Over the past few weeks, I've had the same conversation with dozens of CEOs, COOs, and engineering leaders about the vision for AI at their companies. Different companies, different stages, different industries. But the end state they describe is nearly identical every time.
They want one system that connects all their tools, holds all their context, and is accessible to everyone at the company. They want to ask it questions and get real answers, not from one app, but across everything. They want it to live where they already work. And increasingly, they want it to go further: to take action, build things, and automate the workflows that are eating their teams alive.
A COO at a venture-backed operating firm told me his team built an internal AI tool to manage workflows across systems, because right now, information lives in a dozen different places. He said he's literally typed specific keywords into Slack messages just so he could find them later. "Three days go by and you can't find a damn thing."
An investor at one of the largest growth equity firms in the world put it even more directly:
"We need a central portal where everyone can collaborate and build things using AI, with a shared system of truth. Right now, 20 people are enriching data in 10 different ways using three overlapping data sources. Two people are working on the same exact project. They didn't even know."
A CS operations leader at a SaaS company said something that stuck with me: "This is a new sort of industry. I want to follow it, but I don't know what people are calling this. Maybe it hasn't been defined yet."
I think it has. We call it the AI computer.
The convergence
What's remarkable is that everyone is arriving at the same place independently.
The CEO of a fintech company told me he'd been tinkering on the side, building his own system with AI coding tools and open-source agents.
The CEO of an e-commerce intelligence company described how he'd been connecting his entire business stack via APIs and MCPs, creating what he called an "institutional brain." He was deep into it. But his non-technical cofounder couldn't use any of it. He told me:
"What you guys have created here is something I've realized is possible and incredibly powerful, which is connecting the tech stack to something with real compute power, in a way that is accessible and people without knowledge of the terminal can benefit from."
This is the pattern. Leaders see what's possible with AI. They look at their fragmented tool stack. They imagine a single connected layer of intelligence across the company. And because they haven't seen a great option that does this, they assume their team needs to build it.
When I show them what we've built, the reaction is always the same:
“That's what I was trying to build.”
The 70% problem
I don't think these teams are wrong to experiment. The instinct to build is a signal that the need is real. But there's a ceiling that keeps showing up.
A technical operator at a SaaS platform told me:
"I'm a very technical person. I look at something like this and think I could probably do this myself. I could get 70% of the way there, which would satisfy most of my needs."
Then he paused. "Obviously, for someone less technical than me, this is like magic."
That 70% is the gap. What works for one person in a terminal doesn't work for the rest of the company. And the last 30%, the permissions, the security, the integrations that don't break, the ability to deploy apps anyone on the team can use, is where the real value lives. They need a platform, not a side project.
Most leaders I talk to say the solution has to live in Slack. They do not want a new app to log into or a dashboard nobody checks after the first week. Slack is where their teams already spend their day. Anything that requires people to context-switch out of Slack to get answers is dead on arrival.
The companies that get the most value from AI are the ones where the barrier to using it is zero, where anyone on the team can ask a question in the place they're already working and get a real answer backed by real data from across the company.
The investor I mentioned earlier referenced Block's internal AI system, Goose, as the model:
"Having a central hub, you look at the value Block was able to unlock. They talked about Goose being their core system that everyone collaborated on. This is essentially allowing every company to create their own version of that."
Block spent years and dedicated engineering teams building Goose. Most companies don't have that luxury. They need the same outcome without the same investment.
When companies measure the value, they go all in
There's a moment that keeps happening in these conversations. A leader starts out cautious, with maybe a few people testing AI with a small pilot. Then they start measuring what it actually delivers.
One of our earliest design partners, a company with over 200 employees, started with a small team. Within weeks, they requested to consolidate all of their AI tools into a single platform. They didn't want five different AI subscriptions across five departments. They wanted one system of intelligence for the whole company, because once they saw what was possible with connected tools and real context, going back to fragmented point solutions felt like going back to paper.
The companies that lean in hardest are the ones that measure the impact. They see data inquiries go from hours to seconds. And competitive intelligence that took half a day now takes ten minutes. Their sales team gets real-time pipeline insights pulled from four different systems in a single answer. That’s when the ROI becomes obvious, and the investment follows.
What's changing is that AI is no longer a cost to manage. It's becoming the infrastructure that makes every other investment more productive. The companies that figure this out early are pulling ahead.
The AI computer category is forming
I've spent the last few weeks listening. And what I'm hearing is that a new category is being defined in real time.
Every company will have an AI computer. A single system of intelligence that connects to all their tools, is accessible to everyone on the team, and goes far beyond search and answers.
The AI computer writes code. It builds apps. It takes action. It knows your business better than any single person can.
I've spent my career in enterprise software, and I've never seen a need emerge this fast and this uniformly across the market. Every company, every leader, is arriving at the same conclusion at the same time. The only gap is that most of them think they have to build it from scratch.
They don't.
That's what we're building at Adapt. If you want to see it, let’s talk.
