AI Bias and Succession Planning: What Leaders Need to Know

Technology doesn't make a decision objective

Artificial intelligence has the potential to improve succession planning. It can analyze information across a larger group of employees, identify patterns, compare experience against future role requirements, surface overlooked candidates, and help leaders apply more consistent criteria. Those are meaningful advantages.

But there is a dangerous assumption organizations need to avoid: a decision doesn't become objective simply because technology is involved. AI systems work with information created by people, collected through organizational processes, and interpreted through models designed and used by people. If bias already exists in those processes, AI can reproduce it — and potentially apply it across a much larger population.

The National Institute of Standards and Technology (NIST), an agency of the U.S. Department of Commerce, specifically identifies bias as an important AI risk. Its AI Risk Management Framework recognizes that bias can arise from human decisions, organizational systems, and the data and technology itself. NIST also cautions that reducing complex human behavior to measurable data can remove important context.

That's especially important in succession planning, because we're not simply analyzing numbers. We're making judgments about people, potential, opportunity, development, and the future of an organization.

Bias that used to live in one executive's head can now be applied uniformly across the whole pipeline.

Why this matters now

Consider a leadership team trying to identify potential successors for several critical positions. Traditionally, executives might sit around a table and discuss the employees they know. That approach has obvious limitations. Who's visible to senior leadership? Who has an influential sponsor? Who received the best assignments? Who works in the function that historically produces executives? Who reminds existing leaders of themselves?

AI offers the possibility of widening that field. Instead of relying entirely on executive memory, an organization could potentially analyze information across hundreds or thousands of employees. That sounds more objective. But imagine that the historical data shows that most executives followed a particular career path. AI may identify that pattern, then identify current employees whose careers most closely resemble it.

The analysis may be technically accurate. The succession conclusion may still be wrong. Perhaps people outside that traditional path never received comparable opportunities. Perhaps previous leaders favored certain experiences. Perhaps the business will need completely different leadership capabilities in five years. Or perhaps the organization's historical decisions already contained bias. The AI didn't create that history — but it can learn from it.

This matters from both a leadership and employment perspective. The U.S. Equal Employment Opportunity Commission (EEOC) has made clear that employment selection procedures can create discrimination concerns when they disproportionately exclude protected groups. The agency has also addressed the potential for AI and algorithmic tools used in employment decisions to create discriminatory outcomes. For leaders, the lesson is straightforward: using AI doesn't transfer responsibility for a people decision from the organization to the technology.

Where bias enters the data

AI can only analyze what it has access to. That's the first place leaders need to look. Imagine two equally promising employees. Employee A has worked for a manager who actively develops people — stretch assignments, mentoring, cross-functional projects, leadership training, executive presentations, and increasingly significant responsibilities. Employee B performs extremely well but works for a manager who doesn't focus on development, and receives fewer opportunities and less exposure.

Compare their records and Employee A appears to have considerably more evidence of leadership potential. And that's true. But why? Opportunity creates data. An AI system may accurately determine that one employee has more leadership experience. It cannot automatically determine whether that difference represents greater potential or greater opportunity.

The same issue appears throughout organizational data. Performance evaluations: some managers write thoughtful, detailed reviews; others write three sentences. Some rate almost everyone highly; others believe nobody deserves the top rating. If AI analyzes those evaluations without accounting for the differences, inconsistent management practices become inputs into supposedly consistent talent analysis.

Promotions may appear to provide objective evidence of capability, but promotions were themselves human decisions. High-visibility assignments become evidence supporting readiness — yet someone made the original decision about who received the opportunity. Leadership assessments can provide useful information, but shouldn't automatically become truth; organizations need to understand what an assessment measures, whether it is appropriate for the intended purpose, and how it fits with other evidence.

The principle is simple: before trusting an AI conclusion, understand where the underlying data came from.

Where bias enters the prompt

Bias doesn't only live in databases. It can enter through the person asking the question. Consider prompts like “Why isn't this employee executive material?” or “Explain why this person isn't ready to lead” or “Which of these employees looks most like our successful executives?” Each question already contains an assumption, and AI is then being asked to work within it.

Compare that with: “What evidence should we examine before determining whether this employee has potential for greater leadership responsibility?” Or: “What information might be missing from our assessment of these succession candidates?” Or: “What alternative explanations should we consider for the differences in these employees' career histories?” Or: “Could any of our selection criteria unintentionally favor employees who have historically received greater visibility or opportunity?”

Those prompts do something different. They use AI to challenge the leadership team's thinking instead of simply validating it.

Fluent doesn't mean objective

Generative AI creates another risk. Its answers sound good. They're organized. They're confident. They're often remarkably articulate. That can make an AI-generated talent analysis feel more authoritative than the underlying evidence warrants.

A beautifully written explanation of why someone appears to be a strong successor is still only as useful as the information supplied, the criteria used, the assumptions built into the process, and the quality of human review.

This is another reason NIST's work is useful for leaders. The NIST AI Risk Management Framework encourages organizations to consider accountability, transparency, privacy, reliability, explainability, and the management of harmful bias when using AI. For succession planning, that leads to an important rule: never confuse confidence of presentation with confidence of evidence.

