At a recent strategy session, a leadership team spent 45 minutes reviewing an AI initiative that hadn't produced a single measurable result in six months.
The vendor was still "onboarding."
The executive sponsor was still presenting the same hockey-stick slide from the kickoff deck.
Nobody asked the hard question.
Here's what nobody in that room was willing to say out loud: the project was producing something. Just not what it promised.
It was producing the appearance of transformation.
Board optics. Analyst calls. LinkedIn announcements about "our AI journey." The CEO looked visionary. The CTO looked innovative. The vendor looked busy.
Everyone looked great.
The pain of failure? Still six months away.
The reward for looking innovative? Available right now.
That's not a technology problem.
The Hidden Payoff Nobody Talks About
There's a concept in psychology about the hidden reward embedded in behavior that clearly isn't working. We keep doing something not because it's producing results, but because it's giving us something else. Something we won't admit out loud.
For mid-market executives, that something is the appearance of leadership without the accountability that comes with it.
Think about what a high-profile AI initiative delivers before it delivers results:
- It signals ambition to the board.
- It positions the executive as a transformation leader.
- It generates activity: demos, vendor calls, status updates. All of it looks and feels like progress.
It isn't.
The project is producing something. Just not what it promised.
The Real Problem Is Governance. Not Technology.
Here's what I've learned from years of post-incident analysis: projects don't fail because the technology is bad. They fail because nobody agreed, in writing, who makes decisions when the technology doesn't behave.
Nobody owns the outcome. Nobody answers when it blows up.
So when things go sideways, the response is more meetings. More activity. More appearance. Zero accountability.
Gartner projects that more than 40% of agentic AI initiatives will be canceled by 2027. That's not a technology failure rate. That's a governance failure rate.
The companies landing in the successful minority aren't running better technology. They've answered one uncomfortable question before anything gets built:
If this project fails six months from now, who specifically answers for it?
If the answer is "we all do," the answer is nobody.
What Accountability Actually Looks Like
Getting AI governance right doesn't require a 200-page framework or a dedicated compliance team. It requires three things most companies skip entirely:
- Decision rights. Who approves what. Who can stop the project. Who isn't allowed to expand scope without sign-off.
- Failure criteria. Defined in advance. Not discovered after 18 months of sunk cost.
- A named owner. One person. Not a committee. Not "the leadership team." One person who answers.
That's it. That's the conversation worth having before the vendor kickoff, not after the hockey stick becomes a cautionary tale.
The Bottom Line
I'm not anti-AI. The companies that build accountability architecture in the next 24 months will have a real competitive advantage over the ones still running their third inconclusive pilot in 2027.
But that advantage doesn't come from better technology. It comes from better governance.
And governance isn't a framework you download. It's a set of decisions about who decides, what happens when it breaks, and who answers for it.
If you're sitting on an AI initiative that's been in perpetual "progress," ask yourself one question before the next vendor call:
When this fails, who answers?
If you have to think about it, you already know the answer.
Mike McKenna is the founder of TEAM Solutions and a crisis leadership advisor to mid-market CEOs. If your company is deploying AI without clear accountability architecture, that's a conversation worth having before it becomes a post-mortem.
