Open any basic math workbook and you’ll likely find a mix of problems asking students to solve a mathematical equation. At first glance, the requests appear almost identical: Get the answer. Figure it out. Arrive at a solution.

But solving and evaluating are not the same thing. Solving asks, “What is the answer?” Evaluating asks, “How did you get there?” and “Does the answer make sense?” And elevating asks, “What do I uniquely know that is valuable here?”

Those are the questions I ask my 12-year-old when he tackles a math problem. Not just, “What answer did you get?” but, “How did you arrive at that answer?” and “What did you know from experience that helped you get there?” When he can answer those questions, learning has taken place.

Artificial intelligence (AI) doesn’t remove that opportunity to learn. In some ways, it makes the distinction even more important.

For decades, success has been defined by our ability to help someone arrive at the right answer. Individual contributors and leaders alike have spent years in primary and secondary education with a heavy emphasis on solving problems. That expectation follows us into the workplace, where leaders have traditionally been expected to solve problems — or help others solve them.

A new product launch needs to happen? Let’s identify the variables and come up with a solution. A customer has a challenge? Let’s solve it. A team is struggling? Let’s figure out the answer.

The entire system has been predicated on solving. And leaders will continue to need to help solve. But AI is changing the system and, in the process, changing what’s required of individuals and leaders. AI can solve. It can provide a solution, perform an analysis, draft an outline, write content, summarize information and generate ideas in seconds. It can give you an answer faster than many people ever could.

From Solving to Evaluating

If AI can solve, something people previously looked to their leaders for, does that mean leaders pack it up and go home? Do we relegate leadership to the bots? Are the answers already given and humans have lost? Just the opposite.

This is where the role of the leader evolves in a profound way. When AI is solving the equation, it becomes the leader’s responsibility to help their teams evaluate what AI provides. And evaluating is fundamentally different from solving.

Evaluation asks different questions, not simply “What’s the answer?”

Instead, it asks:

• Is the answer accurate?
• Is it reliable?
• Does it reflect the context?
• What assumptions is it making?
• Will it work in the real world?
• What might be wrong with it?
• Should we use it at all?

The value of leadership, combined with AI, lies not in producing the first answer, but in applying human judgment to determine whether the inputs, process and output deserve to be trusted.

Our recent Blanchard research reinforces this shift. When individual contributors were asked where they most need their leaders to focus as AI use increases, the top two answers were coaching and development (27%) and judgment and decision-making (26%) — well ahead of building trust or empathy. Thus, as AI becomes better at generating answers, people increasingly look to leaders to develop better thinking. While these leadership skills were already in demand, the acceleration of AI makes them even more urgent.

The paradigm is already shifting. Our research suggests employees aren’t looking to leaders for faster answers. They’re looking to leaders who help them make better judgments. Leadership is moving from solving to evaluating. But it doesn’t stop there.

From Evaluating to Elevating

Evaluation ensures we don’t make bad decisions. Elevation is what helps us make extraordinary ones.

AI gives us access to humanity’s stored knowledge. It recognizes patterns in what already exists and predicts the most likely answer based on everything that has come before. Interestingly, our research also found that when people use AI, they believe their own greatest value still comes from distinctly human capabilities. Nearly one-half (45%) identified creative thinking and innovation as where they add the most value when working with AI, followed by solving complex or ambiguous problems (40%) and applying judgment to AI-generated work (36%).

AI may generate possibilities, but people still create breakthroughs. But breakthrough ideas rarely come from predicting what has already happened. They come from people.

The next great product, business model, customer experience or scientific breakthrough often emerges because someone saw possibility where everyone else saw a mistake.

Think about the invention of the Post-it Note. The adhesive was originally considered a failure because it didn’t stick well enough. It took human curiosity, experimentation, and a leader willing to encourage exploration for that “failed” invention to become one of the most recognizable office products in the world.

AI would likely have optimized the original objective, but humans reimagined it. That is elevation.

The leader’s role is no longer simply to validate AI’s output. It is to create the conditions where people can build on it, challenge it, combine ideas that don’t obviously fit together and imagine possibilities AI cannot see because they don’t yet exist.

AI is an extraordinary starting point, but it should never become the finish line. This is where leaders become coaches in the truest sense. A great coach doesn’t do the work for the team member. They ask thoughtful questions that stretch thinking, uncover assumptions, and invite new perspectives. They help people move beyond the first answer toward a better one.

