewards discipline and punishes sloppiness.

If your operating rhythm is tight, your deal reviews rigorous, your pipeline inspection consistent, AI will amplify that. You’ll move faster. Your reps will be better prepared. Your forecasts will be more accurate.

If your operating rhythm is loose, AI will expose every crack. The tools will sit unused. The data will be garbage. The productivity gains will never materialize.

The question for every CRO is simple: are you building the infrastructure for AI to accelerate, or are you hoping AI will fix problems you haven’t solved?

One path leads to compounding advantage. The other leads to expensive shelfware and frustrated teams.

And the companies that figure out how to truly rethink their roles and processes, not just optimize existing ones? They’re going to create a gap that’s very hard to close.

The clock is already running.

If this episode gave you something to think about, I’d appreciate a rating or review on Apple Podcasts or Spotify. It helps other revenue leaders find the show!

I noticed something interesting about Owner at 10:30pm last Tuesday night.

Someone on our Biz Ops team requested to install a Slack app for a side project he was working on. The thing that stood out is that it was 1:30am his time. He was up late cooking but not because of a deadline. Not because someone was breathing down their neck. Just because he was excited to build.

I was up late too, deep in Claude Code working on a project I couldn’t put down. And when I looked at Slack, there were a bunch of other people online for the same reason. I’ve certainly been accused of it but this isn’t workaholism. It’s something fundamentally different imo.

Psychologist Robert Vallerand’s research on passion draws exactly this distinction. He found two types of passion for work: harmonious passion (where the activity remains under your control, integrated with your life) and obsessive passion (where the activity controls you through anxiety and compulsion). Both drive long hours. Only one is sustainable and actually improves performance.

I recently asked one of our senior product leaders (a true 10X engineer himself) what really separates the 10X engineers from the rest. I never understood it as a non-technical person. I assumed he’d say decision-making, grit, or resourcefulness etc.

His answer: “Love of the game.”

He explained that some people just can’t stop thinking about their work. When they hit a gnarly engineering problem, they ruminate on it for days (in the shower, on walks, at 2am) until they arrive at some epiphany that solves it in an elegant or novel way.

That’s not something you can train. It’s not something you can incentivize with comp plans or stock. It’s intrinsic. That same mutant bailed our on typical Monday morning workout because he was up until 1:30am building.

Elena Verna (who is an absolute legend in the Growth world) once said that her job is her hobby on a podcast and that always stuck with me. I think we’ve accidentally built a company of people whose jobs are their hobbies at Owner and I’m realizing this might be one of our biggest competitive advantages.

A few examples:

My VP of RevOps, Steve Dinner, spent a good chunk of the holiday break texting me about a Claude-based RevOps agent he was building. Not assigned. Not requested. He just couldn’t help himself. That agent can now do the work of multiple business analysts in minutes and is going to let us ship a bunch of Q1 roadmap items that wouldn’t have made the prioritization cut otherwise.

One of our applied AI leaders said this to his boss: “I used to tell everyone my role at <redacted> was my dream job when I was working there. But this is the real dream job. My Mom is gonna kill me but I’m gonna start working on <redacted> during my vacation hahahaha.” That’s love of the game in action.

His boss said this to me: “I feel like there's a certain persona that finds the stuff we do so fulfilling that it doesn't feel like work / does it in their free time, and I just need to spend all my recruiting energy just finding that.”

We’ve never explicitly screened for this in our hiring process. But I’m making the implicit explicit moving forward because I think it’s a superpower.

How to Screen for This in Interviews

Here are some questions that seem to surface this trait even though it wasn’t intentional:

• “What are you building or learning outside of work hours right now?”

• “What are you doing with AI right now? What have you build for yourself?”

• “What content do you consume about your craft? Podcasts, books, newsletters?”

The answers reveal whether someone is pulled toward the work or just pushing through it.

Workaholics grind because of anxiety, obligation, or external pressure. People with love of the game build because they genuinely can’t help themselves. The energy is completely different—and so is the output. I think this is one of the reasons that we have so many former founders in our EPD organization.

The research backs this up. A meta-analysis in Frontiers in Psychology found that work engagement (characterized by enjoyment without compulsion) actually protects against depression and burnout. Workaholism, despite the long hours, predicts worse mental health and doesn’t improve performance. The energy source matters more than the hours logged.

How to Attract These People

Your job postings, culture content, and interview process either signal “this is a place for builders” or they don’t.

A few things that I think work:

• Be explicit about the pace and intensity of your environment

• Talk about what you’re actually building and why it matters

• Let your current builders be visible (podcasts, LinkedIn, conference talks)

• Don’t oversell work-life balance if you’re a high-growth startup—the right people want the intensity

The wrong people will self-select out. That’s the point.

How to Keep Them

Attracting builders is one thing. Keeping them is another. Here’s what I’ve learned:

1. Let builders build. “Let them cook,” as we say at Owner. Give them room to experiment and tinker. Throw them big, ambiguous projects. If you hire someone with this energy and then saddle them with process and approvals, you’ll kill exactly what made them special.

2. Stay out of their way. These people are allergic to bureaucracy. When they have ideas that seem a bit out there, let them rip. The ROI on letting a builder follow their intuition is enormous.

3. Keep the bar extremely high. Builders want to be around other top talent. If you tolerate mediocrity, your best people will leave. Full stop. You have to performance manage out the folks who aren’t keeping up—not because you’re ruthless, but because your top performers deserve to work alongside other top performers.

These three principles map directly to the three psychological needs that drive intrinsic motivation: autonomy (let builders build, stay out of their way), competence (keep the bar extremely high), and relatedness (being surrounded by other top performers). A meta-analysis showed intrinsic motivation accounts for 45% of variance in work engagement and performance, which is nearly triple the impact of extrinsic motivators.

The Compounding Effect

Once you become an environment for builders, they attract builders.

When you hit critical mass of people who treat their work as a craft, it becomes self-reinforcing. They refer their friends. They raise the bar in interviews. They create an environment where that energy is the norm, not the exception.

And then you end up with a Slack that’s active at 1:30am. Not because anyone has to be there, but because they just want to cook.

I was recently featured in Vasco’s 2026 RevOps Trends & Predictions report, and I want to expand on some of my thoughts because I don’t think most revenue leaders are grasping the urgency of what’s happening.

Here’s the uncomfortable truth: If your RevOps team hasn’t fundamentally transformed in the last 24 months, you’re already losing.

That’s not hyperbole. We’re over two years into the AI era, and the gap between companies that have adapted and those still “figuring it out” is becoming a chasm. I talk to revenue leaders all the time who can’t get a single meaningful AI use case into production. Meanwhile, we have eight ten high-value AI implementations in production at Owner.

The difference is talent and foundations and we need to have an uncomfortable conversation about it.

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The Data Foundation Problem Nobody Wants to Talk About

Most companies are trying to bolt AI onto broken infrastructure. It’s like trying to install a turbocharger on a car with a cracked engine block.

Here’s the shift that the best GTM teams have already made: Snowflake is our system of record, not Salesforce. The CRM is where reps live and work. But the data manipulation, transformation, processing, and intelligence? That happens in the data cloud.

AI is only as good as the data underpinning it. When you want to build an ML-powered account scoring model, you’re not doing that with a deterministic formula in a Salesforce field. You’re building that model in your data warehouse and pushing the intelligence back to the places your team operates.

The companies struggling to get value from AI almost always have the same root cause: data quality and data foundation issues. They’ve got data siloed across sales, marketing, product, and third-party sources with no coherent way to synthesize it and instead of biting the bullet and addressing it, they make excuses.

My CEO hit me with this absolute banger this week: “If you argue for your limitations, you get to keep them,” which is exactly what I hear from leaders who tell me their AI investments are stalled because the data team hasn’t addressed the problem. You have to force them to fix it. Call a code red at the exec level and get the resources you need to address it or you’re cooked.

Why RevOps Leaders Need to Become Technical (Or Get Replaced)

I said something in the interview that might sound harsh: if your RevOps team hasn’t evolved, those probably aren’t the right leaders.

The world is going to reward the innovative, curious, forward-thinking tinkerers. RevOps needs to get more technical. You need people who can do more themselves rather than shipping everything to developers. My VP Rev Ops and I were texting all Christmas break as he was building a rev ops agent that is now churning out insanely high quality PRDs and building production ready features.

The pace of change is accelerating. If you’re a company moving slower in AI adoption than your competitors, you will lose. Full stop.

What our data science team can produce in a week or two used to take months during my Shopify days. They’re building and coding with AI, running evals and tests, iterating rapidly, and shipping high-quality projects. Writing SQL queries is now instantaneous because we can just ask AI to do it.

The Compounding Flywheel You Can’t Ignore

Here’s where this gets existential for laggards: AI creates compounding advantages.

Think about it this way: Our GTM AI Lead builds something that makes every sales rep 10% more efficient. That means we get 10% more ARR for every headcount. Which means we’re growing faster for the same cost. Which means there’s more capital to invest in product. Which means our product gets better. Which means our brand gets stronger.

Traditional automation might make your sales reps 5% more efficient. AI can make them 50% more efficient. We just ran a pilot this week with a new tool we built that drove up BDR decision maker connects by 85%. EIGHTY FIVE PERCENT!! That was two weeks of work from our applied AI guys.

This is why our sales reps are three to four times more efficient than most of our competitors. We were late to our market and most competitors had four or five year head starts on us. And yet our product is now miles ahead in almost every way. That gap keeps getting bigger and bigger.

This is the trend I expect to accelerate in 2026: Winners in each category will break away so fast that we’ll see a massive flight to quality. The best talent flocks, the key players in the ecosystem want to partner, the VC dollars flow in and the market is won. I believe the gulf between the great companies and the rest will widen dramatically and we’ll see an exaggerated winner-takes-all outcome in many spaces.

The Cultural Transformation That Actually Matters

There’s a bigger shift happening that doesn’t get enough attention: GTM is evolving from a people-centric model to systems architecture model.

That sounds cold, but hear me out.

We can now build purchase experiences that are agent-based and don’t require humans. We can serve parts of our market with manageable unit economics that were previously uneconomical. We can introduce AI agents and workflows into the customer journey to accelerate deals, save money, and improve the experience.

