20 leaders: Data or gut instinct?

balancing data – A Fast Company Impact Council asked 20 leaders how they balance data-driven decision-making with gut instinct—arguing that the two aren’t enemies, but inputs that must be combined, timed, and validated as markets move faster.

The question lands with a familiar workplace tension: when the numbers are there, do you trust them—or do you listen to something less measurable, like instinct and lived experience?

In a set of responses collected by the Fast Company Impact Council. leaders across product. operations. music. insurance. education. and technology describe a shared stance: data and gut instinct aren’t a choice between certainty and imagination. They’re pieces of the same process, and the hard part is knowing when each should lead.

Peter Smart, Fantasy, puts it plainly: no metric delivers certainty, so decision-making has to draw on “all of the available information”—data, the team, long-range objectives, and lived experience—to make the “smartest decisions possible.”

Thomas Scott of Wrike says he sees the same gap from a different angle. His company is “completely committed” to data collection across functions. customers. products. and employees. but the data “almost never provides ‘the answer.’” He compares it to talking with his high school daughter about college and jobs: you gather as much information as you can “in the time we have. ” then you decide with imperfect information—while using a gut-based sense of risk to balance that decision-making clock.

That timing theme shows up repeatedly. Lior Div of 7AI argues that data can show what worked. but it rarely tells you what’s next. especially as “agentic AI” accelerates change. His approach is to use data to frame the problem. then trust instincts and “move fast.” Hesitate. he warns. and “the window closes.”.

Other leaders describe the limits of forward-looking confidence. Andrea Montecchi from Oliver Wight argues that data is often richer in the “rear-view mirror” than it is ahead. Domain knowledge—what must be true about the data. the dependencies. and the risks behind a planned outcome—should support the right questions and qualifications. rather than negate the inferences of high-quality data.

In beauty, Kim Wileman of No Makeup Makeup says the decision wasn’t spreadsheet-driven. She notes that she’s been in beauty for 30 years and has watched brands over-index on numbers and miss cultural moments. For her. data is the starting point. but “gut tells you what’s possible”—and the leap toward “less is best” came from decades of watching what women actually want. when the data gave her enough confidence to move.

Tammy Nelson at CONQUERing describes a similar structure: start with data to create “guardrails and consistency. ” such as using sales trends. historical performance. and “launch velocity” to plan inventory for new product launches. Yet she says there are moments—especially in creative. fast-moving businesses—when experience and intuition matter so much that team judgment can override the model if something about the opportunity feels different.

Keith Mann of Pella Corporation focuses on a specific use case: in innovation spaces where data doesn’t exist yet. start with instinct. Then use data to validate rather than decide. He describes beginning with a deep understanding of customer pain points to identify opportunities and shape concepts. then validating with data. while keeping instinct as the directional force—citing early e-commerce investments and product innovation as examples of that balance.

Logan Mulvey of GoDigital Music frames decision-making in cultural terms. With globalization of music comes more data than ever. but data “can’t predict a cultural moment.” When picking a single or a major campaign. he uses data to understand the landscape and relies on instinct to identify the emotional “X-factor” that will resonate with listeners.

The responses also reflect an accelerating role for machines. Pierre Le Manh of the Project Management Institute challenges the easy version of the narrative that “humans bring judgment. machines bring data.” He says the reality is more complex: in a growing number of decisions. AI is becoming better than people. and leaders’ instinct to verify can actually degrade performance. The leadership skill he highlights is “discernment”—knowing which decisions to delegate to AI. which to take with it as a thinking partner. and which to keep human—because “deciding is itself part of the answer.”.

Tara Zedayko of Ollie takes a feedback-loop view. Data is the foundation, but not the whole picture: it sharpens questions, reveals patterns, and reduces bias. Gut instinct—grounded in experience—helps interpret ambiguity and act when data is incomplete. Then measure outcomes rigorously, so the loop strengthens both models and intuition over time.

For Darren Person of Cengage, the risk is clear: instinct without validation becomes guesswork. He starts with an instinct. uses data to pressure-test it. and if the data contradicts his theory. he digs deeper into why—treating the goal as making better decisions over time rather than choosing between data and instinct.

Tony Bedard of Frontier Co-op argues that many business calls are neither purely analytical nor purely human. He describes situations where performance data can signal you can’t make money. but you may still move forward because it builds loyalty and trust for future opportunities. He also describes the opposite: considering discontinuing a product line based on poor performance. while a trusted employee believes they can turn it around—betting on the person rather than the numbers. Very few decisions, he says, are “purely black and white.”.

Jaymes Black of The Trevor Project adds another layer: experience fortifies instinct over time. Data can show trends and averages, but it can’t always provide the personal, human perspective. He says he looks at the information in front of him and also thinks back on previous. more personal interactions relevant to the decision—so gut instinct is strengthened by lived experience.

R. Ethan Braden of Texas A&M University describes the relationship as partnership. Data keeps leaders honest by showing what is working and where attention is moving. but it often reflects what has already happened. Judgment and taste help reveal what may matter next—especially in an AI age where average answers are easier to produce.

Vineet Mehra of Chime treats data and instinct as complements, not competitors. Data tells what’s happening and helps optimize at scale; instinct comes from being immersed in culture and understanding people. He describes a “performance storytelling” model that blends culturally relevant campaigns with performance marketing. using AI and analytics to validate and scale ideas. while insisting the spark comes from human creativity. intuition. and ingenuity.

Yet even advocates of data-intuition balance draw lines around misuse. Are Traasdahl of Crisp warns about “data theater,” where numbers are used to justify decisions already made emotionally. She says she uses a cycle where gut generates a hypothesis. data stress-tests it. and AI makes the cycle fast enough to matter—but she is careful about confirmation bias with a spreadsheet.

That skepticism about numbers-only thinking echoes in David Klanecky’s statement at Cirba Solutions. In battery recycling and critical mineral refinement—where rapidly evolving technology. policy. and demand intersect—data alone can’t capture what’s emerging. The approach is to incorporate data to validate direction and manage risk. while relying on teams’ decades of experience and gut instincts to move with clarity.

Adam L’Italien of Liberty Mutual Insurance describes the “productive tension” between the two inputs as the real power in decision-making. Data can surface truths that push back on what leaders think they know. Instinct can make them curious when a dataset feels incomplete. Leaders who balance both build a habit of pressure-testing in both directions—asking what the data says. and what it isn’t capturing—so each decision sharpens the next one and judgment compounds across the business.

Khozema Shipchandler of Twilio narrows the use case: he uses data to validate the current landscape, reserving gut-driven decisions for “non-linear bets” where numbers haven’t caught up to market shifts.

Pawan Verma of Cencora offers a final balancing frame. In an environment of increasing noise and accelerating change. leaders often drift toward extremes: gut-first. reactive decisions on one side. and data-first focus that can become too narrow on the other. His job. he says. is to find familiarity as a middle response—leading with heart and humility. arriving open-minded. widening the data and perspectives considered. and staying attuned to those he leads. Familiarity, he adds, is re-earned through unlearning, self-awareness, and belief in humanity’s unlimited potential for growth.

Across the full set of responses, the lesson is not that data is weak or gut is unreliable. It’s that certainty is rarely available—and the most effective leaders design decisions around that reality: using data for guardrails. validation. and measurement. while treating instinct as direction. pattern recognition. and interpretation when the next outcome can’t be fully graphed yet.

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