You Were Oversold AI, and They Failed to Deliver
Why the team selling AI for everything is now selling a fix for the problem they created.
You’ve been digging through the trenches with your teams, trying to get them aligned on a new AI path with integration into your product, your projects, your deliverables. And somehow, it’s just not making the impact you believed it was supposed to. It’s not your fault.
Listening to a tech podcast this week, I heard a familiar message. One I’ve been trying to get people to listen to.
“The thing about AI for business,” it said, “it may not fit the way your business actually works.”
I stopped what I was doing, gobsmacked, that the team that had been selling AI for everything was stepping it back.
Once you see the messaging shift you’ll catch it everywhere.
It opens by conceding the hard part: AI doesn’t automatically fit your business. Real. Then it pivots — *we embedded it across HR, IT, and procurement, saved millions, freed thousands of hours.* And it lands on the destination: AI deep in the work that moves the business.
There’s a first line where AI didn’t fit, and a last line where it does. So what happened in the middle? What changed, so that the thing that didn’t fit suddenly fit?
Nothing changed. The ad states plainly there is a fit problem then declares it has the only solution by its closing. The only thing it adds between those two points is more AI.
”Deep in the work”
That’s not a solution. It’s the destination. Sold to you as if it were the road that gets you there — a road only they can sell you. They never once admitted that they sold you a wolf ticket. Because describing the real work would require them to admit their playbook was wrong.
Your teams create interactive experiences for humans by humans, with context and reasoning that live and work in the reality of the business. Then you’re asked to hand that work to a system and have it understand not just what the component is, but why it exists. Who it’s for.
The machine sees inputs and outputs. It doesn’t see context. And that’s where it breaks. That’s the real work not being named.
Embed AI deeper, they say, and it’ll figure out the context, but it won’t. Because the context lived in the handoff. In the way humans communicated, not in specs.
So here’s the one tool I want you to leave with.
The next time your team is asked to provide spec documentation for a machine, say this:
Our visual outputs have intent and reasoning behind them. Before we hand this to a system, we need to agree on what we’re actually asking it to understand and why.
Because the system will still misinterpret the handoff — it’ll skip the context, miss the exceptions, jump to a conclusion you never authorized. The rework still happens. It just happens faster now, with a model’s name on it and an ROI claim that can never be verified.
I write for the people living inside that gap — the ones who can see the handoff dropping and don’t have the authority to move it. If that’s you, the question above is yours to use in your next meeting.
There’s a second half coming soon. Because the leaders who keep buying the “embed it deeper” pitch aren’t naive. They’re doing something rational given the constraints they’re working inside. And that’s worth understanding before you judge the choice.
The Operating Gap diagnoses the systems that bury good work. If this landed, forward it to the person who keeps green-lighting the “embed it deeper” planning. Or send me your story at raevyndigital.com.
RaeVyn will be giving a talk on Sept 26th at the PMI Dallas Chapter’s North Texas PM Conference, UNT Frisco.
“Stop Patching Symptoms: A Diagnostic Framework for Project Delivery Failure.” Register here