But here’s the catch: the leaders managing these teams have to think in an AI-native mindset. They need to operate like systems engineers designing agent-based customer experiences, not just managers of people.

This is the cultural transformation that separates winning companies from everyone else. You need two things: strong data foundations AND leaders who can think architecturally about how AI fits into every part of the customer journey. This is a perquisite for anyone in a senior leadership role.

What I’d Rebuild From Scratch

If I could tear down one category and start over, it would be sales engagement platforms.

Most of them are trying to slap AI into existing products, and it’s not working because it’s not built from AI-first principles. It’s AI for the sake of AI and it’s a closed ecosystem.

What I want as a business leader is composability, flexibility, and an open API ecosystem. I want control over how the product works. I want to bring my own model and don’t want to be token constrained. I want to be able to build on top of these tools, not be trapped by them.

The model I’d love to see is Shopify: simple out of the box for a mom-and-pop operation, but endlessly customizable for teams that want to build sophisticated, deeply integrated experiences. A developer-centric approach where you can actually code on top of the platform.

That experience layer is missing from most sales acceleration tools right now.

The Bottom Line

The conventional wisdom says transformation takes time. That you should be thoughtful and cautious. That you should wait for the tools to mature.

That conventional wisdom is going to get a lot of companies killed.

The companies who invest in data foundations now, who hire AI-native leaders, who move faster on adoption, who take risks are going to break away from the pack in ways we haven’t seen before.

And the companies still evaluating pilots? They’re going to look up in 18 months wondering what happened.

The reckoning is already here. The only question is which side of it you’re on.

If you found this valuable, you can check out the full Vasco 2026 RevOps Trends & Predictions report featuring perspectives from Jeff Ignacio, Beth Kel, Cliff Simon, and other operators on where RevOps is headed.

Most revenue leaders think RevOps maturity means specialization. Comp analysts. Dashboard builders. Deal desk processors. Clean swim lanes, narrow mandates, expertise in one domain.

Tyler Will is betting the opposite direction. At Intercom, he’s watching the “file a ticket, get a dashboard” model die in real time. When leaders can query Snowflake from Claude Code in three minutes, the specialists who only knew how to execute are exposed. What survives is the ability to ask the right question — to show up to the forecast meeting with a point of view, not a notepad.

AI doesn’t eliminate junior work and preserve senior judgment. It eliminates answers and elevates questions. The RevOps generalist, once seen as a phase you grow out of, is becoming the endgame.

Tyler is VP of RevOps at Intercom, where he’s spent the last three years rebuilding the go-to-market engine for the AI era. Before that: six years at LinkedIn across RevOps and strategy, and five years as a management consultant at Bain. He’s seen revenue operations from every angle — strategy, execution, and now transformation.

Intercom has become a case study in AI-native go-to-market. They launched Fin, their AI agent, and shifted from seat-based to resolution-based pricing. But the product transformation forced something harder: rewiring comp plans, post-sales motions, and the entire RevOps operating model. Tyler has been in the middle of all of it.

This conversation covers the tactical details most AI discussions skip — how comp plans actually change for usage-based models, why post-sales teams are growing instead of shrinking, and what it means to instrument a revenue system when the old metrics no longer apply.

1. The RevOps Specialist Is an Endangered Species

The classic RevOps org chart is built on specialization. Comp analysts who own comp. Dashboard builders who own BI. Deal desk processors who own approvals. It mirrors the Frederick Taylor model of scientific management — break work into discrete tasks, assign specialists, optimize each function independently.

Tyler is watching that model collapse.

“The ability to ask the right question suddenly becomes way more valuable. It’s always been valuable, but now that’s the thing that’s going to keep you employed and busy — versus ‘I can follow a process and do it accurately.’”

The catalyst is self-service. Tyler queries Snowflake directly from Claude Code — questions that used to require a ticket, a dashboard build, and a week of waiting now take three minutes. His SVP of Sales found $9 million in miscategorized pipeline the same way. (It was a Claude hallucination. But the speed of discovery — and correction — would have been impossible a year ago.)

This doesn’t eliminate the analytics team. It changes what they do. McKinsey’s 2024 research on AI in the workplace found that knowledge workers spend 60% of their time on tasks that AI can now accelerate or automate — data collection, processing, and basic synthesis. What remains is judgment, context, and business acumen.

“They actually have to be closer to the business. There’s no longer a ‘file a ticket, we’ll build a dashboard, here you go.’ It’s them getting embedded and coming to the forecast call and hearing ‘here’s a thing we’re concerned about’ — and being able to dig into that and write a great analysis.”

The RevOps analyst who shows up to take notes is already redundant. The one who shows up with a point of view is irreplaceable.

The shift: From “I know how to build this” to “I know what question to ask and what the answer means for the business.”

2. AI Literacy Is an Asymmetric Bet

Jason Lemkin argued in a recent episode that CROs need to implement at least one AI tool end-to-end — not delegate it, do it themselves. Tyler doesn’t go quite that far, but he lands closer to Lemkin than to the “my RevOps team will figure it out” camp.

“If you sort of draw the pendulum — he’s at the extreme of ‘got to do it yourself’ to ‘can be completely ignorant of what’s going on’ — I fall probably two-thirds of the way toward Jason’s position.”

The logic is asymmetric risk. Being at 95% AI fluency beats 75% even if 85% is technically optimal. The downside of over-investing is wasted time. The downside of under-investing is existential.

“There are plenty of things in life where you could be off by 20% either way and it kind of doesn’t matter. This one is like — you’re probably better at 95% than you are at 75% if 85% is the right answer.”

This echoes what Ethan Mollick calls the “jagged frontier” of AI capability — the boundary between what AI does well and what it doesn’t is irregular and constantly shifting. Leaders who stay close to that frontier learn where to trust AI and where to override it. Leaders who delegate entirely never develop that judgment.

For Intercom specifically, AI fluency isn’t optional — they’re selling an AI product. A CRO who can’t explain how Fin works, what RAG means, or how the ATTD flywheel (analyze, test, train, deploy) operates will “look pretty stupid” in front of customers.

But even for companies not selling AI, Tyler sees the same dynamic playing out internally. The leaders who understand their tools — Clay, Gong, Claude — will instrument their revenue systems in ways that leaders who delegate simply can’t imagine.

The frame: AI literacy isn’t a setting to optimize. It’s a bet where the penalty for undershooting is getting lapped.

3. The Post-Sales Motion You Knew Is Gone

Here’s a narrative violation: Intercom sells an AI product designed to automate customer support. Their post-sales team is growing.

The shift from seat-based to resolution-based pricing broke the old CSM playbook. Tyler describes the old world with a kind of nostalgia:

“There’s something almost quaint at this point about selling credit seats. They provision it to everybody, assign a bunch of users, run a tutorial. ‘How about it?’ And you call us if you need anything.”

That motion assumed a ceiling — X seats, fixed price, predictable usage. The CSM’s job was adoption and retention. Expansion happened at renewal.

Resolution-based pricing inverts the model. Customers start small — maybe 15% of their support volume routed through Fin. The CSM’s job becomes continuous expansion: unlock the next tier, add the next channel, build the next procedure. Every increase in usage is both a success outcome and a commercial event.

“There’s much more of a continuous learning and continuous engagement that is needed. It becomes both a success motion and engagement motion — getting people comfortable with that. But then it becomes a commercial conversation again.”

Gainsight’s 2024 Customer Success Index found that usage-based models require 40% more customer touchpoints than seat-based models to achieve the same net revenue retention. The motion is heavier, not lighter.

Intercom responded by splitting roles and creating new ones. Pre-sales and post-sales SEs are now separate specializations. They built an R&D Services team (think forward-deployed engineers) to help customers implement sophisticated use cases. CSMs are being upskilled to be more technical and more commercially aware.

“We’re asking more from certain roles. We’re splitting roles, creating new roles. That’s all a product of the nature of this AI product.”

The insight: AI doesn’t eliminate customer success. It transforms it from reactive support into continuous commercial engagement.

4. Comp Plans That Reward Usage, Not Just Contracts

When Intercom shifted to resolution-based pricing, their comp plans broke immediately.

The problem: customers would commit to a small Fin contract, then blow past it in actual usage — entering “overages” or pay-as-you-go territory. None of that incremental revenue hit quota. So reps did the rational thing: they chased every overage customer, trying to get them to add $500 or $1,000 to their contract. Terrible experience for customers. Waste of rep time.

“As soon as somebody would go into overages, they’d run them down and go, ‘Put another hundred Fin resolutions a month onto a contract.’ That wasn’t a great experience for anybody.”

Tyler’s team rebuilt comp in phases.

Phase 1: Credit for overages. Reps got quota credit for contracted amounts plus the MRR of non-contracted usage. They didn’t annualize it (to avoid rewarding seasonal spikes), just gave monthly credit. This stopped the overage-chasing behavior while rewarding reps who drove actual usage.

Phase 2: Pure MRR accumulation. The account management team moved to a model where they accumulate the MRR of all their accounts over the comp period. A solid contract flows for six months; you get credit each month. Usage goes up, you benefit. Usage drops, you feel it.

“It’s been interesting. It’s complicated. I don’t not recommend it, but I also am not like, ‘We found the answer, everyone should do this.’ It’s a little messy at times.”

The messiness is the point. OpenView’s 2024 SaaS Benchmarks found that companies with usage-based pricing models change their comp plans 2.3x more frequently than seat-based peers. The business model requires iteration.

Tyler’s comp principles survived the chaos:

• Two metrics max (maybe three for one plan)

• Meaningful weights — no 5% slivers for pet projects

• Instrumentable — reps can see their performance in real time

• Explainable in five minutes on a whiteboard

“If we feel like one of those breaks, we’re going to go back to the drawing board.”

The principle: Comp plans for usage-based models must reward the behavior you want (driving adoption) without punishing customers for experimenting.

5. Success Metrics Without Input Metrics Is Just Noise

Tyler opened with a provocation: revenue leaders are obsessed with success metrics and “not rigorous enough on input metrics.”

