How to Use AI in Succession Planning Without Losing the Human Element

Where AI can make succession planning easier

AI can make succession planning easier. It can help organize information, compare job requirements, identify development gaps, create questions and help leadership teams take a more organized look at their succession pipeline.

I think there's a lot of value in that. The concern is what happens when a tool that started out helping leaders organize information gradually becomes something they rely on to tell them who has potential, who's ready and who should be next. Those aren't simply data questions. They're leadership decisions.

The answer isn't keeping AI out of succession planning. It's figuring out where it can make the process better while making sure leaders remain responsible for the people decisions. AI should help with the analysis. Leaders should make the decisions.

Succession planning has always involved a lot of moving parts. Which positions are critical? What happens if someone leaves unexpectedly? Who could step into that position? How ready are they? What experience are they missing? What knowledge needs to be transferred? What will that job require three or five years from now?

AI can help leadership teams work through some of this much faster, which may be particularly useful for smaller and midsize organizations that don't have large HR departments or sophisticated talent-management systems.

For example, AI can help develop criteria for identifying critical positions. It can help leaders think through how difficult someone would be to replace, what knowledge might leave with that person, which customer or stakeholder relationships depend on them and what would happen operationally if the position suddenly became vacant. The technology can help organize the assessment, but leadership still has to determine which positions really matter to the organization.

It can also help create a profile of what a future leader will need. Rather than simply looking at the person doing the job today and trying to find someone similar, leaders can ask what that position will need three or five years from now. That may produce a very different picture.

AI can also be useful when looking at readiness and development. Once you've identified a possible successor, you can compare that person's experience with what the future position will require. What experience is missing? What skills need work? Would a cross-functional assignment help? Would mentoring, a temporary leadership responsibility or exposure to another part of the business give you better information about the person's readiness?

That's where I think AI becomes particularly useful. It helps us ask better questions and gives us more options to consider.

Use the tools, but keep the leadership decisions with the leaders.

Where AI can get us into trouble

One of my concerns is that AI can make incomplete information look much more complete than it really is.

Think about the information organizations have on their employees. Some managers write detailed performance evaluations. Others barely complete them. Some employees get stretch assignments and exposure to senior leadership. Others do equally valuable work but don't get those opportunities. One person may have years of documented accomplishments while another has very little information in the system.

AI can analyze the information that's there. It can't automatically know what's missing or why it's missing.

There's also a risk of using yesterday's leaders as the model for tomorrow's. If we're feeding AI historical information about people who succeeded in the organization, it's reasonable that it will find people who look similar to them. But the organization may need something very different going forward.

Future leaders may need to be comfortable with AI, new technology, changing workforce expectations, new business models or greater uncertainty. Instead of simply asking who looks like the people who succeeded here before, we need to ask who can lead the organization we're becoming?

Be careful when the numbers look too precise

Leadership potential isn't an exact science, and putting a number on something doesn't necessarily make it more accurate.

If a system tells me someone is "82% ready" for a senior leadership position, I'd want to know exactly what went into that number. A person may have all the technical qualifications and still struggle to develop other people. Someone else may have tremendous potential but need more experience making decisions when there isn't a clear answer. Another person may look perfect on paper and have absolutely no interest in the job.

The same caution applies to bias. AI doesn't automatically eliminate bias just because a recommendation came from technology. If the information going into the system reflects inconsistent evaluations, unequal opportunities or old assumptions about what a successful leader looks like, those problems can show up in the results.

That's why a polished recommendation shouldn't automatically be treated as an objective one.

Build human review into the process from the beginning

Human review shouldn't happen after AI has already produced an answer. It should be part of the succession process from the beginning.

Start with the business. Where is the organization going over the next three to five years? What challenges are coming? What capabilities will leaders need that may not be as important today? AI can help leadership teams explore possibilities, but the people running the organization need to decide which assumptions make sense.

