By: Matt Emma
Today’s tech sectors are increasingly defined by automation and AI integration. Approximately 72% of organizations today implement AI for their business functions, integrating it within their workflows to accelerate delivery, quantify outputs, and track tool usage with precision. Yet beneath these gains, Eva Kutzler, an executive coach and organizational psychologist, observes that teams execute faster while thinking less expansively.
She identifies this as a decline in “cognitive agility,” or what she often calls cognitive range, referring to the mental flexibility required to move fluidly between strategy execution and foresight, as well as making judgements under pressure.
“AI is definitely an extraordinary amplifier, but leaders are unintentionally turning it into a thinking substitute,” Kutzler explains. “The people with the strongest ownership and judgment don’t thrive in environments where success is measured by tool compliance. AI automated workflows are important, but so are critical thinkers who can use them to maximize profits.”
Research has often linked cognitive flexibility to better problem-solving and innovation during technological intervention. A study in the National Library of Medicine found that strong cognitive flexibility can improve problem-solving by up to 35% in complex environments. Cognitive agility within teams enables members to navigate complex challenges and integrate diverse perspectives, resulting in enhanced team performance and innovative outcomes. To Kutzler, these insights reveal a growing misalignment between how performance is measured and what sustains long-term adaptability.
“Weekly AI usage quotas and adoption metrics look productive, but cognitively agile employees are asking, ‘So what? Now what?’ They care about impact and long-term consequences. If that space for critical thinking disappears, they leave,” she says. In her view, the unintended outcome becomes a compliance-driven culture that may retain reliable operators but gradually lose innovative thinkers.
Kutzler also points out that conventional performance metrics, such as consistency and speed, do not fully capture cognitive agility. Instead, she encourages leaders to look for evidence of mental dynamism instead. She says, “In interviews, ask about ambiguous situations where the rules changed unexpectedly. Cognitively agile people are naturally predisposed to describe pivots and reiterations. They lead with that.”
Career trajectories, she notes, often reveal the same trait. According to her analysis, professionals with nonlinear career paths across industries or problem domains tend to demonstrate broader contextual reasoning. Internally, she advises leaders to observe who consistently questions long-term implications, asking about accelerations that create bottlenecks or altered outcomes due to slowed adoption. “Agile thinkers instinctively run scenarios in their heads. They think beyond the task and into the system, always looking at the meta-awareness of things,” Kutzler shares.
Cognitive agility, in her view, can be developed through deliberate leadership practices. She points to perspective switching in coaching conversations, as it can encourage employees to evaluate decisions from multiple perspectives. High-fidelity planning, such as best-case, likely, and failure scenarios, can further strengthen strategic foresight. Kutzler also highlights that structured decision post-mortems help examine overlooked signals and flawed assumptions, “They need to always look back, constantly assessing what signals were ignored, what surprised them, and what objectives were missed,” she says.
Kutzler recommends recalibrating incentives to focus on three indicators: judgment, adaptation, and reflection. “Ask what someone critically analyzed, how they adapted a process intelligently, and what they learned,” she says. “That shift signals that thinking carries more value than box-ticking.”
Through her advisory work, Kutzler begins with what she calls an operating system audit, typically starting with the leader. Using cognitive preference assessments, she maps how individuals process information and make decisions in complex environments. She explains, “Personality assessments tell you who someone is. Cognitive preference data tells you how they think, which is far more actionable for leadership.”
She then evaluates team structures, coaching rhythms, and organizational frameworks to understand if the environment supports cognitive strengths. Client engagements range from intensive six-month sprints for newly promoted VPs to long-term advisory relationships and team activation sessions, which address organizational design and leadership effectiveness.
Ultimately, Eva Kutzler believes that short-term gains from strict AI mandates often appear strong for measuring productivity with data, but over time, organizations risk narrowing the thinking capacity required to apply these tools intelligently. She remarks, “AI should expand human judgment. The companies that thrive will be the ones that protect cognitive agility because that’s what allows leaders to ask better questions than technology can ever answer.”
Disclaimer: The opinions expressed in this article are those of Eva Kutzler and do not necessarily reflect the views of any organizations she is affiliated with. The information provided is for general informational purposes only and is not intended as professional advice. Readers are encouraged to seek personalized guidance based on their specific circumstances.