The degenerate case is the CRO at SKO who tells everyone to “close more bigger deals” and expects that to translate into behavior change on Monday morning.

“What am I supposed to do with that? You need to actually understand the whole system and how it works — be able to instrument it, monitor it, talk about it, and importantly, set targets.”

But the opposite failure mode exists too. Work backward far enough and you land on “people coming to work” as your input metric. That doesn’t drive outcomes either.

The art is finding the inputs that matter — the engagement points in the customer lifecycle that actually predict success. For Intercom’s account management team, that might be quarterly business reviews with the right customer segments. For hunters, it might be demos or qualified pipeline.

Research on Goodhart’s Law — “when a measure becomes a target, it ceases to be a good measure” — explains why this is hard. Push pipeline targets to individual reps and they’ll stuff junk into Salesforce. Monitor the same metric at the manager level with quality guidance, and you get accountability without gaming.

“Not only is it the metrics, but it’s the level of your org that you’re going to inspect them at that matters a lot — where you can get the goodness without getting some of that bad behavior.”

Tyler’s heuristic: find the minimum inputs for maximum signal. Like hitting a tennis shot — if you’ve pulled your opponent off the court, you don’t need to hit it 100 mph. Just put it in play and win.

“What is it we need to do to really understand our business — to not find ourselves in a spot where, ‘Oh, if only everybody had done this thing, we would have been fine,’ and it turned out nobody was doing it?”

The discipline: Instrument the system, not just the scoreboard. And know which level of the org should see which metrics.

The Bottom Line

The AI transformation isn’t one transformation. It’s a cascade — product changes force pricing changes, pricing changes break comp plans, comp changes reshape post-sales motions, and all of it demands a different kind of RevOps team.

Tyler’s Intercom is a preview of where B2B go-to-market is heading. Not because every company will sell AI products, but because the tools available to revenue teams are changing what work looks like. The leaders who self-serve on data will outpace the ones who wait for dashboards. The CSMs who drive continuous commercial engagement will outlast the ones who “call us if you need anything.” The RevOps generalists who ask the right questions will replace the specialists who only knew how to execute.

The question isn’t whether your revenue engine needs to change. It’s whether you’re rebuilding it now — or waiting until the gap is too wide to close.

And the clock is already running.

If this breakdown was useful, I’d really appreciate a rating or review on Apple Podcasts or Spotify. It helps more revenue leaders find these conversations.

A LinkedIn post made the rounds claiming that Clay promises to eliminate headcount but actually adds $300K+ in costs when you factor in GTM Engineers, agency support, training, and credit overages.

Classic gotcha format. I took the bait and fired off a reply.

But even though the post was designed to generate controversy, it touched on a misconception so common among revenue leaders that it’s worth unpacking. The real issue isn’t whether Clay lives up to its marketing. The issue is that many of us are evaluating AI investments through the wrong lens entirely: cost reduction.

The Post Was Built on a False Premise

I checked Clay’s website. There’s no mention of reducing headcount anywhere. Their messaging is about GTM acceleration: “Turn your growth ideas into reality today.”

The original post constructed an elaborate argument against a claim the company never made. But this tells us something about how the market perceives AI tooling. We’ve been conditioned by years of vendor pitches about “doing more with less” that we unconsciously internalize cost reduction as the primary value proposition.

This is a mistake. The best revenue leaders I know don’t evaluate AI investments based on what they can cut. They evaluate them based on what they can unlock.

A 160-Year-Old Economic Principle That Should Change How You Think

In 1865, English economist William Stanley Jevons noticed something strange about coal consumption in Britain. As steam engines became more efficient and required less coal per unit of work, total coal consumption didn’t decrease. It went up. Dramatically.

Improved efficiency made steam power economically viable for factories that couldn’t afford it before. New industries emerged. The efficiency gains didn’t reduce demand for coal. They created demand.

This became known as Jevons’ Paradox.

When your BDRs become dramatically more efficient and can execute outreach at 3x the volume with the same effort, the rational response isn’t to cut headcount. The rational response is to hire more BDRs because the unit economics just got really attractive.

This is exactly what happened at Owner. Our business runs incredibly efficient on a per-head basis. That means we’ve actually hired far more salespeople than our peers, not fewer. In the last few months, we’ve gotten materially more efficient through AI tooling and process improvements.

Our response? We hired 27 XDRs in Q4.

The efficiency gains didn’t shrink our appetite for headcount. They expanded it.

The Right Question to Ask

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I’ve been saying for months that most AI use cases being pushed on LinkedIn are hype and BS. I’ve also been saying that you should be making AI a priority anyway. That paradox has frustrated a lot of revenue leaders who want to know what’s actually working.

So when Jason Lemkin told me he’s deployed 20+ AI agents across SaaStr’s go-to-market functions—and that they’re outperforming his human reps—I knew we needed to have a real conversation. No demos. No sponsored takes. Just raw lessons from someone who’s built portfolio companies like Salesloft, Pipedrive, Talkdesk, and Intercom.

What emerged was one of the most honest discussions about AI in GTM I’ve had. The takeaways challenged some of my own assumptions and reinforced others. Here’s what I walked away with.

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1. The Catalyst: When Human Reps Become the Bottleneck

Jason’s journey to AI agents didn’t start with a strategic initiative. It started with frustration.

“In May 2024, two of our high-paid sales reps—$150K to $200K plus—quit without notice going into SaaStr Annual,” Jason told me. “Amelia and I just looked at each other and said, ‘We’re done with this. We’re going all in on agents.’”

The decision wasn’t just about turnover. It was about chronic underperformance despite premium compensation. Jason described reps who wouldn’t follow up on LinkedIn job changes even when explicitly told to. Inbound leads sitting untouched. Basic blocking and tackling being ignored.

The lesson here isn’t about AI replacing humans. It’s about AI replacing mediocrity. When your highest-paid performers can’t execute fundamentals consistently, the calculus changes. Jason’s frustration mirrors what I hear from revenue leaders constantly—the gap between what we’re paying and what we’re getting has become untenable.

This connects to something I’ve observed at Owner: the best performers are getting exponentially better, while the middle of the pack struggles to keep up. AI is accelerating that divergence.

2. The Uncomfortable Truth: Mid-Pack Jobs Are in Terminal Decline

Jason didn’t mince words on this one: “Mid-pack jobs in GTM are in terminal decline.”

Here’s what he means. SaaStr now sends 70,000 hyper-personalized emails—compared to 7,000 that humans sent before. The AI-generated emails are better than what the humans produced. Not dramatically better, but consistently better. And they generated 15% of SaaStr London’s revenue.

“The agents are better than a mid-pack AE or SDR or BDR,” Jason explained. “They’re not better than your best performers. But that middle tier? They can’t compete.”

This has profound implications for compensation and career trajectory. Jason believes elite performers should earn 2-3X more—similar to how some OpenAI engineers earn millions. Traditional sales had no leverage mechanism; now it does.

The profile of who thrives in this new world looks different. It’s not the BDR who becomes an agent manager. It’s more like a growth PM or data scientist who becomes a GTM engineer. Jason recommends looking internally for “the nerdy SDR with a math degree who loves data.” External hires for “Senior GTM Engineer” roles are likely to disappoint because the role is too new to have an established talent pool.

At Owner, we’re seeing this play out. We’ve achieved 3X productivity improvement per AE and are hiring 25 people in 75 days—a perfect illustration of Jevon’s paradox. Higher productivity doesn’t mean fewer jobs; it means more investment in what’s working.

3. The Implementation Playbook: You Are the Agency (For Now)

Here’s where Jason’s advice diverged sharply from what most vendors are selling. When I asked about how to get started, he was emphatic: “Do it yourself. You are the agency for now.”

He means this literally. The CRO, CMO, or VP Sales needs to personally deploy the first agent. No delegating. No bringing in agencies. Twenty to thirty hours over 30 days of hands-on work.

“If you don’t roll up your sleeves in the age of AI GTM, you will become obsolete,” Jason warned.

The training process he described is surprisingly manual:

1. Point the agent at your database (or just your website initially)

2. Upload your perspectives and documentation

3. Agent generates 10-30 sample questions or emails

4. Review every single email for 1-2 hours daily

5. Correct hallucinations immediately (e.g., “Owner is for 100-chain restaurants” → “Owner scales but core is 1-5 locations with significant to-go business”)

6. Agents remember corrections via hive mind—they improve daily

The key insight: deployment failures are training failures, not product failures. Jason shared a story about Momentum (one of my favorite tools) that initially seemed broken. Turns out, a sales rep had never linked their Google account. When Jason showed them the link on a live Zoom, the rep reluctantly connected—revealing 30 days of zero activity. The rep quit that day.

“Shame on us,” Jason said. “Pre-Claude 4, products genuinely didn’t work. Post-Claude 4, all products are above the line. Failures are implementation failures.”

4. The Vendor Selection Framework: FDEs Over Brand Names

When I asked how to choose between the explosion of AI vendors, Jason offered a framework I haven’t heard elsewhere. He evaluates on a 2x2 matrix:

1. Quality of the vendor

2. Quality of the forward-deployed engineer (FDE) support

“Don’t sign a contract until you talk to your forward-deployed engineer,” he advised. “Agents require weeks of training before going live. Better to have a worse vendor with a great FDE than the best vendor with no support.”

His first agent was Artisan—not because they were “best,” but because they offered full deployment support. A competitor demanded $100K upfront with no deployment help. Jason walked.

For companies just starting, Jason recommends inbound AI as the lowest-hanging fruit. “There is no excuse on planet Earth for some 21-year-old SDR to qualify me whether I’m worth their time.” Tools like Qualified (bought yesterday by Salesforce!) handle instant answers, automatic scheduling, ICP filtering—24/7, any language.

The selection process:

1. Pick ONE tool solving a medium/high priority problem

2. Demo and research

3. Critical: Say “I want to talk to my FDE”—bypass the sales rep

4. Talk to the forward-deployed engineer before signing

5. Budget $50K-$100K for the first tool

Don’t be intimidated by the jargon. “Training” means uploading data and answering questions. “Ingestion” means data upload. “These are all SaaS apps,” Jason reminded me. “Just with an LLM hookup.”

