AI Whack-a-Mole: Why Your AI Projects Keep Failing
You ask AI to do a thing. It says the thing is done. But it’s not done. So you fix it. Then you find something else that’s wrong. Then you fix that. Then you’re back to the first problem, slightly differently shaped.
This is AI Whack-a-Mole. And if you’re using AI regularly for real work, you’ve felt it.
It’s not a sign that you’re using AI wrong. It’s a sign that you’re using AI without enough structure — and structure is fixable.
What AI Whack-a-Mole Actually Looks Like
It shows up differently depending on how you’re using AI, but the pattern is consistent: you’re spending significant time managing the AI’s output rather than using it.
Some common forms:
The Cleanup Loop. You have a prompt that works reasonably well. But every time you use it, you’re doing the same 15 minutes of cleanup afterward. The AI keeps making the same kinds of mistakes. You correct them. You don’t update the prompt. The next time, same thing.
The Drift Problem. You’re working on something complex across multiple sessions. The AI loses track of earlier decisions, reverts to defaults, or contradicts something it said two steps ago. You spend more time re-briefing and correcting than actually moving forward.
The Cascade Failure. You’re using multiple AI steps in sequence — the output of one feeds into the next. A mistake early on propagates forward, and by the time you catch it, you’ve got a chain of outputs that all need to be redone.
The Approximate Output. The AI produces something that’s technically an answer to what you asked, but not quite what you needed. Close enough that it’s faster to fix than to redo — but this keeps happening, which means you’re never fully satisfied with what comes out.
All of these have the same root cause: the AI doesn’t have a clear enough definition of what “done” looks like.
Why This Happens (It’s Not the AI’s Fault)
This is a common misdiagnosis. People assume AI Whack-a-Mole means the AI isn’t good enough, and upgrade to a newer model or a different tool. Sometimes that helps marginally. It rarely solves the problem.
The real issue is architectural. When you ask AI to do something without specifying what a correct output looks like — what it must contain, what it must not contain, where it should stop and check in with you — you’re leaving the AI to interpret “done” on its own. And its interpretation will be approximately right, which is exactly what produces the Whack-a-Mole cycle.
This isn’t the AI being lazy or incompetent. It’s doing exactly what it was asked to do. The problem is the ask wasn’t specific enough.
The Structural Fix
The solution isn’t a better prompt. It’s a better system.
Specifically, three things eliminate most AI Whack-a-Mole:
1. Separate your “how” from your “what.”
Most prompts try to do too much at once — explain the context, describe the task, specify the format, set the tone, and handle edge cases, all in one block of text. This creates inconsistency because small variations in how you phrase the prompt produce significantly different outputs.
The fix is to store your “how” separately — a persistent instruction set that lives in your AI Project or a dedicated document — and keep your prompts focused on the specific “what” of each task.
2. Define what done looks like before you start.
Before asking the AI to produce anything, specify what a successful output contains, what it explicitly should not contain, and at what point it should stop and check in with you rather than making a decision on its own.
This is what a Contract is. Not a legal document — a simple, structured definition of inputs, outputs, and checkpoints. It forces the AI to evaluate its own work against a clear standard rather than presenting you with its best guess at what you wanted.
3. Catch errors at the step where they happen, not after they’ve propagated.
In any multi-step AI workflow, adding a review checkpoint after each step costs less than fixing cascading errors at the end. This feels slower but is significantly faster in practice, because the cost of catching an error early is much lower than the cost of unraveling a chain of outputs built on a flawed foundation.
The Deeper Pattern
AI Whack-a-Mole is a symptom of a mindset that treats AI like a smart search engine — you put something in, something comes out, and you judge the result. If it’s not right, you try again.
That works for simple, one-shot tasks. It breaks down completely for anything complex, multi-step, or repeated.
The mindset shift that actually eliminates the problem is treating AI like a system rather than a tool. Systems have inputs, outputs, standards, and checkpoints. Tools have on/off switches.
When you start designing AI workflows the way you’d design any other reliable process — with clear specifications, defined quality standards, and structured checkpoints — the Whack-a-Mole cycle stops. Not because the AI got smarter, but because the system got clearer.
A Practical First Step
Pick the one AI task where you do the most repeated cleanup. Write down, as specifically as you can:
- What you give the AI to start
- What a perfect output contains
- What a perfect output does not contain
- Where the AI should stop and ask you a question rather than guess
That’s a Contract. Apply it to that one task and see what changes. Most people who do this notice an immediate reduction in cleanup time — not because the AI changed, but because it now knows what it’s aiming for.
From there, you can build. But start with one.
FAQ
Is AI Whack-a-Mole just a sign that I’m using AI for the wrong things?
Sometimes, but not usually. Most AI Whack-a-Mole happens with tasks where AI genuinely can help — the problem is the workflow structure, not the task selection.
Does this require technical knowledge to fix?
No. The fixes described here — separating instructions from prompts, defining outputs, adding checkpoints — are all conceptual changes that can be implemented with plain text documents.
What if I’m using AI for one-off tasks rather than repeated workflows?
One-off tasks are actually where AI tends to perform best, because the lack of structure matters less when you’re not repeating the same task. Whack-a-Mole is primarily a repeated-workflow problem.
How long does it take to see results from these fixes?
Usually immediately. The first time you apply a well-defined Contract to a task you’ve been doing the hard way, the difference is noticeable within that session.
Mitch Schwartz is the founder of Ops Machine, a Montreal-based AI integration and workflow consultancy. He works with nonprofits and organizations mid-transformation to find where AI fits, build the right systems, and make sure teams actually use them. Book a free discovery call →