AI Does the Work. People Solve the Problem.

AI Does the Work. People Solve the Problem.

Over the years, I’ve seen organizations seek outside expertise for two very different reasons. Sometimes they know exactly what needs to be done and simply need skilled people to help execute. Other times, the challenge is much less straightforward. The problem is poorly defined, the data doesn’t tell a clear story or processes have evolved to the point where what happens in practice looks very different from what was originally intended. And sometimes, what starts as a well-defined problem evolves into something entirely different as we dig deeper and uncover things no one initially anticipated.

In these situations, solving the problem requires more than technical expertise. It takes people who can engage with stakeholders, understand how work actually gets done, challenge assumptions and identify what needs to change to improve outcomes.

AI is rapidly transforming the execution side of this equation.

When a problem is well understood and the path forward is clear, AI can be a tremendous accelerator. It can analyze information, generate code, automate repetitive tasks, produce documentation and help teams move from concept to implementation much faster than traditional approaches.

But being able to execute faster doesn’t necessarily mean we’re solving the right problems.

Looking Beyond the Original Question

On one project, we were asked to analyze software usage logs to understand how users navigated a software application. It’s exactly the kind of task AI can help accomplish quickly: processing large volumes of data, identifying patterns and mapping user behavior.

But as we dug into the data, we discovered something unexpected. More than half of the activity came from users who didn’t have software licenses, something the software vendor hadn’t realized was happening, leading to a significant loss of potential revenue.

What started as a straightforward usage analysis uncovered a much larger issue. The team had spotted something the software vendor didn’t even know to look for.

AI could have performed the analysis and potentially even identified the discrepancy. But human curiosity and experience helped us recognize that the data was telling a bigger story than the one we’d been asked to investigate. By looking beyond the original question, we uncovered a significant business issue the software vendor hadn’t even known to look for.

This is where human experience and curiosity remain essential. Sometimes we learn more by questioning what we’ve been asked to solve than by simply finding the answer.

Rethinking AI-Enabled Problem Solving

The greatest opportunity isn’t simply using AI to accomplish more work with fewer resources. It’s combining organizational knowledge, human judgment and AI-enabled execution to solve the right problems more effectively.

At MANTECH, our See It Solved™ methodology reflects this approach through three principles:

1. Spot the Real Issues: Look beyond the initial request to understand the underlying challenge and what might be missed. Keeping people involved in AI-enabled analysis allows us to question assumptions, recognize unexpected findings and uncover issues that might not have been considered in the first place.
2. Clinch the Execution: Use the right combination of human expertise and AI capabilities to develop and implement solutions that deliver measurable results.
3. Bring Everyone Along: Engage stakeholders throughout the process to ensure solutions are understood, adopted and effective in practice.

As AI takes on more of the execution, these human contributions become increasingly important. AI can help us answer questions faster, but experience, curiosity and organizational understanding help us recognize when we should be asking different questions altogether.

For senior leaders, this shift has implications for how teams are structured, how talent is developed and how AI investments are evaluated. Success shouldn’t be measured solely by how quickly a solution is delivered or how much effort is automated. It should be measured by whether we’ve identified the right problem, delivered an effective solution and created meaningful operational or mission value.

The opportunity is to use AI to accelerate execution while investing more human expertise in determining what should be done, why it matters and whether it works. Getting all three steps right is what turns impressive capabilities into impactful outcomes.