The RevOps Reckoning: Why Most Companies Are Already Too Late

Vasco’s 2026 RevOps Trends & Predictions report

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.

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.

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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.