What “Owning” the Work Really Means in the Age of AI-Enabled Leadership
One of the most interesting parts of my job is interviewing senior leaders from around the world and
across industries to gather their insights so we can build realistic business leadership simulations that help organizations learn by doing.
Recently, as part of a new leadership simulation for a global professional services firm specializing in construction and project management, I had a fascinating conversation with one of their senior leaders.
Like many organizations, they are trying to develop stronger early-career and mid-level leaders. Their business operates in two worlds simultaneously. They must execute today's projects flawlessly while also building tomorrow's business through a pipeline of opportunities, customer relationships, and future commitments.
During our discussion, we began talking about accountability and ownership. The leader made a comment that really got me thinking about what he said and then about what I am seeing in the business world these days.
Paraphrasing his observation, he said:
"A few years ago, we struggled to get people interested in owning the work. Today, we're seeing something different. Some employees aren't just avoiding ownership—they're outsourcing ownership. They're handing the thinking over to AI tools and then acting as if the output is their contribution."
That is a very different problem. For decades, leaders worried about employees who weren't engaged enough.
Now they have to worry about employees who appear engaged, appear productive, and appear knowledgeable, while quietly allowing technology to do the intellectual heavy lifting.
Before we can solve that challenge, we need to answer a more fundamental question.
What Does "Owning the Work" Mean Today?
Owning the work does not mean doing every task yourself. It never did.
Great leaders delegate. Great teams collaborate. Great organizations leverage tools. Owning the work means accepting responsibility for the outcome.
It means:
- Understanding the problem deeply.
- Applying judgment rather than blindly following recommendations.
- Being accountable for the consequences of decisions.
- Anticipating risks before they become crises.
- Continuously improving the result.
- Being able to explain the "why" behind the answer.
In practical terms, if AI helps create a proposal, analyze a dataset, write a report, or develop a project plan, the employee still owns the final product.
If the recommendation fails, "the AI said so" is not a defense. Ownership means standing behind the work because you understand it well enough to challenge it, improve it, and defend it. And that's where many organizations are beginning to encounter problems.
The New Challenges of Ownership in an AI-Driven World
“The Illusion of Competence”
AI can produce impressive-looking work in seconds. The presentation looks polished, the spreadsheet looks sophisticated, and the analysis sounds intelligent.
The problem is that appearance and understanding are not the same thing.
Many leaders are discovering that employees can now produce executive-level outputs without developing executive-level thinking.
When questioned beyond the surface, the understanding often falls apart.
The Erosion of Critical Thinking
Critical thinking is like a muscle. If you stop exercising it, it weakens. Many professionals (young AND old) have become exceptionally skilled at prompting AI systems but have not yet developed equal skills in evaluating the answers.
Instead of asking:
"Is this right?"
They ask:
"Does this look right?"
Those are very different questions.
Reduced Intellectual Curiosity
One of the great benefits of struggling through a problem is that the struggle itself creates learning. You ask questions, you explore alternatives, and you discover connections.
When AI instantly provides an answer, there is a temptation to skip the exploration phase entirely. The result is efficiency without understanding.
Accountability Becomes Blurred
When projects succeed, everyone is happy. When projects fail, ownership becomes less clear. Was it the employee's decision? The team's decision? The manager's decision? The AI recommendation?
Organizations that fail to establish accountability standards around AI use will increasingly find themselves trapped in this gray area.
Leadership Development Slows Down
This may be the biggest risk of all. Future leaders are developed by solving difficult problems. They learn through analysis, mistakes, judgment calls, and experience. If AI consistently removes the difficult thinking from the process, organizations may accidentally create a generation of managers who can operate technology but struggle to lead people, make decisions, and navigate ambiguity.
And leadership has always been an exercise in ambiguity.
Five Things Advanced Leaders Can Do Today
The good news is that AI is not the enemy. The challenge is not preventing people from using AI. The challenge is teaching people how to own the work while using AI.
Here are five practical ways leaders can do exactly that.
1. Require People to Explain Their Thinking
Instead of reviewing only the answer, review the reasoning.
Ask questions such as:
- How did you arrive at this recommendation?
- What assumptions did you make?
- What alternatives did you reject?
- What risks concern you most?
If someone truly owns the work, they can explain the thinking behind it.
If they cannot, they may be owning the output but not the process.
2. Make Judgment More Important Than Analysis
AI is becoming very good at analysis. Humans still own judgment. Leaders should increasingly evaluate employees based on how they interpret information, prioritize trade-offs, and make decisions—not simply on how much information they can generate.
The future competitive advantage is not access to answers. It is the quality of judgment applied to those answers.
3. Reward Intelligent Challenge
One of the most dangerous phrases in business may soon become:
"The AI recommended it."
Create a culture where employees are expected to challenge recommendations. Whether the recommendation comes from AI, a consultant, a senior leader, or a spreadsheet, thoughtful skepticism should be rewarded. Ownership requires the courage to question.
4. Build "Human Checkpoints" Into Major Decisions
For significant projects, investments, customer proposals, or strategic initiatives, require employees to identify:
- The key assumptions.
- The greatest risks.
- What could go wrong.
- What data may be missing.
These checkpoints force deeper engagement and prevent blind acceptance of AI-generated outputs.
5. Teach Ownership as a Leadership Skill
Ownership is not a personality trait. It is a learned behavior. Organizations should explicitly teach employees that using AI does not transfer accountability.
The person presenting the recommendation owns the recommendation. The person signing the proposal owns the proposal. The person leading the project owns the project.
Technology can assist the work. It cannot own the work.
Final Thoughts
As I reflected on my conversation with this construction and project management leader, I realized that we may be entering a new chapter of leadership development.
For years, leaders struggled to get employees to take ownership. Today, many employees appear to be taking ownership while quietly outsourcing the most important part—the thinking. The organizations that thrive over the next decade will not be the ones that simply adopt AI the fastest. They will be the ones that develop leaders who know how to leverage AI while still maintaining curiosity, judgment, accountability, and ownership.
Because at the end of the day, customers don't hire AI.
They hire people.
And when something goes wrong, they still expect a human being to step forward and say:
"I've got it. This is mine. I'll take care of it." That, now more than ever, is what owning the work really means.
