The AI Output Trap: Why More AI Doesn’t Always Mean Better Results

AI has given us an incredible ability to produce more, faster.

We can analyze large amounts of information, generate documentation, summarize complex material, identify patterns, create recommendations, troubleshoot problems, and develop polished artifacts in a fraction of the time these activities once required.

And we should be experimenting with it.

I certainly am.

But the more I work with AI in project delivery, the more I find myself coming back to one principle:

Experimentation is important. But experimentation without a problem to solve can produce impressive artifacts without producing meaningful outcomes.

There is tremendous pressure right now for organizations and teams to embrace generative AI. Understandably so. Nobody wants to be the organization that stood still while an entirely new way of working developed around them.

But in the rush to figure out everything AI can do, I think we’re sometimes skipping a much more important question:

What problem do we actually need AI to solve?

The AI Output Trap

One of the most impressive things about generative AI is also one of its potential traps:

Give it information and it will produce something.

Give AI project documentation and ask it to analyze it. Feed it operational data and ask for recommendations. Give it requirements and ask it to create supporting documentation. Provide a collection of project artifacts and ask what it sees.

The results can be remarkable.

They can also look remarkably complete.

That distinction matters.

A beautifully structured analysis with recommendations, tables, risks, and insights creates an immediate sense that something valuable has been accomplished. But the sophistication of the artifact doesn’t necessarily reflect the completeness of the thinking behind it.

We’ve created an artifact. That doesn’t automatically mean we’ve created an outcome.

And because AI has dramatically reduced the effort required to produce analysis, documentation, dashboards, summaries, and recommendations, we can now create far more information than ever before.

But our ability to consume information—and more importantly, act on it—hasn’t increased at nearly the same rate.

More analysis doesn’t automatically create more clarity.

Sometimes it just creates more noise.

The Swiss Cheese Problem

This is what I’ve started thinking of as the Swiss cheese problem of AI.

Imagine giving an AI tool a collection of project artifacts and asking it to produce an analysis or deliverable.

The output may be detailed, polished, and largely accurate.

But if the information we provided didn’t represent the complete context of the problem we’re trying to solve, the result can look whole while actually being full of holes.

Swiss cheese.

Those gaps may represent business context. User behavior. Process dependencies. Historical decisions. Technical constraints. Organizational nuances. Or simply information that existed somewhere other than the documents we happened to provide.

AI didn’t necessarily fail.

We didn’t give it the context required for the outcome we expected.

That’s an important distinction.

The answer isn’t necessarily to give AI more information.

It’s to give AI the right information for the problem we’re asking it to solve.

Start With the Outcome, Not the AI

This has changed how I think about AI adoption in project management and delivery.

Rather than starting with:

“What can AI do with all of this information?”

I think we should start with:

“What problem am I trying to solve?”

Then work backward.

What does a successful outcome look like?

What would someone need to understand to produce that outcome correctly?

What context would a knowledgeable human need?

Where does that context live?

Only then should we decide what information, documentation, data, process knowledge, or technical artifacts AI needs.

The desired outcome determines the context.

That sounds simple, but I believe it’s one of the most important disciplines organizations can develop as they implement AI.

Because the goal shouldn’t be to get the most out of AI.

The goal should be to get the right things out of AI.

From AI Experimentation to Intentional AI Adoption

I’m a big believer in experimenting with AI.

In fact, I think teams should be encouraged to test it, challenge it, learn what it does well, discover where it struggles, and continuously identify opportunities to integrate it into their work.

But experimentation becomes much more valuable when it is intentional.

For me, intentional AI adoption follows a simple process:

1. Define the problem

Start with the friction—not the technology.

Where are we losing time? Where are errors occurring? Where is repetitive work consuming skilled resources? Where are people struggling to find or interpret information?

Be specific about the problem AI is being asked to help solve.

2. Define the outcome

What would success actually look like?

A faster process? Fewer errors? Higher-quality output? Better consistency? Less manual effort? Faster access to information?

If we can’t describe what improvement looks like, it becomes very difficult to determine whether AI actually created value.

3. Build the right context

Work backward from the desired outcome.

What information would a knowledgeable person need to understand the problem correctly?

That might include technical documentation, business processes, user context, historical decisions, data, examples, policies, or other supporting information.

This is where the holes in the Swiss cheese begin to disappear.

4. Give AI a specific job

Instead of asking AI to broadly “analyze” everything, give it a clearly defined responsibility.

The narrower and more intentional the assignment, the easier it becomes to evaluate the quality of the result.

5. Validate the value

This may be the most important step.

Was the output accurate?

How much human intervention was required?

How much time did we actually save?

Did quality improve?

Did we reduce errors or rework?

And ultimately:

Did this make the work better?

Did AI Remove Work—or Just Move It?

This is a question I think we need to ask more often.

AI can produce an impressive amount of work in an impressive amount of time.

That’s exciting.

But generating something quickly isn’t the same as completing the work quickly.

If AI creates an artifact in five minutes but a subject matter expert spends hours correcting assumptions, filling contextual gaps, removing irrelevant information, and restructuring the result, we need to account for that effort when evaluating the value of AI.

Maybe AI still saved 30 percent of the effort.

That’s valuable.

Maybe it saved 70 percent.

Even better.

Or maybe it simply moved the work downstream.

We won’t know unless we’re measuring the outcome rather than celebrating the output.

The measure of successful AI adoption isn’t how much AI-generated work we produce. It’s how much useful work AI removes.

That distinction matters enormously as organizations begin measuring the ROI of generative AI.

Small AI Use Cases Can Create Big Value

There’s a temptation to approach AI transformation with grand questions:

How can AI transform our entire delivery lifecycle?

How can we become an AI-first organization?

How can AI reinvent the way our teams work?

Those are worthwhile strategic questions.

But some of the most valuable AI use cases may start much smaller.

Where does a team repeatedly encounter the same type of error?

Where are skilled people spending time searching for information?

Where are people manually comparing large amounts of data?

Where is documentation repetitive?

Where could AI accelerate analysis without replacing the judgment required to make the final decision?

Those aren’t particularly flashy AI use cases.

They don’t necessarily make impressive transformation slides.

But if a narrowly defined AI use case consistently saves several hours, improves accuracy, or removes frustrating repetitive work, that’s real value.

And once it works, expand it.

Experiment.

Measure.

Learn.

Build on what works.

That’s how experimentation becomes adoption.

Intentionality May Be One of the Most Important AI Skills

We’re in an AI arms race of sorts.

Organizations are investing. Leaders are experimenting. Teams are learning. New AI tools and capabilities are appearing almost daily.

That urgency isn’t necessarily a bad thing.

We should be curious. We should experiment. And we should absolutely challenge ourselves to rethink how work gets done.

But urgency shouldn’t eliminate discipline.

Successful AI adoption may ultimately have less to do with how many AI tools an organization deploys or how many AI-generated artifacts a team produces.

The better measure may be whether teams become really good at answering four questions:

What problem are we solving?

What does AI need to understand to help us solve it?

What does a successful outcome look like?

Did using AI actually make us better?

If we can’t answer those questions, another AI-generated dashboard, analysis, document, or recommendation probably isn’t the answer.

It may just be another slice of Swiss cheese.

Experiment with AI. Absolutely. But don’t confuse producing something impressive with solving something important.