Instead of asking, “Did AI solve the problem?” leaders begin asking:

• How could we make this better?
• What isn’t AI seeing?
• What would delight our customer?
• What’s possible that no one has tried before?
• What human insight could transform this from good to exceptional?

The future of leadership is about developing people’s uniquely human capacity to imagine what doesn’t yet exist, connect ideas in unexpected ways, exercise wisdom and create value that no algorithm can predict.

The paradigm shift isn’t just from solve to evaluate. It’s from solve to evaluate to elevate.

AI can generate answers. Leaders help people determine which answers matter — and inspire them to create the ones AI never could.

Practical Tips for L&D Teams: Help Leaders Evaluate AI Output and Elevate Employee Contributions

For L&D professionals, this shift means leadership development can no longer focus primarily on teaching leaders how to solve problems. Instead, training should help leaders build the skills to critically evaluate AI-generated outputs and coach employees to contribute the uniquely human judgment, creativity and contextual insight that AI cannot replicate.

Evaluate AI Output

Instead of training leaders to accept AI-generated answers at face value, help them develop the habits of critical evaluation before taking action.

Encourage leaders to slow down and ask questions that uncover employees’ thinking: How did they approach the challenge? What assumptions did they make? What information or context influenced their decisions? How did they approach tackling a challenge or opportunity?

This might mean starting with evaluating the prompt or input before assessing the output. Evaluation is looking at what goes in as much as what comes out. Did they factor in the task, audience, context, constraints, risk level and what “good” should look like? This is the part of the math problem where you write down the known variables and figure out the unknown variables you may be solving for.

Encourage validation.

Leaders must step back and ask, “Does this actually make sense?” Not in a judgmental way, but in a way that allows people to look for clear claims, sound reasoning and specific recommendations.

Assess usefulness and real-world fit.

Help leaders evaluate whether AI’s response will truly help someone make a decision or take meaningful action.

Encourage them to consider whether the recommendation fits their industry, organization, customers and team. Does it feel practical and achievable, or does it sound good in theory but fall short in reality? AI can generate ambitious ideas, but leaders must apply human judgment to determine whether those ideas align with organizational priorities, available resources, stakeholder buy-in, authority, workload and timing.

Challenge the first answer.

Train leaders to ask employees to generate counterarguments, risks, tradeoffs and alternative options. Use AI to pressure-test thinking, not simply confirm the original idea.

Consider the person.

Evaluation also means knowing whether the individual proposing the solution is capable of implementing the solution. Do they have the confidence and competence to deliver? Evaluating AI output needs to extend to evaluating the capacity of the human being accountable for taking action.

Elevate Employee Contributions.

Once leaders can evaluate AI-generated outputs, the next step is teaching them how to elevate employee contributions.

Leadership development should equip leaders to coach employees to apply their experience, judgment and creativity so AI becomes a starting point for better thinking rather than a substitute for it.

Consider training leaders on the following best practices:

Encourage the infusion of lived experience.

Ask employees to identify places where they have faced similar challenges and encourage them to share how that experience aligns with or diverges from what AI has provided. Recognize the employee’s role in applying context, experience, discernment and accountability.

Use AI work as a coaching moment.

Uncover why AI was used, what the human provided and what they noticed. This conversation keeps the employee responsible for the final thinking, not just the final output.

Separate AI assistance from employee value.

AI may draft, summarize or brainstorm. Employees add value by interpreting, prioritizing, adapting, checking risk and making the final call.

Create psychological safety.

Make it safe for employees to disclose AI use, ask basic questions and admit uncertainty. Be clear about what AI can support, what requires review and what must remain human-led. Consider what team norms, productive or detrimental, may be forming around AI use and actively shape them for greater effectiveness.

Recognize responsible AI behavior.

Praise employees who catch errors, verify claims, adapt generic content, disclose AI use and improve the work with real context. Reward better thinking, not simply faster output.

Final Thoughts

The teachers who asked us to “show the math,” “show your thinking,” and “bring your own ideas” understood something we are now rediscovering at work.

The value was never only in getting the right answer. It was in how we arrived there.

As answers become easier to access, the leader’s role is evolving from being the person with the answer to being the person who evaluates the thinking, elevates the learning and creates the conditions for people to bring their best judgment, curiosity and originality to the work. What matters now is not only what people produce, but also how they think, what they question and how their unique intelligence moves the organization forward.