Then use AI where it saves time. Let it help organize critical-role criteria, competency frameworks, development questions, readiness criteria and knowledge-transfer needs. Once that work is done, bring in what the data doesn't tell you.

What have leaders actually observed about the people being considered? How does someone respond under pressure? Do people trust them? Can they influence people they don't supervise? Do they develop others? How do they react when they're wrong? Do they accept feedback? Can they think beyond their own department?

Those are things leadership needs to discuss.

And we should challenge our own thinking too. Are we favoring the people we know best? Are we mistaking confidence for competence? Are quieter leaders being overlooked? Have all of the candidates had similar opportunities to demonstrate what they can do? Are we evaluating future potential or simply rewarding past performance?

AI may help us catch some of those assumptions. Leaders should be willing to question the AI results as well.

Give people an opportunity to prove themselves

A succession plan shouldn't end with names in boxes.

If you think someone has leadership potential, give them an opportunity to demonstrate it. Put them in charge of a cross-functional project. Let them solve a difficult business problem. Give them exposure to another part of the organization. Have them present to senior leadership or ask them to develop someone else.

Then pay attention to what happens.

This gives the organization something much more useful than speculation. You get actual evidence of how the person handles greater responsibility and where they may still need development.

AI can help identify possible development experiences. The experience itself is where you learn about the person.

Don't forget about employee information

Succession planning involves some of the most sensitive information an organization has about its employees. Performance, potential, readiness, development needs, career aspirations and leadership discussions shouldn't automatically be entered into an AI system simply because doing so makes the analysis easier.

Organizations need clear rules about which AI tools are approved, what information managers can enter, how employee information is protected and what should never be put into an external or public AI tool.

Managers need to understand those rules before they begin using AI for succession planning. Responsible AI use isn't just an IT concern. It's part of responsible leadership.

Before you add AI, get the succession process right

If an organization doesn't have a good succession process, AI isn't going to fix that.

Start by identifying the positions that are truly critical to the organization's continuity. Determine what those positions are likely to require in the future. Establish consistent ways of looking at performance, potential, readiness, development needs, career interests and critical experience.

Then decide where AI actually adds value. Maybe it saves time organizing information. Maybe it helps identify gaps or development possibilities. Maybe it gives the leadership team questions they hadn't considered.

Also decide where the boundaries are. What information can be used? Which tools are approved? Which decisions require HR, legal or senior leadership involvement? Most importantly, who is responsible for the final decision?

The answer should never be, "That's what the AI recommended."

If leaders are making decisions about people's futures, they need to understand those decisions and be willing to take responsibility for them.

AI doesn't replace the succession conversation

I don't think organizations have to choose between traditional succession planning and AI. There's room for both.

Use the technology to make the work easier. Let it organize information, identify gaps, generate development ideas and challenge some of your assumptions. Then bring the leadership team together to do what technology can't do: talk about the people, understand what the organization needs, create opportunities for future leaders and make the difficult decisions.

The purpose of succession planning hasn't changed. You still want capable people prepared to step into important roles when they're needed.

AI gives us another set of tools to help us get there. Use the tools, but keep the leadership decisions with the leaders.

Sources

National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF 1.0). Guidance for organizations on managing AI risks, including accountability, transparency, privacy and harmful bias. https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10?utm

National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. Additional guidance addressing risks associated with generative AI, including data privacy, bias and human oversight. https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence?utm

U.S. Equal Employment Opportunity Commission (EEOC). Employment Discrimination and AI for Workers. Information about how federal employment discrimination protections apply when AI and automated systems are used in employment decisions. https://www.eeoc.gov/sites/default/files/2024-04/20240429_Employment%20Discrimination%20and%20AI%20for%20Workers.pdf

U.S. Department of Labor — Artificial Intelligence Literacy Framework. Released February 13, 2026, it focuses on helping employers and workers develop the skills needed to use AI effectively and responsibly in the workplace. https://www.dol.gov/newsroom/releases/eta/eta20260213?utm