Weekly(?) AI Digest

Sharing a collection of stuff I read and listened to this week on AI

Welcome to my (maybe) weekly digest of AI stuff. I planned to send my team a list of good AI content each week so I figured I would just open source it to all y’all.

I’m absolutely slammed these days so this may or may not be weekly. We’ll see🤷

Here are some recent ruminations about GTM AI and you’ll find the reading/listening links below.

“Where do I start with AI?”

A perfect representation of the possibilities and limitations of AI 😆

Revenue leaders and founders have been asking me one question non-stop: “Where do I start with AI?” My default answer was “I have no idea. No one does. Just start learning what you can and figure it out.”

That answer was, obviously, not that helpful.

I’m starting to form an initial opinion that could be more useful with the major caveat that we’re all just figuring stuff out so this is an informed hypothesis at best.

Although people are asking “Where do I start with AI?" I think the question is really more like “How do I start transforming my organization to become AI-native?”

I think there are two initial pillars:

1. AI transformation strategy

2. Data foundations

Most leaders are skipping these basics and jumping straight into use cases and random experiments, which is where a lot of the frustration is coming from. Without a solid data foundation, AI isn’t all that useful because ‘garbage in, garbage out,’ and without a solid transformation strategy, we’re sort of just throwing darts in the dark and the organization experiences a lot of trash.

AI transformation strategy

There are few pieces here but I’m still developing a more confident framework (feedback welcomed!). I roughly think there are three planks to your AI transformation strategy:

1. Building capabilities → these aren’t just technical capabilities. These are skills that your organization requires to become AI native

2. Prioritization framework → how are you going to make smart decisions about where to invest

3. Implementation roadmap → once you’ve prioritized your opportunities then you have you ship fast

Component 1: Building AI capabilities

It starts with you, but then you need help.

Read my call to arms why if you’re not an AI native leader in 2025, you’re cooked.

If you're not AI-native, your organization won't be either. This isn't about becoming a technical expert; it's about developing AI fluency and modeling the learning behaviors you want to see in your team.

Here's what that looks like practically:

Dedicate learning time weekly. I spend 4-8 hours every week for AI experimentation and learning. Not reading about AI or watching demos, but actually using tools. I draft content in Claude, analyze data in ChatGPT, vibe code in Bolt/Codex/Windsurf and test new workflows with various tools like Clay. The key is hands-on experience, not theoretical knowledge.

Build a learning culture, not a training program. Traditional training doesn't work for AI because the landscape changes weekly. Instead, create systems for continuous organizational learning:

• Share experiments openly. Out leadership Slack channel is filled with "AI Experiments" and learnings where leaders share what they tested, what worked, and what didn't. No successes required – failure data is just as valuable.

• Create safe-to-fail environments. The biggest barrier to AI adoption isn't technical – it's psychological. People fear looking stupid or breaking something. Make it clear that experimentation is expected and mistakes are learning data.

• Establish AI mentorship pairs. Match your early adopters with hesitant team members. Peer learning works better than top-down mandates.

The harsh reality: You can't outsource your AI transformation strategy to consultants or expect a single "AI expert" hire to magically solve everything. But you also can't expect your current team to figure it out alone.

Here's the balanced approach that actually works:

The 70/20/10 Capability Model

I think roughly sixty to seventy percent of your capability development should focus on leveling up your existing leadership team. Your Rev Ops leader, head of demand gen, and frontline sales managers need to become AI-fluent. Maybe not AI experts (although you’re hoping some will be), but certainly AI-fluent. They need to understand what's possible, what's realistic, and how to manage AI-enhanced processes.

This means dedicating real time and resources to their development. Set a clear expectation that this is a requirement of the job now. Create monthly "AI show and tell" where leaders share experiments and learnings with each other. Send your Rev Ops leader to AI-focused conferences (Winning by Design had some great content) and training programs. Most importantly, build AI literacy into your leadership development program and coaching so it becomes part of how your team thinks about solving problems.

Why does this matter so much? These are the people who understand your specific GTM challenges, customer base, and operational constraints. They're the ones who can connect AI capabilities to real business problems instead of chasing shiny objects that don't move the needle.

Twenty percent should come from hiring strategic AI expertise. I’m hiring a GTM AI Lead right now to bolster our AI capabilities. This is someone who's built AI-enhanced processes at other growth companies and understands the operational realities of revenue organizations. They need to be on the cutting edge.

You're looking for someone who’s built really interesting things, not someone who just talks about AI. They need a track record of building processes and tools. Most importantly, they need the ability to translate technical capabilities into business outcomes and strong change management skills because they'll be leading organizational transformation.

This person should own your AI transformation roadmap, bridge technical and business teams, lead capability development for your leadership team, and measure and communicate AI ROI. They're your internal AI translator and change agent.

The final ten percent should come from strategic consulting for specialized needs. Use consultants for specific, time-bound projects where you need deep expertise you can't develop internally. This might include AI strategy development workshops, technical integrations you can't handle internally, industry-specific AI use case development, or advanced analytics model building.

I don’t think you can hire a consulting firm to "lead your AI transformation" and outsource this responsibility. They don't understand your business well enough and won't be there to manage the ongoing cultural change that makes or breaks AI adoption. Consultants can provide specialized expertise, but the transformation has to be led from within. You can also get a bunch of this from the best vendors. If you’re spending real money then a company like Clay will dig in deep with you and they are on the absolute cutting edge.

Component 2: Prioritization framework

Here's the biggest mistake I see revenue leaders making: they treat AI initiatives like a buffet. They try a little bit of everything without any systematic way to decide what's worth pursuing.

You need a prioritization framework so you can drive focus. The problem with random AI experimentation is threefold:

1. Resource waste - Your team gets scattered across too many initiatives

2. Change fatigue - People get overwhelmed by constant new tools and processes

3. No learning - Without systematic evaluation, you can't tell what's working and why

I don't care what framework you use, but I would encourage you to use one. My personal framework is the 4 P's (Possibilities + Payoff + Probability + Perspiration), adapted from Annie Duke's decision-making principles in How to Decide.

Possibilities focuses on mapping the full opportunity landscape. What AI applications could potentially impact your revenue operations? I use Teresa Torres' Opportunity Solution Tree approach here. Start by identifying your biggest revenue challenges, then brainstorm potential AI applications that could address each challenge. Don't filter yet - the goal is comprehensive ideation across your entire revenue engine.