AI can reinforce the past when you need to prepare for the future

Historical data naturally describes the organization that already existed. Succession planning is supposed to prepare the organization that will exist next.

Suppose your most successful executives over the past 20 years built their careers through operations. An AI system might reasonably identify operations experience as associated with executive success. But what if your organization's next decade will depend upon digital transformation, AI adoption, global partnerships, new distribution models, or completely different customer expectations? Replicating historical leadership profiles could make succession planning less forward-looking rather than more.

Don't only ask what characteristics successful leaders had. Ask what successful leaders will need. Those aren't necessarily the same question.

Review practices that catch it

The answer isn't to remove people from succession decisions and trust algorithms. Nor is it to reject AI and return to entirely subjective talent conversations. Both humans and AI can introduce bias. The better solution is a process in which each helps challenge the other.

Define criteria before discussing names. Determine what the future role requires before discussing who should fill it — the experience, capabilities, leadership behaviors, and future-facing competencies that matter. This reduces the temptation to design criteria around a favored candidate.

Ask where the evidence came from. For every important data point, ask how the information was created: a performance rating, an assessment, a manager recommendation, a promotion, a stretch assignment? Then consider whether employees had comparable opportunities to generate that evidence.

Examine who was left out. Don't only review the people AI identifies. Ask who wasn't identified, and why. Which employees have less data? Who works outside highly visible functions? Who hasn't received executive exposure? Who may have demonstrated leadership informally rather than through title or assignment? Sometimes the most important succession discussion concerns the person who isn't on the list.

Require evidence for human opinions too. If an executive says “She's not ready,” ask what evidence supports that. If someone says “He's definitely high potential,” ask the same question. AI recommendations shouldn't escape scrutiny — and neither should executive opinions.

Examine the results. If an AI-supported process influences employment decisions, organizations need to examine the outcomes, not simply assume the process is fair because the same technology was applied to everyone. Consistent application of a biased process still produces a biased process.

Keep a human accountable. Someone needs to own the decision — not the software vendor, not the algorithm, not the prompt. A leader. The organization should be able to explain what information was considered, what judgment was applied, who reviewed the recommendation, and why the ultimate decision was made.

Bias isn't only about who gets selected

Bias can influence development long before a promotion occurs. Who gets mentoring? Who attends leadership programs? Who receives a stretch assignment? Who gets introduced to senior executives? Who gets responsibility for an important customer? Who gets invited into the strategic meeting? Who is told, “I think you could lead this organization someday”?

Those decisions create future leadership candidates. If organizations wait until the final succession decision to examine bias, they're starting too late. A stronger approach examines the entire leadership pipeline.

Because succession planning isn't simply about choosing from the available candidates. It's also about understanding how those candidates became available in the first place.

What to do next

First, define AI's role. Decide whether it will organize information, generate questions, identify patterns, suggest development options, or influence actual employment decisions.

Second, review the data. Understand what information is being analyzed, where it came from, and what may be missing. Third, establish human review: decide who evaluates AI-supported conclusions and who owns the final decision.

Fourth, examine opportunity as well as outcomes. Don't only ask who has the strongest record — ask who had the opportunities to build that record. Fifth, involve HR and counsel where appropriate. The closer AI gets to influencing an actual employment decision, the more important governance and professional review become.

The goal isn't to eliminate human judgment from succession planning. It's to make that judgment more deliberate, more evidence-based, more transparent, and easier to challenge. AI can help — but only if leaders remain willing to question both the technology and themselves.

Don't automate yesterday's assumptions

AI has the potential to make succession planning significantly better. It can widen the talent pool, organize evidence, identify patterns humans miss, challenge assumptions, and help leadership teams have more structured conversations about future talent.

But organizations need to be careful about what they're asking AI to reproduce. If yesterday's leadership decisions contained blind spots, feeding them into tomorrow's technology doesn't remove the problem. It may simply make the pattern easier to repeat.

Use AI to illuminate the succession pipeline — not determine who deserves to move through it.

Sources

National Institute of Standards and Technology (NIST) — Artificial Intelligence Risk Management Framework (AI RMF 1.0): NIST's framework for helping organizations manage AI risks and promote trustworthy and responsible AI use.

NIST AI Resource Center — AI Risk Management and Human-AI Interaction: addresses human and systemic bias, loss of context in AI models, human oversight, and the importance of clearly defining responsibility for AI-supported decisions.

U.S. Equal Employment Opportunity Commission (EEOC) — Employment Tests and Selection Procedures: EEOC guidance addressing employment selection procedures and their potential discriminatory impact.

U.S. Equal Employment Opportunity Commission (EEOC) — Artificial Intelligence and Algorithmic Fairness Initiative: explains the EEOC's focus on ensuring that AI and algorithmic tools used in employment decisions comply with federal anti-discrimination laws.

U.S. Equal Employment Opportunity Commission and U.S. Department of Justice — Employers' Use of Artificial Intelligence Tools Can Violate the Americans with Disabilities Act: addresses the potential for AI tools used in employment decisions, including decisions about performance, pay, and promotions, to result in unlawful discrimination.