5. Where We Actually Are: Bottom of the First Inning

Despite all the progress, Jason was remarkably humble about the current state. “We’re in the bottom of the first inning for innovation in AI and GTM. I don’t even think we’re in the second inning.”

The 70,000 emails SaaStr sends are better than human emails—but only a little better. True hyper-personalization still uses just 1-3 data points. Token costs remain high for comprehensive data synthesis.

Jason’s vision of what’s possible: “AI should send emails as good as Adam’s investor cold email”—the one that got Jason to invest in Owner. That email represented top 0.1% IQ with several hours of crafting. Future AI should pull every competitor, adjacent product, website visit, and 10-year interaction history, then synthesize the entire database in real time.

“AI is smarter than us now,” Jason said. “It should leverage the full hive mind.”

The products that achieve this vision will be massive winners. But we’re not there yet.

The Hard Truth About What This Means for You

I asked Jason what advice he’d give to sales professionals navigating this transition. His response was characteristically direct:

Be self-aware about your growth appetite. Want venture scale with triple-triple-double-double growth? Prepare for the hardest work of your life. Want lifestyle? Join a company growing 10-20% and burning $0. “Be fine with less upside.”

Step up or find a new role. Mid-pack performance equals obsolete. You must be 5-10X more productive than you were a year ago. Elite performers will earn 2-3X more.

Roll up your sleeves. Personally deploy at least one agent. This isn’t an agency game yet—maybe in 24 months. You can’t bring the 11 agencies from your last job.

When I reflected on my own experience, I realized I’m working as hard as I ever have—despite all the tailwinds at Owner. Jason echoed this: “I’m working the hardest I ever have.” The magical middle ground of 2020-2021 is gone. You could have had lifestyle, growth, and low hours. Now it’s either high intensity or slow-growth lifestyle companies.

What I’m Taking Away

This conversation reinforced something I’ve been thinking about for months: the gap between AI hype and AI reality is closing fast, but only for leaders willing to do the work.

The companies winning with AI aren’t the ones buying the flashiest tools or hiring the most agencies. They’re the ones where revenue leaders personally deployed the first agent, spent 30 days training it, and built institutional knowledge about what actually works.

At Owner, we’ve built 9 high-impact AI production use cases and achieved 3X productivity improvement per AE. But I’ll be honest—we’re still in the early innings too. The infrastructure we built in 3 weeks (that others said would take months) is just the foundation.

The question isn’t whether AI will transform GTM. It’s whether you’ll be the one doing the transforming—or the one being transformed.

If this resonated, I’d love a 5-star review on Apple Podcasts or Spotify. It helps us reach more revenue leaders navigating this same transition.

It’s planning season, which means a lot of you are in the middle of disagreements you can’t seem to resolve.

CMO wants one thing. CFO wants another. CPO is chasing a different priority entirely. And you’re stuck in the middle, trying to get resources for a plan that feels increasingly like a fantasy.

Here’s what I’ve learned from watching dozens of these situations unfold: The revenue leaders who win aren’t the ones with the best spreadsheets (that helps though). They’re the ones who’ve built enough trust and credibility to actually influence decisions.

This week I sat down with Allison Metcalfe, CRO at Zenoti, to unpack this exact challenge. Allison has one of the more unconventional paths to the CRO seat; she spent her first decade in customer success before moving into revenue leadership at LiveRamp, Demandbase (CRO), and Cloudinary (CRO).

When I asked around about her superpowers, the same theme emerged repeatedly: she’s exceptional at managing executive relationships.

In an era where CROs are increasingly becoming systems architects rather than quota-crushers, this skill might be the most important one we’re not talking about.

1. Everyone is already weighing in so you might as well let them

Early in her career, Allison attended a three-day workshop with Liz Wiseman, author of Multipliers, that fundamentally changed how she thinks about leadership.

Allison had just been promoted and was about to take over a distressed team at Jigsaw (Salesforce’s first acquisition). Her original plan was to fly to Spokane, present her vision, and tell everyone not to worry because she had a plan.

After the workshop, she ditched the entire presentation.

“I went up to Spokane and put everyone in a room with a whiteboard and a marker and facilitated a discussion. One of the biggest tenets of Multipliers is that everyone’s weighing in on what you’re doing as a leader, whether or not you’re inviting them to. They’re at the bar after your presentation, talking about your plan, what you don’t get, what they think is stupid. So you may as well give them the opportunity to weigh in and hear them out—because they’re doing it anyways.”

This isn’t just about being inclusive. There’s a psychological mechanism at work here called the endowment effect where people naturally value things more when they’ve invested time and effort into creating them.

When your team sees their language, their ideas, and their concerns reflected in your plan, they’re exponentially more likely to execute it with conviction. Our job isn’t to create pretty plans. It’s to drive behavior change. And behavior change starts with ownership.

There’s a 360-degree assessment in the Multipliers framework that asks a devastatingly simple question: On a scale of 1-10, how much of your intelligence do you use working for [your manager]? (I asked my People Business Partner to add this to our engagement survey structure immediately after I recorded with Allison).

If you work for what Wiseman calls a “diminisher,” you’re probably not using much of your brainpower because why bother thinking when someone’s just going to tell you what to do anyway? But if you work for a multiplier, you know you’ll be expected to bring answers, not just problems.

The question I’d challenge every leader to ask themselves: Are your people using 80% of their intelligence, or 40%?

2. Advocacy and inquiry: The two-part framework for productive disagreement

One of the most practical tools Allison shared is something her executive coach, Lesly Higgins, taught her: the framework of advocacy and inquiry.

Advocacy means showing your work. When you present a decision or recommendation, walk people through how you got there: “I started here, made this assumption, looked at this data, and ultimately landed here.”

Inquiry means asking others to do the same. Instead of assuming someone’s position is crazy, ask: “Can you help me understand how you got there?”

This sounds simple, but most executive teams are terrible at it. We jump to conclusions, build narratives about what our peers “really” think, and end up talking past each other for months.

The magic of this framework is that it creates space for productive disagreement without triggering defensive reactions. When you show your work, you give people specific points to push back on: “Actually, I don’t agree with assumption three—can we unpack that?”

That’s infinitely more useful than the vague objection you’d get otherwise.

A critical caveat: Allison was quick to point out that this doesn’t mean consensus-driven decision-making. That doesn’t scale.

“You have to be clear: this is not a democratic vote. I’m soliciting feedback, I’m curious, but ultimately it’s my decision. You’ve got to create space to hear people out without being beholden to the opinions of the entire group.”

3. The CFO relationship: Run toward the discomfort

If there’s one executive relationship that makes or breaks CROs, it’s the one with finance.

Allison’s approach is extremely powerful: lean in hard, even when it feels unnatural.

“I often find where I’ve been successful is by doing that which feels most unnatural. Run toward the discomfort and embrace it. I try to bring finance along with me wherever I go. I’ll invite my FP&A partner to leadership team meetings. I don’t want there to ever be a perception that I’m trying to hide anything.”

This goes beyond transparency. It’s building such deep partnership that your CFO could defend your plan to the board without you in the room. But here’s the part that many revenue leaders miss: this requires financial literacy on your side.

I’m often surprised when revenue leaders reach out for advice because their CFO is pushing back on something, and when I ask basic questions ”What’s the LTV:CAC of this channel?” they don’t know. How can you have credibility in that conversation if you don’t understand the financial language?

Allison framed trust using a formula I hadn’t heard before: Trust = Competence + Caring.

The caring part is about demonstrating that you genuinely care about the financial health of the business, not just your sales number and comp plan. The competence part is about being able to have substantive financial conversations through unpacking cells, explaining the numbers behind the numbers.

One more insight that’s worth noting: different CFOs care about different things. At one company, it was all about EBITDA. At another, sales and marketing as a percentage of revenue. At the next place? ARR per employee.

“I want to know what you care about, and I’ll optimize for that.”

4. How to navigate planning season logjams

With 2026 planning in full swing, a lot of leaders are stuck in exactly the kind of disagreement Allison and I discussed. Her framework for working through these impasses is practical and immediately applicable.

Guard Rails and Conditions for Ignition

When proposing new investments (which are always scary, especially if it’s something new), Allison structures the conversation around three zones:

• Green Zone: We’re comfortable. The investment is working as expected.

• Red Zone: Something’s off. We need to pause, pivot, and reevaluate.

• Ignition Conditions: It’s exceeding expectations. Time to double down.

This framing does two things. First, it shows you’re not being a cowboy—you’ve thought about what happens if this doesn’t work. Second, it creates shared ownership of the decision. You’re asking the CEO, CFO, and CPO to agree upfront: “If it hits this threshold, we keep going. If it hits this threshold, I’ll stop.”

Present Trade-offs, Not Ultimatums

The other critical move is articulating what doesn’t happen if you don’t get the investment.

“I do think we as CROs don’t have that conversation as well as we should. We’re talking a lot about ‘I need this to do this.’ But you also need to say: ‘If this is not here, this can’t happen. This is what’s at risk.’”

This isn’t about creating fear or manipulation. It’s about presenting complete options rather than demands.

And here’s the key: you can’t be attached to a specific outcome. The moment you’re pushing “my way or the highway,” you trigger psychological reactance—that automatic resistance people feel when their freedom of choice is threatened. Instead, come in with genuine options and let the trade-offs speak for themselves.

5. The Sasha Fierce Principle: Developing your Alter Ego

Toward the end of our conversation, Allison shared something unexpected on how she handles the emotional weight of executive feedback using what she calls the “Sasha Fierce principle.”

“When I first got more senior and was getting into tough conversations... the feedback gets harder and harder the more senior you get. No one really says ‘great job’ anymore. I read this thing about Beyoncé—someone asked, ‘You always say you’re so shy, but then you’re on stage.’ And she said, ‘That’s not Beyoncé. That’s Sasha Fierce.’ When she reads things about herself, she’s not reading about herself—she’s reading about this other alter ego.”

This resonated deeply with me. I’m actually reading Todd Herman’s book The Alter Ego Effect right now (he’s coming on the podcast soon). Herman built the Black Mamba alter ego with Kobe Bryant and has worked with athletes like Roger Federer and Cristiano Ronaldo on the same concept.

