A CRO at a $300M SaaS company told me that he had just come out of a board meeting where they discussed cutting 15% of the go-to-market team. Not because the team was underperforming. Not because the business was struggling. Because the board had convinced themselves that AI would handle what those people were doing within 18 months — and they wanted to get ahead of it.
“We’re not even using AI that well yet,” he told me. “But the board read the Block announcement and now everyone’s counting heads.”
This is the moment we’re in. And I want to be honest with you about how strange and how consequential it is.
The Block Signal and What It Actually Means
Jack Dorsey didn’t dress it up. When Block cut from 10,000 to fewer than 6,000 employees this month, he said: “This is not driven by financial difficulty, but by the growing capability of AI tools to perform a wider range of tasks.”
Read that again. He’s not saying AI replaced those roles. He’s saying he believes it will.
Oracle followed the same week: 20,000–30,000 cuts to free up $8–10 billion for AI infrastructure investment.
And here’s the data that makes this stranger: 45,000 tech layoffs in March 2026. 20% explicitly attributed to AI — up from less than 8% last year. But at the same time:
Only 14% of organizations have AI solutions ready to deploy
Only 11% are actively running AI in production
76% are deploying or implementing agentic AI in theory
Companies are restructuring for a capability that hasn’t fully arrived. The expectation of AI performance is driving business decisions faster than AI performance itself.
This has never happened before. In every prior technology wave — cloud, mobile, SaaS — companies restructured after the technology proved itself at scale. This time, the restructuring precedes the proof. The expectation economy has arrived.
And if you run a GTM organization, you have a decision to make before someone makes it for you.
The 65-Point Gap Nobody Is Talking About
Here’s the number that should keep every executive up at night: the gap between intent (76% deploying agentic AI) and reality (11% in production) is 65 points.
Sixty-five percent of organizations are somewhere in between — buying tools, running pilots, writing policies, reorganizing teams, reallocating budgets — but not actually running AI-powered workflows at scale.
They’re paying for the transition without getting the performance. That’s the worst possible position. You have the cost of change without the benefit of capability.
Why does the gap exist? Three reasons, and they’re all fixable with the right architecture:
Data quality. Organizations are committing to agentic AI workflows faster than they’re addressing the data quality those workflows require. An AI agent is only as good as the signals it runs on. If your CRM is 60% accurate, your AI-powered outreach is 60% accurate — and it’ll move 10x faster than a human in the wrong direction.
Integration depth. Demandbase’s new B2B AI GTM Report found that integrating CRM, marketing automation, predictive scoring, and advertising tools into a unified stack boosts conversion rates 53%. Most companies have those tools. Almost none have them fully integrated. You’re not missing software — you’re missing connective tissue.
Governance frameworks. 76% of GTM leaders are deploying agentic AI at a faster rate than they’re establishing the governance frameworks those deployments require. No decision rules. No escalation logic. No human-in-the-loop design. Just agents running in the wild.
The companies that are winning — the ones in that 11% running AI in production — aren’t doing something heroic. They built the infrastructure first. They didn’t skip the connective tissue.
Check out the Claude Cowork and Code Guides for Go-To-Market Below
What the AI-Native GTM Org Actually Looks Like
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