There’s an obvious irony in writing yet another AI article to tell you there’s too much talk about AI. I know. Bear with me, because that’s exactly the point.
Every company is talking about it. Every conference has a panel on it. Every bit of software has grown an AI button. Your board’s asking what you’re doing about it, vendors are making bigger and bigger claims, and LinkedIn is wall to wall with people telling you what the future looks like.
Then I look beyond the conversation at what’s actually happening inside these businesses, and it’s a very different picture.
There’s interest. Workshops. A policy. A pilot or two. A steering group. A pile of licences bought. What there often isn’t, is anything genuinely live, used by real people every day, actually changing how the business runs.
We’ve got far too comfortable calling that progress. It isn’t. It’s activity. Many organisations have moved from AI curiosity to AI experimentation, but far fewer have reached genuine AI adoption at scale. And you can be extraordinarily busy with AI while achieving almost nothing.
The Gap Between AI Activity and AI Adoption
Over the last couple of years, I’ve sat in on plenty of conversations where the first twenty minutes made it sound like the company was miles down the road. The vocabulary was all there. Agents. Automation. Intelligent workflows. Transformation.
Then someone asks the only question that matters. What’s actually live today? Not planned. Not demoed last month. Not something three people in one team are trying. What are people across the business genuinely using, and what’s changed because they are?
That’s usually where the room goes quiet.
I’ve watched organisations spend the thick end of seven figures on AI. Big launch, slick showcase, the right consultants in the room, everyone nodding along. Then you go looking afterwards for the thing it was all meant to produce, and there’s nothing running behind the curtain. They paid a small fortune for a brilliant show. They forgot to build anything.
That’s the trap. That’s where activity gets mistaken for progress.
So be honest with yourself for a second. You bought the licences. How many of your people opened one this week? You ran the pilot. What changed because of it? You stood up the steering group. What’s it actually shipped? Buying access to AI isn’t adopting it. Running a pilot isn’t changing how the work gets done. And a committee, however senior, is not a strategy.
Technology First, Problem Second
The oddest thing about this market is how often the tool turns up before the problem does.
The conversation opens with should we buy Copilot, build our own assistant, stand up an agent. It should open with what you’re actually trying to fix. Where are your people losing hours? What’s annoying your customers? Which decisions are getting made on bad information? What gets done the same way every single day for almost no return?
Get the problem clear and AI might be part of the answer. It might also not be. Sometimes the fix is a simpler process. Sometimes it’s better data. Sometimes it’s the dull automation that’s existed for twenty years. Sometimes it’s just clearer ownership and a couple fewer approval steps, none of which gets you a launch event.
AI isn’t fairy dust you sprinkle over a broken process. Bolt it onto something that already works badly and all you’ve done is help it fail faster.
That’s why so much of this quietly dies after the demo. The demo looked great. What nobody fancied doing was the unglamorous bit afterwards: the data, the permissions, the people who actually do the job, the risk, the result the business was promised. Real implementation is checking who’s allowed to see what, cleaning up the information, talking to the people on the ground, and finding out whether the idea holds up anywhere outside the meeting room.
It also means being willing to stop. Not every idea deserves a pilot, and not every pilot deserves to be scaled. Ploughing on because someone senior already announced it from a stage is exactly how a bad call becomes a three-year programme.
AI Agents Are Changing the Risk Landscape
It isn’t just that you’re slow. It’s that the tech is sprinting.
You might still be deciding whether your staff are even allowed to use public AI tools. The market has already moved on to agents that can go and find information, use applications and carry out whole chunks of a process on their own.
Unlike traditional AI assistants that generate content or answer questions, AI agents can interact with business systems and execute tasks, creating an entirely different level of operational and security risk.
That’s a serious leap. Letting a system suggest an answer is one thing. Letting it act is another thing entirely.
You’ve spent years controlling what people can get to inside your business. Identities, permissions, monitoring for anything that looks off. Now you’re bringing in what are effectively digital workers, often before you’ve decided who owns them, what they’re allowed to see, or how you’d stop one if it started doing something strange.
There’s plenty of talk here too. But ask who has visibility of which AI tools are in use, what company information is being typed into them, or how you’d halt an internal agent halfway through a task, and the answers get vague very quickly.
A written AI policy is worth having. It just doesn’t give you visibility.
Policies don’t show which AI tools employees are actually using, what data is being entered into them, or whether AI-generated actions are being monitored and governed appropriately.
It won’t show you where your data’s going, it won’t tell you someone’s quietly using a personal account, and it won’t watch an agent moving across five systems. You think you’re right at the start of using AI. Your people are already well ahead of you, and that gap between your AI governance policy and what’s really happening is where the trouble starts.
Moving From AI Theatre to Real Adoption
Here’s the part nobody wants on a slide. Nobody has this fully figured out. Anyone who sounds completely certain right now should make you nervous, because the technology’s changing too fast and we’re all still learning what works, what’s safe and what actually creates value.
There’s nothing wrong with admitting you’re experimenting, or that your strategy is still taking shape, or that you looked at a use case and quietly killed it. What there is something wrong with is the performance. The launch with nothing behind it. The confidence you haven’t earned. The invoice that bought you a show instead of a result. False confidence doesn’t help your board make a good call, it doesn’t help your people understand what’s changing, it won’t make your data safer, and it won’t turn a weak idea into a strong one.
That’s the conversation we focus on at MTI.
The unglamorous questions, not the predictions. Are you actually ready? Is the use case worth doing at all? What information will it need, and how do we secure it? Who owns it? What’s going to change, and how will you know whether it worked? Less exciting than another think-piece about the future of AI. Far more likely to leave you with something real.
The companies that come out of this well won’t be the ones making the most noise. They’ll be the ones honest enough to admit where they actually are, pick a real problem, and do the boring, difficult work of solving it.
So here’s the only question worth answering. When the showcase is over and the consultants have gone home, is anything actually running? If the honest answer is no, you’re not doing AI.
You’re acting.
Stop performing. Go and build something real.
This is the first article in a series from MTI’s Chief AI Officer exploring what successful AI adoption really looks like, from governance and security to agents, data readiness and measuring business value.
Next in the series: Graduates are no longer learning the future of work. In many cases, they’re bringing it with them.
Ready to move beyond AI theatre?
Whether you’re evaluating use cases, developing governance frameworks or trying to understand where AI can genuinely create value, MTI can help you separate opportunity from hype.
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