The idea is simple but powerful: develop a professional persona that is you but not you. It creates psychological distance from criticism and allows you to perform at a higher level in high-stakes situations.

Allison has fully embraced this—even naming her car Sasha Fierce.

“Develop a persona that’s the professional version of you. It helps you be a little bit tougher.”

What Separates Good CROs from Great Ones

I asked Allison what she thinks separates a good CRO from a truly great one. Her answer was deceptively simple:

“A great CRO really cares about the customer. A good CRO really only cares about sales efficiency. A great CRO cares just as much about CSAT as they do about win rate.”

This tracks with something I’ve been thinking about a lot lately. The best revenue leaders think holistically about enterprise value, not just their number and comp plan. When you only optimize for your own metrics, everything eventually falls apart.

The other piece of advice that stuck with me was about horizontal growth versus vertical growth—something Allison learned from, of all people, Sherry Redstone:

“If you’re ambitious and want to grow, too many people are focused on climbing up. But the great people really focus on going wide. The higher up you get, the more valuable it is to have a broader perspective on the business and not just your thing.”

This is the opposite of what most ambitious people do. We’re taught to specialize, to go deep, to become the expert in our domain. But at the executive level, that myopia becomes a liability.

The Bigger Picture

What I took from this conversation is that the CRO role is evolving. Personal sales acumen matters less than systems thinking, architectural capability, and—critically—the ability to build trust across the executive team.

Allison put it bluntly: “I don’t think I’m the best salesperson at Zenoti by far. My skills are in my architectural way of thinking and the fact that I always think about the entire customer journey.”

As AI continues to transform how revenue gets acquired, this shift is only going to accelerate. The CROs who win will be the ones who can think like revenue architects, collaborate genuinely with their peers, and navigate the complex human dynamics of executive leadership.

That’s less about closing deals and more about opening relationships.

If you enjoyed this breakdown, consider subscribing to the Revenue Leadership Podcast where I interview elite revenue leaders every week. And if you found this valuable, I’d appreciate a rating—it helps more leaders find the show.

I have a confession: Until a few years ago, I built my annual revenue plan the same way most of you probably still do.

Open the spreadsheet. Calculate total ARR target. Divide by average rep quota. Add 20% for inevitable attrition. Multiply by cost per head. Present to board. Get funding. Start hiring.

The model is seductively simple: **People x Quota = Revenue**. It’s what every SaaS playbook teaches. It’s what investors expect. And according to Jenny Dingus, SVP of Global Sales at Clio, it’s fundamentally broken.

Jenny joined me on the Revenue Leadership Podcast fresh off leading Clio through one of the most complex periods in the company’s history: launching multiple new products, orchestrating a billion-dollar acquisition of VLEX, and completely reimagining how a $5B legal tech platform thinks about revenue growth. What she shared challenges everything we’ve been taught about scaling revenue organizations.

Clio just raised a $500M Series G and serves over 150,000 legal professionals globally. Jenny’s spent the last few years proving that the traditional capacity planning model doesn’t work in vertical SaaS and building something better in its place.

Here are the five most valuable frameworks from our conversation.

1. The Integrated Planning Framework: Beyond Linear Capacity

Most revenue leaders are still building plans on a dangerous assumption: that hiring more sellers creates proportional revenue growth. Jenny’s watching this model collapse in real-time, especially in vertical markets.

“You need pipeline, you need market opportunity,” Jenny explained. “You can add headcount all the time but it’s not everything. Headcount alone and brute force is not a sustainable way.”

The problem goes beyond philosophy. Research from [SaaStr](https://www.saastr.com/) shows that rep productivity has declined 30% since 2020, meaning the historical relationship between headcount and output has fundamentally shifted. When productivity degrades faster than you can hire, you’re running on a treadmill that’s speeding up.

Why we still do it: “It’s easy on a spreadsheet,” Jenny said with a laugh. “You need a plan to stress test, but the functions at the center of the planning exercise aren’t operating.”

Jenny’s alternative treats headcount as one growth lever among many.

Jenny’s Integrated Planning Framework replaces the linear model with four parallel growth engines:

1. Pipeline Engineering

“What are all the channels?” Jenny asks. This means forensically understanding every source of qualified pipeline and how they interact. At Clio, this meant spending significant time collaborating with marketing to understand volume, velocity and conversion by channel.

2. Monetization & Pricing Strategy

“Balance market penetration and value capture,” Jenny noted. Clio launched multiple new products while simultaneously running “a big monetization effort.” The key: You can’t set quotas until you understand attach rates and pricing implications. Too many leaders treat pricing as static when it should be a quarterly conversation.

3. Market Expansion

This includes new segments (Clio just launched enterprise), new geographies, and crucially, knowing when to bake them into the plan. “Bake them in conservatively,” Jenny advised. They’re not planning for the full year on new products, just Q1. “This will give us the opportunity to collect enough data to plan for the rest of the year.”

4. Strategic Partnerships

Jenny means systematic channel development and ecosystem plays that create compounding returns, not vanity partnerships that look good in press releases.

The shift in thinking: You’re asking “What’s the optimal mix of growth levers, and what capacity do I need to service the demand they generate?” instead of “How many reps do I need?”

2. Confidence Intervals Replace Annual Commitments

Here’s where Jenny’s approach gets really interesting and really different from conventional wisdom.

For Clio’s new product launches, they’re not forecasting 12 months out. They’re planning in quarters with explicit confidence intervals.

“We’re not planning for the whole year right now, just planning for Q1,” Jenny explained. “Not in the habit of replanning. Not a lot of appetite to complain about targets.”

This approach borrows from Bayesian statistics and product management: update your priors as you learn. [Annie Duke writes about this](https://www.annieduke.com/) extensively in *Thinking in Bets*, noting that the best decision-makers constantly update their confidence based on new information rather than defending initial predictions.

At Clio, they meet multiple times per week to review early data from new product launches. “This informs enablement, marketing spend, resource allocation,” Jenny said. With new products specifically, “They’re watching it minute by minute and making adjustments.”

The sophistication here: They’re distinguishing between Business As Usual performance (where annual planning makes sense) and New/Emerging opportunities (where quarterly learning cycles matter more).

**Why this matters:** When you commit to rigid annual plans in uncertain environments, you create organizational antibodies against the very learning you need. Teams hide bad news. Managers sandbag. Everyone games the forecast instead of learning from reality.

3. The “Draw the Owl” Integration Playbook

When Clio decided to acquire VLEX mid-summer with plans to launch at ClioCloud Conference in mid-October, Jenny faced a systems integration challenge that would break most sales organizations.

She needed to build systems, enable sellers, integrate products, and create new go-to-market motions while running the existing business. Her team’s execution offers a masterclass in change management at speed.

The name comes from Clio’s company value: “Draw the Owl,” inspired by that old meme showing two steps: 1) Draw two circles, 2) Draw the rest of the owl. It’s about empowering teams to figure things out creatively rather than waiting for perfect instructions.

The Operating Structure:

Jenny broke the integration into 5 key workstreams, each with dedicated owners and resourced teams (some with 30-40 people):

• Monetization strategy

• People & org design

• Sales readiness

• Systems integration

• Go-to-market execution

Critical detail: They asked leaders to spend 70% of their time on integration. “We’re pausing everything and all their weight went into it,” Jenny said. No half measures. No “do this on the side.”

The Communication Cadence:

Most acquisitions fail on communication. Clio succeeded by treating messaging as a system:

1. Consistency at scale

“5 times, 5 different ways,” Jenny emphasized. This follows the Rule of 7 from marketing psychology: people need to hear something 7 times before it sticks.

2. More importantly than hearing it, they have to feel it

Jenny’s team didn’t just *tell* people about new products. They put sophisticated demo environments in sellers’ hands so they could experience the customer impact firsthand.

3. Weekly all-hands

Still running with dedicated teams. “Constantly reinforcing what they’re focused on. Sharing information in real time.”

The psychological principle: When organizational change creates uncertainty, human brains default to threat response. The antidote? More *predictable* information. Weekly cadences create rhythm. Rhythm creates safety. Safety enables performance.

4. Enablement as Continuous Loop, Not Launch Event

“Biggest learning: enablement doesn’t stop when you launch,” Jenny said. “That’s the starting point.”

Most companies treat product launches like military campaigns: build up to the big day, then move on. Jenny’s team built a continuous learning system instead.

The Feedback Architecture:

• Centralized Slack channels where sellers ask questions without judgment

• Weekly synthesis of themes and questions

• Cross-functional tiger team (product, PMM, enablement) reviews weekly

• New assets and training created based on actual field friction

The Enablement Toolkit:

• Newsletter of the Week - Curated weekly digest of the most important information for sellers

• Meeting in a Box - Pre-packaged slides and exercises managers can use in team meetings

• AI Sales Sim - Custom bots for practice (right place, right time for their launch)

This mirrors the concept of deliberate practice from Anders Ericsson’s research. Learning doesn’t come from exposure. It comes from repeated retrieval and application with feedback loops.

**What makes this different:** Most organizations have “knowledge bases” that become graveyards. Jenny’s system is *active*. It pulls questions from the field, synthesizes patterns, and pushes refined answers back out weekly. Think of it as a learning flywheel rather than a library.

5. Humility as Competitive Advantage

In our quickfire segment, I asked Jenny what separates good CROs from exceptional ones. Her answer surprised me:

“Humility is a really important quality. Especially right now with everything changing so much. Stay curious and humble and create space to learn not only from market trends, but from our people and from our customers.”

Jenny’s articulating something deeper about decision-making in uncertainty. This connects to Julia Galef’s concept of “scout mindset” versus “soldier mindset.” Soldiers defend positions. Scouts explore terrain. When you’re navigating unprecedented market conditions (AI disruption, efficiency expectations, vertical market constraints), scout mindset wins.

Jenny’s specific advice to new leaders:

“Irrespective of what somebody’s title is, their experience, their tenure, don’t assume they have all the answers. Leaders are looking for the experts in the field to help them form an opinion. If you happen to be in the room for a conversation, you’re there for a reason. Make sure that you’re contributing in a meaningful way.”

