How AI Is Reshaping Executive Assessment and Leadership Development
September 14, 2026 – As organizations look for better ways to evaluate leadership potential, attention is shifting beyond experience and credentials toward how executives actually think, communicate, make decisions, and respond under pressure. AI is creating new ways to analyze those behaviors at scale, giving executive search firms and their clients additional evidence when assessing qualities such as judgment, adaptability, listening, and collaboration.
The opportunity, however, comes with important limitations. AI-generated insights must be validated, tested for fairness, and considered alongside track record, references, organizational context, and experienced human judgment. Used appropriately, the technology can strengthen the evidence available to decision-makers without replacing the relationships, interpretation, and accountability that remain central to consequential leadership decisions.
This is where platforms such as Simsola AI are beginning to play a larger role, giving organizations and executive search firms another way to evaluate the behaviors and capabilities that are often difficult to measure through traditional assessment alone.
Founded by Tammy Wang, an AI and machine learning executive, and Zachary Van Rossum, an organizational psychologist, Simsola AI measures and develops how people think, communicate, and lead through real conversations, using AI to bring more structured, evidence-based insights to hiring and leadership development.
Dr. Wang recently sat down with Hunt Scanlon Media to discuss how AI is reshaping executive assessment and leadership development, the growing importance of human skills, and the role technology can play in helping organizations make more informed leadership decisions.
Tammy, leadership development has traditionally relied on coaching and classroom learning. How is AI changing the way organizations assess and develop leadership capabilities?
Coaching and classroom learning remain valuable, but organizations often struggle to define leadership capabilities such as empathy, influence, and collaboration in observable, context-specific behaviors and determine whether development efforts are actually changing how leaders perform. AI can support this process by analyzing defined behavioral signals in sources such as workplace training conversations and interview transcripts. When used alongside human judgment, employee feedback, and business data, it can help organizations identify patterns more consistently and at a greater scale than manual review alone. However, AI-generated assessments are not automatically objective or valid. The methodology must be scientifically validated, tested for cultural and demographic bias, evaluated within the context in which it will be used, and supported by appropriate privacy and governance safeguards. Its outputs should be treated as indicators of observable behavior, not definitive judgments about a person’s character or overall leadership ability. Used responsibly, these insights can make coaching more targeted, help leaders track behavioral changes over time, and inform as data input, leadership selection and development decisions. Organizations can also examine whether changes in leadership behavior are associated with outcomes such as engagement, retention, business performance. It can help shape organization’s leadership strategy in a fast changing environment.
Simsola focuses on measuring how people think, communicate, and lead through real conversations. Why are these “human skills” becoming more valuable as AI becomes more capable?
AI is not making human skills newly important. Judgment and human connection have always been the real work of leadership. With the repetitive tasks or the “gruby work” can be removed by AI, leaders can focus more on what matters. Leaders are tasked to decide what matters when the answer is unclear, make trade-offs with incomplete information, and bring teams with them. Productivity and efficiency matter, but they are not the core leadership advantage; they are increasingly supported by tools and systems. As AI takes on more analysis and execution, the difference between leaders will be even less about how much work they can process and more about whether they can identify the right problem, understand people, make sound decisions, and create alignment. The challenge is that companies have become good at measuring proxies when select leaders: past success, experience, credentials, and interview performance. We still have few credible ways to directly measure judgment, communication, and human connection as they actually show up in real conversations. That is the gap Simsola is built to address.
Search firms have become increasingly focused on leadership potential rather than just experience. How can AI help identify capabilities that are difficult to uncover in a traditional interview process?
Identifying leadership potential requires interviews to go beyond surface-level questions and examine how candidates think in real situations. Rather than only asking what they did, many experienced interviewers explore why they made a decision, the trade-offs they considered, how they responded to challenges, and what they learned. This can reveal capabilities that are difficult to assess consistently in a traditional interview such as critical thinking, adaptability, and judgment under uncertainty. It is also important to assess patterns across several realistic scenarios, not draw broad conclusions from one polished answer. AI can help make this process more structured and scalable. It can analyze interview conversations for observable evidence of decision-making, and communication and give recruiters a more consistent, reviewable view across candidates. This can reduce the cost and inconsistency of manual assessment. But AI should be in a role to support human judgment. It must be trained and validated against relevant, real-world examples, regularly tested for fairness and accuracy. It should be used alongside the specific context of the leadership role. Its value is to reduce subjectivity and bias, and also make the evidence behind an assessment clearer, more consistent, and easier for experienced recruiters to evaluate.
