When a complex problem shows up at work, our default instinct is often to start solutioning. Great problem solvers spend time upfront deeply understanding the problem; weaker ones jump immediately to a fix. I call this tendency to lock onto the first idea that pops into our heads going with the “first, worst solution.”

Historically, moving from an initial hunch to a full pitch or presentation required effort. That friction was a feature, not a bug—it forced us to slow down and think. But Generative AI removes that friction. It makes it easier than ever to apply fast, lazy intuition to complex problems, bypassing the need for deliberate, deeper analysis.

AI Gives Shallow Solutions Polish

A half-formed thought is all it takes for AI to output a compelling narrative complete with professional slides. Magically, a single prompt turns an assumption-heavy hunch into a polished, boardroom-ready strategy—without anyone doing the hard work of actually understanding the problem.

When we use AI to skip the messy work of problem-solving, we end up building solutions that:

• Ignore the people inside the system: Structures look simple on paper, but creating environments where humans feel purpose, control, and agency is a slower, more dynamic and complex process.
• Anchor bad decisions: Polished, shallow solutions lock organizations into approaches that promise quick wins, but whose unintended side effects only become clear after they are too difficult to unlearn.
• Optimize for the wrong goals: Without spending time understanding root causes, we end up building systems that merely address symptoms.

The Process Is the Work

The value of true problem-solving isn’t in generating deck presentations or handing teams pre-packaged answers. Handing a team a beautiful, AI-generated solution robs them of the opportunity to figure things out for themselves.

Real progress doesn’t happen through show-and-tell presentations. Progress happens when teams:

• Engage directly with their challenges.
• Explore different problem frames.
• Build shared understanding.
• Try things and learn together.

Using AI to jump straight to a polished deliverable skips this entire learning loop. Teams become passive recipients of solutions rather than active designers of their own work. If a team is handed a pre-packaged way of working without going through the friction of learning, real agility stops being a possible outcome.

Agility isn’t something you can prompt, install, or buy. Agility emerges as people, policies, systems, structures and processes adapt and evolve together. Real organizational change requires growth in both the system and the people inside it.

Structural Changes Last

Generative AI code can be quickly tested and refactored, but using AI to redesign team structures or workflows is a completely different story.

When you change how teams are structured and how decisions get made, you alter power dynamics, interaction patterns and how people orient themselves within that system. At the same time, an AI-generated structural redesign might look good on paper and create a burst of short-term activity, the true costs surface later.

In complex environments, top-down and inside-out solutions always miss the mark, and the blind spots of a “first, worst solution” inevitably show up down the road.