This connects to psychological safety research from Amy Edmondson at Harvard. Teams with higher psychological safety make better decisions because information flows more freely. They don’t just feel better.

The counterintuitive insight: Most executives think certainty projects confidence. Jenny argues the opposite. Intellectual humility creates more space for the organization to surface problems early when they’re still solvable.

The Happiness Advantage: Why Optimism Compounds

When I asked Jenny about the best thing she’d read recently, she immediately cited The Happiness Advantage* by Shawn Achor: “His thesis is that happiness fuels success and not the other way around.”

Achor’s research at Harvard showed that positive brains have a 31% higher productivity than negative, neutral, or stressed brains. Sales improve 37%. Accuracy on tasks improves 19%.

The mechanism: When you’re in a positive state, your brain releases dopamine. This doesn’t just make you feel good. It activates the learning centers in your brain. You literally get smarter when you’re happy.

Why this matters for revenue leaders: You can’t out-process your way to exceptional performance if your team is operating from a stressed, threat-response state. The “grind culture” so prevalent in sales doesn’t just burn people out. It makes them objectively worse at complex problem-solving.

I wanted to share an important insight that has been rattling around the back of my brain for a while but I was only able to bring definition to quite recently.

I think one of the biggest reasons many GTM AI implementations fail is because they stack multiple layers of generative outputs on top of one another, which creates compounding lossiness.

We’ve all been there. You see a really slick demo of an AI product, rally the resources to run a proof of concept then the results of this tool are cool but nowhere near good enough to put in front of reps or customers. The deal strategy that’s suggested has some solid suggestions but 40% is a little off or too obvious or not quite as good as your reps would do. Or the emails that are generated are plentiful and decently personalized but still not good enough to push to tens of thousands of prospects. There’s SO much potential there and it feels like with a few incremental improvements, it could work but you just can’t get it across that imaginary line.

Lossiness Explained

Think about what happens when you photocopy a document, then photocopy that copy, then photocopy that one. Each generation degrades slightly—text gets fuzzier, details blur, contrast shifts. By the fifth or sixth copy, you’re looking at something barely readable.

This is lossiness. It’s the degradation of information quality that occurs each time data gets transformed, compressed, or regenerated.

In AI systems, lossiness happens because generative models don’t reproduce information, they approximate it. When an LLM generates text, it’s making probabilistic predictions about what comes next based on patterns it learned during training. It’s not retrieving perfect information; it’s creating a best guess. And every best guess introduces small deviations from perfect accuracy.

Here’s what happens technically: An AI model takes input data, processes it through multiple layers of neural networks, and outputs something new (tokens). An LLM is a token prediction machine and knowledge is represented as those tokens. At each layer, the model is lossy; it compresses information through tokenization, makes approximations during generation, and introduces uncertainty. The model might be 95% accurate at each step, which sounds great. But here’s the problem: when you chain these steps together, the losses compound exponentially.

Stacking lossiness

Here’s where it gets dangerous for GTM teams.

Read more

Enjoy!

Cannonball GTM

The Five P's Framework: How to Pick Winning AI Use Cases with Kyle Norton

Kyle Norton has made exactly zero bad AI bets at Owner.com…

Read more

7 months ago · 2 likes · 1 comment · Cannonball GTM

Here’s a counterintuitive truth that will make most CMOs uncomfortable: the mandate for marketing leadership hasn’t changed in the AI era—but the resource equation has become so dramatically different that clinging to old operating models is professional suicide.

I recently sat down with Kyle Coleman, Global VP Marketing at ClickUp, whose 16-year journey from SDR to senior marketing leadership spans some of the most transformative companies in B2B SaaS. Kyle spent 6 years at Looker through its acquisition by Google, 5 years at Clari where he became CMO, a stint at Copy.ai as CMO, and now ClickUp. What emerged from our conversation wasn’t just another “AI will change everything” platitude. Instead, Kyle articulated something far more nuanced: a complete reimagining of how marketing executives allocate their scarcest resource: human cognition.

The data backs up what Kyle has experienced firsthand. Research shows that 78% of organizations now use AI in at least one function, yet only 1% describe their AI rollouts as “mature.” More telling: companies that successfully integrate AI into marketing workflows achieve 60% greater revenue growth than peers, but 70-85% of AI projects fail to meet desired ROI. The difference? It’s not the technology. It’s the operating philosophy.

Kyle’s perspective matters because he’s lived through the evolution. At Looker, he witnessed the shift from traditional outbound to problem-oriented positioning. At Clari, he scaled a category design playbook. At Copy.ai, he experimented with AI-native workflows. Now at ClickUp, he’s synthesizing these lessons into what might be the clearest articulation yet of how marketing leadership must evolve—not by doing different work, but by fundamentally restructuring how work gets done.

The AI Resource Paradox: Why Your Team Hasn’t Gotten Smaller

Most revenue leaders assume AI adoption follows a simple equation: automation = fewer people. The research reveals something far more interesting.

Kyle observed what academic studies now confirm: 84% of marketing teams report no decline in team size despite 83% reporting increased productivity from AI. This apparent paradox resolves when you understand what’s actually happening. Teams aren’t shrinking—they’re undergoing what Kyle calls “cognitive reallocation.”

Here’s the pattern Kyle has observed across three platform transformations: organizations that treat AI as a cost-cutting tool achieve marginal gains at best. Those that treat it as a cognition-multiplier achieve what BCG research quantifies as 1.5x higher revenue growth and 60% greater competitive advantage.

The distinction maps to what researchers call the 70-20-10 principle. AI leaders invest 70% of their resources in people and processes, 20% in technology and data, and only 10% in algorithms. Yet most organizations do the inverse—obsessing over which AI tools to buy while neglecting the harder work of fundamentally redesigning how marketers spend their mental energy.

Kyle’s framework for thinking about this centers on a simple question: “What percentage of your team’s cognitive capacity is spent on work that AI could handle versus work that only humans can do?” At most organizations, the answer is uncomfortable. Research suggests 30-50% of marketing time goes to low-value work—content reformatting, meeting scheduling, report generation, data entry.

The teams that win aren’t eliminating this work. They’re codifying it, then offloading it to AI, which frees senior talent to focus on what Kyle calls “the only defensible marketing activities”: understanding customer psychology, designing category narratives, building authentic relationships, and making strategic bets that require human judgment.

Process Documentation as Competitive Moat: The Counter-Intuitive Path to Velocity

Here’s where conventional wisdom gets completely upended. Most marketing leaders view process documentation as bureaucratic overhead—the thing you do when you’re mature and slow. Kyle argues the opposite: rigorous process codification is what enables speed.

This reflects research showing that centralized knowledge bases enhance productivity by up to 30%, and organizations with structured documentation reduce incident resolution times by 30% while experiencing 20% overall productivity boosts. But the real insight Kyle surfaced goes deeper than efficiency metrics.

He references what ClickUp has identified as “work sprawl”—the proliferation of tools, documents, and processes that fragment organizational knowledge. The average marketing team now uses 91 different cloud services. Each tool contains trapped knowledge. Each handoff between systems creates friction. Each context switch burns cognitive capacity.

Kyle’s approach at ClickUp centers on what he calls “process AI-fication”—a three-step framework:

First, document everything. Not in the sense of creating SOPs that sit in dusty folders, but building living, executable process maps that capture decision trees, exception handling, and quality criteria. This feels slow initially. It is slow. But it’s the necessary foundation.

Second, identify the 80/20. Which processes are truly high-leverage and require human judgment? Which are repeatable, rules-based work that could be systematized? Kyle cites Pareto’s principle: 80% of marketing work follows predictable patterns. That 80% becomes your automation target.

Third, build content waterfalls. This is where the magic happens. Kyle described how they transform a single piece of trapped knowledge—say, a sales call recording or a strategy document—into a cascade of formats using AI. The same insights become blog posts, social content, sales enablement materials, customer education, and internal knowledge base articles.

What’s fascinating is that the constraint isn’t AI capability—it’s having clear enough processes that AI knows what to generate. Organizations without documented workflows can’t scale AI effectively because there’s no “recipe” to automate. Those with rigorous processes discover that AI can execute the recipe faster and more consistently than humans ever could.

The result: marketing teams spend less time on production and more time on what Kyle calls “strategic problem-solving”—the work of understanding customer psychology, identifying market shifts, and designing narrative strategies that only humans can do.

Owning the Problem: Category Design in an AI-Saturated Market

Since recording the episode, I’ve already referenced Kyle’s framework for category ownership multiple times. It’s one of the sharpest and most useful insights I’ve heard in a while and his perspective on category design carries weight because he’s executed it at scale. At Clari, he helped establish “Revenue Operations” as a distinct category. At ClickUp, he’s evangelizing “work convergence” as the antidote to “work sprawl”.

The fundamental insight aligns with what Christopher Lockhead and Category Pirates have researched extensively: category kings capture 76% of total market capitalization in their categories, leaving competitors to fight over 24%. But Kyle adds a crucial practitioner insight that academic research misses: problem evangelization has gotten both harder and more important in the AI era.

Here’s why. In a world where AI enables hyper-personalized outreach at scale, every buyer is drowning in solution pitches. LinkedIn is saturated with vendors claiming their product is “better.” But very few companies are prosecuting the problem itself; making the problem bigger than the solution, educating buyers on why existing approaches are fundamentally broken, and owning the language customers use to describe their pain.

Kyle references what he learned from observing Clari’s success: the companies that win categories aren’t necessarily those with the best product; they’re the ones that best articulate the problem. Salesforce didn’t win CRM by building better software than Siebel. They won by prosecuting the problem of “expensive, complex, on-premise software” and creating new language: “No Software.” HubSpot didn’t beat Eloqua feature-for-feature. They evangelized the problem of “interruptive outbound marketing” and created new vocabulary: “Inbound Marketing.”

At ClickUp, Kyle is applying this playbook to what he calls the “work sprawl crisis.” The problem isn’t that existing project management tools are bad, it’s that organizations are drowning in disconnected tools. The average knowledge worker now switches between 10 applications per hour. This fragments context, creates information silos, and makes it impossible to maintain the kind of unified process documentation that enables AI-fication.