Many organizations are investing heavily in AI tools, yet leadership effectiveness often remains the biggest barrier to transformation. What are companies getting wrong when preparing leaders for the AI era?
Companies often prepare leaders to adopt AI tools, rather than to lead AI-enabled change. Many organizations begin with adoption metrics: how many licenses were activated, how often employees use a tool, or how many tokens they consume. These measures can create activity without value. A company can spend its AI budget quickly and still be unable to point to a better customer outcome, faster decision, stronger product or workflow. The deeper challenge is that successful organizations are often built to repeat what worked before. They reward reliable execution, risk management, and delivery against established plans. Those strengths remain important, but they are not enough when AI is changing customer expectations, competitive boundaries, and the economics of work. Leaders must also question assumptions, identify the problems worth solving, run thoughtful experiments, and adjust course as evidence changes.
What does this mean for leadership?
This requires more than AI literacy. Leaders need sharper judgment to decide where AI should, and should not, be used; the curiosity to challenge existing workflows. Communication skills become essential to explain change, hear concerns, and build trust with teams whose roles may be evolving. They need to create enough psychological safety for people to raise inconvenient risks, so they can test new ideas, and learn from failure without being punished for it. Companies also need to avoid a false choice between specialists and generalists. Domain expertise remains essential. But leaders must be able to connect expertise across product, technology, operations, customers, and sales, so the organization can move quickly without making narrow, isolated decisions. The AI era requires leaders to redesign work around outcomes, and help people adapt to the change. Those are capabilities that organizations often say they value but rarely measure. These qualities in leadership effectiveness often become the biggest barrier to transformation.
Conflict management, communication, and collaboration are recurring themes in your platform. Why do you believe these skills will become even more important as AI automates more technical work?
AI will make it easier to generate work, but harder to maintain shared commitment about what work is worth doing and who is responsible for it in a rapidly changing landscape of capabilities. As options multiply and change accelerates, teams need leaders who can invite honest disagreement, resolve conflict fairly, and create commitment around a clear direction. AI can make it faster and less expensive to build. It does not have full context around prioritizing problems with complex trade-offs or how work should be divided as roles evolve. Those decisions require people to communicate clearly and align on shared priorities. This is why conflict management becomes more important in the current time. Healthy disagreement brings forward different perspectives and help build commitment once the decision is made. Strong leaders anchor these conversations in a clear strategy and shared principles. They make space for challenge so much unknown in the market without allowing conflict to become personal or paralyzing. It’s a tough role help teams move forward with transparency in the current work environment.
“The companies that benefit most from AI will not simply be those that generate the most output.”
Anything else?
The companies that benefit most from AI will not simply be those that generate the most output. They will be the ones whose leaders can turn more ideas, more uncertainty, and more diverse viewpoints into focused action. That is why communication, collaboration, and conflict management are not peripheral skills. They are essential leadership capabilities, visible in the real conversations where teams disagree, decide, and act.
Looking ahead five years, how do you see AI reshaping executive search, succession planning, and leadership development? What parts of the process will always require human expertise?
It is difficult to predict exactly how executive search will change over the next five years, but AI is likely to make leadership decisions more continuous and evidence-based. Today, executive search, succession planning, and leadership development are often separate processes. Search firms assess candidates for a role, organizations make succession decisions periodically, and development plans can become detached from how leaders actually perform and the impact of the senior leadership hiring on the internal leadership bench. AI can help connect these processes through a consistent, validated view of observable leadership behavior. In executive search, AI can help structure interview evidence against clear role criteria, identify patterns and inconsistencies, and suggest areas for deeper follow-up. In succession planning, it can help organizations maintain a more current view of leaders’ experience, readiness, development needs, and the capabilities required for future roles. In leadership development, it can turn evidence from real conversations into more specific coaching priorities, such as strengthening judgment under uncertainty, communication, adaptability, or the ability to build alignment. Human expertise will remain essential and take responsibility for the final decision. People must define the organization’s strategy and what leadership success means in context. Human connections build trust with candidates and employees by understanding nuance and exception to conduct sensitive conversations.. AI may reduce administrative work and make evidence easier to compare, but it cannot replace the relationships and judgment required for an important leadership choice. The future is a more thoughtful leadership eco-system, where better evidence supports better human decisions from hiring through development and succession.
Related: How Leading Search Firms Are Turning AI Into Competitive Advantage
Contributed by Scott A. Scanlon, Editor-in-Chief and Dale M. Zupsansky, Executive Editor — Hunt Scanlon Media
