The hunt for skilled team leaders has evolved with AI putting a different spin on how candidates are selected. Traditionally, the search came down to CIOs relying on staff recommendations, employment services, and word of mouth to guide the search Now, AI’s ability to rapidly scan and analyze vast amounts of data can reveal qualified team leaders who might otherwise have been overlooked.
Used carefully, AI can bring clarity to the search for leadership talent. When evaluating potential team leaders, an objective view matters, said Jan Varljen, CTO at product management technology firm Productive. “Biases or favoritism can have a bad impact,” he warned. “AI can give you metrics on performance trends, collaboration patterns, skills adjacency and leadership indicators.”
AI excels at identifying patterns across large datasets, such as engagement scores, delivery metrics, peer feedback frequency and project outcomes, Varljen said. “Of course, all of this information should be double-checked.”
Potential pitfalls
Humans should remain the final decision-makers in hiring, promotions and terminations, said Rohan Chandran, chief product and technology officer at executive search firm Guild Talent. “AI doesn’t understand external circumstances, unstated context, team dynamics, hallway conversations, or the informal leadership moments that never show up in a system,” he explained. “Those nuances often shape the real story behind performance and potential.”
Left to its own devices, AI risks creating disparate impact or bias when used to identify potential leaders, said Eric Felsberg, leader of the AI governance and technology industry group at Jackson Lewis, a national employment law firm. “Suppose the AI considers facially neutral criteria when identifying team leaders, but the identifications favor one race, gender, or age range, at disproportionately higher rates than another,” he said. “This is disparate impact or bias, which could have significant legal ramifications.”
Overconfidence in AI output may be the biggest risk associated with the technology, warned Pankaj Dontamsetty, vice president of operations and insights at supply chain services firm Bristlecone. “Models can appear precise and authoritative, even when the underlying data quality is inconsistent,” he explained. If CRM hygiene is weak, skills data is outdated, or hiring history contains inconsistencies, the model will still produce a clean forecast. “Garbage in, garbage out still applies,” Dontamsetty said.
Building guardrails
Organizations must clarify who owns the decision, Dontamsetty advised. “AI can inform decisions, but it should never own them,” he said. Dontamsetty also stressed the need for strong data discipline. “Data quality matters more than model sophistication,” he stated. “Clear rules are needed to determine which data is used, how current it is, and how it’s validated.”
Ensuring transparency and explainability remains critical. “Leaders should be able to understand, question and reasonably explain AI outputs,” Dontamsetty said. “If a recommendation cannot be challenged or interpreted, that’s a red flag.”
He also recommended implementing regular bias reviews. “Models should be evaluated not only for technical accuracy, but also for alignment with organizational values and future direction,” Dontamsetty said. Meanwhile, strict access controls, including role-based permissions, data masking wherever appropriate, and defined visibility boundaries are non-negotiable once AI integrates with core systems.
Felsberg said both developers and end users need to fully understand whether the model is doing what it purports to do. “Validation studies are critical in the face of a claim,” he stated.
In any event, final hiring, promotion, or termination decisions should always be off-limits to AI, Varljen said. “Any action that could produce legal consequences or alter careers should be in placed in human hands.”
IT, HR, and business leaders all have important roles to play, Felsberg said. “The business can set the criteria for [AI] identification while IT develops the model and HR vets the outcome,” he noted. “I would also add legal to determine whether any laws are implicated.”
Final thoughts
Humans must remain in charge of final decisions based on AI recommendations. “Beyond conducting analyses, human judgment should be leveraged to see if the decisions seem correct,” Felsberg said. “For example, if team leader identifications seem to be mostly younger or male, maybe it’s worth a closer look.” Similarly, if the AI model is mostly recommending poorer performers, an issue may be present.
AI should primarily be used to reduce bias and increase visibility, Varljen said. Yet, human judgment still matters. “Picking a team leader is always more about trust and value alignment than just numbers.”