The strategic move isn’t positioning ClickUp as “better Asana” or “better Monday.com.” It’s evangelizing convergence as a fundamental paradigm shift and positioning ‘tool proliferation’ as the underlying disease that’s killing organizational productivity.

What makes this approach powerful is what researchers call “cognitive anchoring.” When ClickUp educates buyers on work sprawl first, then introduces convergence as the solution, they’re not competing on feature checklists. They’re competing on problem definition. And the research is clear: the company that best frames the problem often defines and dominates the category.

Kyle emphasized that this requires missionary rather than mercenary thinking. You can’t fake problem evangelization. It requires genuine belief that you’re solving something important, combined with the patience to educate markets before monetizing them. Category design is a 6-10 year play, not a quarterly optimization.

The Change Management Reality: Why 85% of AI Deployments Fail

Here’s where Kyle’s experience becomes particularly valuable. He’s not just theorizing about AI transformation; he’s leading it in real-time across a global marketing organization.

The statistics are sobering: 70-85% of GenAI projects fail to meet ROI expectations. 95% of pilots fail to reach meaningful P&L impact. Yet 78% of organizations are deploying AI anyway, often without clear change management frameworks.

Kyle’s diagnosis aligns with what RAND Corporation research identifies as the root cause: 70% of AI implementation challenges are people and process issues, not technical problems. Organizations obsess over which tools to buy while neglecting the harder work of creating accountability and exposure to new possibilities.

Kyle described ClickUp’s approach using what I’d call the “exposure-accountability flywheel“:

First, create exposure to what’s possible. This isn’t about mandating tools. It’s about showing teams concrete examples of how AI transforms their specific workflows. Kyle emphasized that generic AI training fails because it’s too abstract. What works is demonstrating to a content marketer how AI can transform a single blog post into 15 different formats in 10 minutes, or showing a demand gen lead how AI can analyze 1,000 sales calls to surface the most effective messaging patterns.

Second, establish clear accountability. This is where most organizations fail. They deploy AI tools but don’t update performance metrics, don’t assign ownership, and don’t track outcomes. Kyle’s teams have explicit KPIs around AI utilization—not as surveillance, but as progress tracking. If someone isn’t using AI for appropriate tasks, the question isn’t “why are you being lazy?” but “what’s blocking you from adopting this?”

Third, reward the behavior you want to see. This goes beyond compensation. It’s about public recognition, career advancement tied to innovation adoption, and most importantly, protecting time for learning. Kyle mentioned that they explicitly budget “research spikes”—periods where team members can experiment with new AI capabilities without pressure for immediate ROI.

The research on change management frameworks proves this approach. Organizations following structured methodologies like Kotter’s 8-Step Model or Prosci’s ADKAR framework achieve significantly higher success rates. But Kyle adds a practitioner insight: change management for AI requires different emphasis than traditional technology adoption.

Why? Because AI fundamentally changes the nature of work in ways that previous technologies didn’t. Excel augmented human calculation and AI challenges whether humans should be doing certain cognitive tasks at all. This triggers existential questions about role identity and value contribution that pure process training can’t address.

Kyle emphasized the importance of reframing AI adoption from “your job is being eliminated” to “your job is being elevated.” The marketers who embrace AI aren’t replaced—they’re freed to focus on strategy, creativity, and relationship-building that machines can’t replicate. But this reframing requires ongoing communication, tangible examples, and psychological safety to experiment and fail.

The organizations that get this right achieve what the data shows: 60% higher revenue growth, 50% faster campaign turnarounds, and critically, higher employee satisfaction because people spend time on meaningful work rather than administrative drudgery.

The Blurring Lines: How GTM Functions Are Converging

Kyle’s observation about blurred lines between marketing, sales, and product reflects a broader trend the research confirms: we’re witnessing the death of siloed revenue functions.

The data is striking. Companies with aligned sales and marketing teams see 24% faster revenue growth and 27% faster profit growth over three years. Yet 65% of marketing and sales professionals struggle with leadership misalignment. The gap represents massive competitive advantage for organizations that figure it out.

Kyle described how this plays out practically at ClickUp. Marketing no longer stops at MQL generation. Sales no longer owns the entire customer journey. Product teams aren’t isolated in roadmap planning. Instead, they operate as unified revenue teams with shared metrics around pipeline velocity, conversion rates, and customer lifetime value.

This echoes what the research on Revenue Operations shows. Organizations adopting integrated RevOps models grow revenue 19% faster and are 15% more profitable than peers. But the transition isn’t easy. It requires fundamental restructuring of how work gets done.

Kyle’s framework for thinking about this centers on what he calls “outcome ownership vs. activity ownership.” Traditional structures assign activities: marketing generates leads, sales converts them, customer success retains them, product builds features. Modern structures assign outcomes: the entire GTM team owns revenue growth, retention rates, and customer satisfaction. The question shifts from “did you complete your activity?” to “did we achieve the outcome?”

This requires several structural changes Kyle has implemented:

Shared data platforms. No more separate marketing automation, CRM, and product analytics systems that don’t talk to each other. Unified customer data that everyone accesses and contributes to.

Cross-functional planning cycles. Rather than marketing planning separately from sales, they plan together with product input, aligned around customer segments and revenue goals.

Blended teams. Product marketers who spend time with sales. Sales engineers who contribute to content. Customer success insights feeding product roadmap. The boundaries become porous.

Unified metrics. Everyone measured on contribution to pipeline velocity, not siloed metrics like MQLs or sales activities or feature releases.

What’s fascinating is that AI enables this convergence in ways that weren’t practical before. When marketing can instantly analyze sales call transcripts to optimize messaging, when product can see which features correlate with expansion revenue, when sales can access real-time product usage data—the artificial boundaries between functions dissolve.

Kyle emphasized that this doesn’t mean everyone does everything. Specialization still matters. But the specialization becomes “depth in a domain” rather than “ownership of a silo.” A demand gen specialist still has deep expertise in campaign mechanics, but they’re working toward shared revenue goals alongside sales operations, product marketing, and customer success.

Leadership Philosophy: Running Toward the Fire

Kyle’s approach to leadership carries lessons beyond marketing specifically. He described his philosophy as “pushing people and running toward fires“—which initially sounds like typical executive platitudes but reveals deeper wisdom on examination.

The research on high-performing teams shows that psychological safety combined with high standards creates optimal performance. But Kyle adds a crucial nuance: in periods of rapid transformation, the role of leadership isn’t to shield teams from change; it’s to accelerate their ability to navigate it.

“Pushing people” in Kyle’s framework doesn’t mean unrealistic demands or burnout culture. It means holding high standards while providing the support to meet them. It means expecting AI adoption, rigorous process documentation, and category-level thinking then removing obstacles that prevent teams from executing.

“Running toward fires” means the opposite of what most executives do when crises emerge. Rather than delegating problems downward, Kyle described actively engaging with the hardest challenges the organization faces. More than heroic individual contributions, it’s modeling the behavior of confronting difficulty rather than avoiding it.

This connects to what researchers call “transformational leadership”—the kind that inspires teams to exceed their own expectations. But Kyle grounds it in practical behaviors: being first to experiment with new AI tools, personally testing process improvements before rolling them out, transparently sharing what’s not working alongside wins.

The outcome Kyle described aligns with the change management research: teams that see leadership actively engaging with transformation (rather than mandating it from a distance) adopt changes 2x faster and with less resistance.

The Content Waterfall: From Trapped Knowledge to Scalable Assets

Kyle’s most tactical insight centers on what he calls “content waterfalls”—a systematic approach to transforming trapped knowledge into scalable marketing assets using AI.

Here’s the problem most organizations face: valuable insights exist in sales call recordings, strategy documents, customer conversations, and team members’ heads. But extracting and scaling this knowledge requires manual effort that doesn’t happen consistently. The result: knowledge remains trapped, underutilized, and eventually lost.

Kyle’s content waterfall framework has three layers:

Layer 1: Capture. Record everything—sales calls, strategy sessions, customer interviews, brainstorming meetings. The key is making capture automatic and low-friction. Tools like Grain, Otter, or Fathom handle transcription and key moment extraction.

Layer 2: Extract. Use AI to identify core insights, frameworks, and narratives from raw transcripts. This isn’t just summarization—it’s pattern recognition across multiple conversations to surface themes, objections, messaging that resonates, and competitive intelligence.

Layer 3: Generate. Transform extracted insights into multiple formats—blog posts, social content, sales enablement scripts, customer education materials, internal knowledge base articles, presentation decks. The same insight becomes 10-15 different assets optimized for specific channels and audiences.

What makes this powerful is what Kyle calls “insight leverage“—getting maximum value from each piece of captured knowledge. A single sales call with a customer describing their “work sprawl” problem might become:

• A LinkedIn post about the hidden costs of tool proliferation

• A blog post analyzing the productivity impact of context switching

• A sales email using the customer’s exact language to describe the problem

• A case study (with permission) showing the challenge and solution

• An internal knowledge base entry for how to handle this objection

• A slide in the next board presentation about market insights

The research backs this up. Organizations with robust knowledge management practices see 20-30% productivity improvements. But Kyle’s insight goes beyond efficiency: content waterfalls enable consistent problem evangelization at scale.

When you’re systematically capturing how customers describe their problems and using AI to generate problem-oriented content across all channels, you’re not just creating marketing materials—you’re establishing the language and narrative of the category itself.

The Mandate Hasn’t Changed—The Operating Model Must

Kyle’s ultimate insight returns to where we started: the fundamental mandate for marketing leadership remains exactly the same—drive revenue growth through customer acquisition and expansion—but the resource equation that enables this has been completely transformed.

The CMOs and VPs of Marketing who will succeed in the next decade aren’t those with the biggest teams or budgets. They’re those who master the art of cognitive reallocation—freeing human talent from automatable work to focus on the irreducibly human tasks of understanding psychology, designing narratives, building relationships, and making strategic bets.

This requires several simultaneous transformations:

Operational: Rigorous process documentation and AI-fication of repeatable work
Strategic: Shifting from solution marketing to problem evangelization and category design
Organizational: Breaking down silos between marketing, sales, and product into unified revenue teams
Cultural: Leading change management that creates exposure and accountability for AI adoption
Personal: Modeling the behavior of running toward complexity rather than away from it

The data shows the stakes clearly. Organizations that execute these transformations achieve 60% higher revenue growth, 2x faster market adaptation, and sustainable competitive advantage. Those that don’t face the prospect of fighting for the remaining 24% of market share while AI-enabled competitors capture the lion’s share.

Kyle Coleman’s journey from SDR to marketing leader at some of B2B SaaS’s most successful companies has given him pattern recognition most marketers lack. He’s seen what works when companies transform from single products to platforms, from reactive to category-defining, from siloed to integrated.

The lesson isn’t that every organization should copy ClickUp’s or Clari’s playbook exactly. It’s that the fundamental operating model for marketing leadership must evolve from managing headcount and budgets to orchestrating the optimal balance of human cognition and AI capability.

In Kyle’s words: “The mandate is the same. The resources are different. And if you keep trying to execute a 2020 playbook with 2025 capabilities, you’re not going to survive.”

The future belongs to revenue leaders who recognize that the scarcest resource isn’t budget or tooling—it’s human attention and strategic judgment. The organizations that win will be those that ruthlessly protect this scarce resource by offloading everything else to the machines designed to handle it.

That’s not a technology transformation. That’s a leadership transformation.

And the clock is already running.

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Programming note: I’m sure you’ve all noticed that my rate of writing has gone down over the last few months and we’ve missed some weeks with new episodes. To be honest, this summer wasn’t an easy one for me with my dad suddenly passing, a move from Toronto to the Bay area and a few other family emergencies thrown in for good measure. I’m back on my feet now and excited to have more time for the pod and writing. I have some big ideas in my notepad that I’m excited to share and I’ll be publishing a few presentations (GTM25, Vertical Software Summit, etc) to you guys in the next month. Sorry for the intermittent publishing and I’m excited to get back engaged with this amazing community. I love hearing from you guys and I missed all of the thoughtful responses and opinions so keep those coming!

Now onto last week’s episode (8 days late 😬)!

This week's episode is a special one because I interviewed my VP of RevOps, Steve Dinner, who I've worked with hip to hip for almost nine years now. A bunch of people have asked me to do this episode over the last year, but I avoided it because it seemed like shameless Owner self-promotion. Shoutout to Jessica Ashar for the persuasive nudge!

Many revenue leaders think their RevOps function exists to manage technology and enforce process compliance. They hire for technical expertise, optimize for system efficiency, and wonder why their sales teams treat RevOps like the enemy.

This conventional approach misses a crucial psychological element: the moment you start treating reps like metrics on a dashboard rather than humans trying to do their jobs, you've already lost. Your team will live down to those expectations. The validation fields multiply, the friction increases, and suddenly your RevOps team becomes what Steve Dinner calls "the no team" and is completely disconnected from the reality of the rep experience.

Steve Dinner, VP of RevOps at Owner.com, has taken a radically different approach. Starting as a BDR manager who happened to think in systems, he's built a RevOps function that operates from a core belief: people inherently want to do well. Give them the right workflows, reduce cognitive load, and they'll naturally do the revenue-generating activities you want.

The results speak for themselves. Owner has scaled from $5M to $50M+ ARR since Steve’s arrival with a RevOps team that's seen as a strategic partner rather than a roadblock. In our conversation, Steve unpacked the philosophy and frameworks that make this possible.

We explored why his monthly ride-alongs sitting beside reps making cold calls are more valuable than any dashboard, how an Agile transformation turned his team from reactive firefighters into strategic operators, and why the best RevOps leaders might not be the most technical ones. Steve also shared the exact framework he uses to avoid becoming either a "yes team" that takes every request or a "no team" that blocks progress and revealed what CROs consistently get wrong when hiring their first RevOps leader. I hope that more revenue and RevOps leaders in building infrastructure that amplifies human capability rather than constraining it.

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1. The Empathy Paradox: Why Former Reps Make Better RevOps Leaders

Here's what most companies get wrong: they hire RevOps leaders for technical proficiency and wonder why there's a disconnect with the sales team. Steve's path was inverted - he was a rep who discovered he thought programmatically.

"I can't imagine how hard it would be to do what I do well without knowing really what it means to be staring at the dial button," Steve told me. This isn't just about understanding the sales process intellectually. It's about visceral memory - the anxiety before a cold call, the cognitive load of context switching, the frustration when systems get in your way.

The practical application: When Steve does ride-alongs (which he does monthly), he's not just observing. He's reconnecting with that emotional reality. "Even just sitting down and looking at the call button... registering the feelings and emotions that you have going through your head. It's enough to rekindle the empathy for how challenging the job is."

This empathy translates into design philosophy. Instead of asking "how do we force compliance?" Steve asks "how do we make the right action the easiest action?" It's behavioral economics applied to revenue operations - what Thaler and Sunstein call "nudge theory" in their seminal work.

2. Trust as Architecture: Building Systems for Smart People

Steve shared a story that crystallized his entire philosophy. Early in his career, he created a simple dashboard showing what percentage of calls went to tier-one accounts. No rules, no enforcement, just visibility.

Day one: 40-45% of calls to tier-one accounts. Day two: 70%. Day three: 80%.

"People inherently, especially if you're hiring well for the correct roles... they want to do well," Steve explained. This runs counter to what Douglas McGregor called "Theory X" management - the assumption that employees are inherently lazy and need constant supervision. Steve operates from Theory Y: people are self-motivated and thrive with autonomy.

The framework in practice:

• Make the golden path the lowest friction path - Use tools like Momentum to pull information automatically rather than forcing manual entry

• Add friction only where absolutely necessary - Reserve validation fields for true deal-breakers

• Provide visibility, not surveillance - Show reps their metrics in context of success, not punishment

As Steve puts it: "If you give them the right workflow and it actually solves for the challenge that prevents them from doing the next best action... you're going to get the result you're looking for."

3. The Agile Transformation: From Ad Hoc to Systematic Excellence

About a year ago, Steve's team underwent an Agile transformation that fundamentally changed how they operate. This wasn't about following a textbook - it was about creating what he calls "turning variables into constants."

The framework involves:

• Product Owners for each function (Marketing, Sales, Launch, CS)

• Two-week sprints with clear definitions of "ready" and "done"

• Systematic retrospectives that generate 5-15 improvements each cycle

• A working contract that defines exactly how the team interfaces with the business

"Without that, it's super easy for things to fall through the cracks," Steve noted. But more importantly, it gives RevOps the ability to say "here's what we're working on, here's when you can expect it, and here's what needs to wait."

This addresses what researchers at MIT's Center for Collective Intelligence call "collaborative intelligence" - the ability of groups to work together effectively. By creating clear processes and communication channels, Steve's team can handle multiple strategic initiatives simultaneously rather than lurching from fire to fire.

4. The Yes/No Trap: Finding the Middle Path

RevOps teams typically fall into one of two dysfunctional patterns. They either become the "no team" (overwhelmed, defensive, disconnected) or the "yes team" (people-pleasing ticket-takers who never address root causes).

Steve's approach transcends this binary through what he calls "collaborative solution design":

Instead of saying no: "Let me understand what you're trying to achieve and what happens if it doesn't get done."

Instead of saying yes to everything: "Here's how this fits into our broader priorities. If we do this, here's what we're trading off."

This requires what Amy Edmondson calls "psychological safety" - the belief that you won't be punished for disagreement or asking questions. Steve builds this by being radically transparent about trade-offs and inviting stakeholders into the prioritization process.

"Every individual contributor, you have to be able to trust the people around you... they're inherently reasonable," Steve explained. "If you're hiring well, these are people who are focused on the company outcome."

5. The CRO-RevOps Mind Meld: Why Philosophical Alignment Matters More Than Technical Skills

When I asked Steve what CROs should look for in a RevOps leader, his answer surprised me: "You have to define the constraints that your business has. It's going to tell you exactly what kind of leader you need."

For Owner, operating in high-velocity SMB with hundreds of thousands of accounts means RevOps needs to be about workflow optimization and business strategy. The data science complexity lives in a separate function. In an enterprise environment with long sales cycles, the requirements would be completely different.

The hiring framework:

1. Define your constraints - What kind of business are you? What problems need solving?

2. Look for philosophical alignment - Can this person think like you think? Can they make decisions in your absence?

3. Prioritize revenue leadership capability over technical prowess - "There are ways to fill technical gaps. There's not really an easy way to fill a revenue leadership capacity gap."

4. Test for empathy - Have they been in the trenches? Do they understand the human side of sales?

As Steve puts it: "If you can't see this person as somebody who you're going to give a seat at the table... then you're still looking."

The Owner RevOps Playbook: Bringing It All Together

What makes Steve's approach revolutionary isn't any single innovation - it's the philosophical coherence of the entire system. Every decision flows from the core belief that people want to succeed and systems should enable rather than constrain them.

The practical implications cascade through everything:

• Monthly ride-alongs maintain empathy and catch friction early

• Agile processes create predictability without rigidity

• Philosophical alignment enables distributed decision-making

• Trust-based design reduces cognitive load and increases adoption

This isn't just feel-good management theory. It's grounded in decades of research on human motivation, from Herzberg's Two-Factor Theory to Deci and Ryan's Self-Determination Theory. When you treat people as competent agents rather than compliance problems, they rise to meet expectations.

Additional Considerations

Related frameworks to explore:

• OODA Loop (Boyd): Steve's rapid iteration cycles mirror this military decision-making framework

• Cynefin Framework (Snowden): Helps categorize problems as simple, complicated, complex, or chaotic - useful for RevOps prioritization

• Theory of Constraints (Goldratt): Steve's focus on removing bottlenecks aligns with this operations management approach

Further reading:

• Working Backwards by Colin Bryar and Bill Carr - Amazon's operational excellence playbook

• The Hour Between Dog and Wolf by John Coates - neuroscience of performance under pressure

• Thinking in Systems by Donella Meadows - foundational systems thinking