Leadership in the Age of AI: Insights from Dr. Rogayeh Tabrizi

In this transformative era of AI, effective leadership necessitates imaginative vision and a deep understanding of AI’s capabilities. Leaders must develop a nuanced understanding of how AI can be leveraged to enhance decision-making, strategy, and operational efficiency while navigating the ethical and societal implications.

Likewise, as AI takes on more work, the leader must ensure that these technologies serve the greater good—amplifying human potential, rather than diminishing it.

To explore this aspect further, we spoke with Rogayeh Tabrizi, PHD, the founder and CEO of Theory+Practice, a technology company with deep expertise in AI and data. In this interview, she shares her journey towards becoming an AI leader and emphasizes why professionals should embrace AI enthusiastically but with proper caution.

Q. Please share some details about yourself and what drove you toward this position of being an AI leader or an AI expert?

Tabrizi: I didn’t begin my career in AI. My academic path started in particle physics, and I later earned a PhD in economics focusing on game theory. That multidisciplinary journey—from theoretical physics to behavioral science—shaped how I see problems, especially in organizations where silos and misaligned incentives often prevent real progress.

I’ve worked with teams across disciplines, industries, and cultures throughout my career. One consistent theme I noticed was the disconnect between executives and technical teams, between strategy and implementation, and most importantly, between companies and their customers. That conflict was the birthplace of Theory+Practice.

What drew me to AI wasn’t the technology itself, but its potential to bridge those gaps. Classical algorithms are built on assumptions of rationality, and human behavior is anything but rational. Biases, social dynamics, and emotional context shape it. To capture that complexity, we need systems that continuously learn from the unpredictable patterns of real people.

For me, AI is a profoundly human tool. It’s a way to scale understanding of consumers, colleagues, and systems. And when we combine that with behavioral economics and game theory, we’re not just predicting behavior—we’re designing for it.

Q. Can you tell us something about your latest book, ‘Behavioral AI’? How is it helpful for organizations and tech leaders?

Tabrizi: Behavioral AI is a guide for anyone navigating complexity—whether in a Fortune 500 company or a scrappy startup. The book explores how we can use data, AI, and behavioral science not just to build better models, but to ask better questions.

It’s filled with real-world examples of organizations struggling to extract value from their data—not because they lack technology, but because they lack alignment. Leaders are often handed dashboards and predictions, but without a clear understanding of the assumptions behind them. This book helps bridge that gap by providing frameworks to align intuition, data, and decision-making.

Tech leaders will find strategies for identifying “minimum viable data,” reducing noise and bias in AI systems, and combining machine learning with behavioral insights to uncover deeper patterns. Ultimately, the goal is not just automation, but transformation—of how we understand customers, design systems, and lead teams.

Q. The need to reduce bias in AI is becoming increasingly important as the use of artificial intelligence increases. What steps can be taken to keep algorithms free from discrimination?

Tabrizi: Reducing bias in AI starts long before the model is trained. It begins with asking the right questions—what outcomes are we optimizing for, whose behavior are we modeling, and what data are we excluding?

Bias often enters through unrepresentative or incomplete datasets. So one key step is to identify and address data blind spots, particularly where data reflects historical inequities or fails to capture marginalized voices. But bias isn’t just in the data; it’s also in the framing of the problem. We must interrogate assumptions, include diverse perspectives, and establish multidisciplinary teams that bring technical and contextual knowledge.

At Theory+Practice, we also advocate for continuous learning. Algorithms must be tested, monitored, and iteratively improved with real-world feedback. Transparency and explainability help ensure trust, not just in the model, but in the process behind it.

Q. Could you shed some light on ‘the interaction between human judgment and AI’?

Tabrizi: AI should never be viewed as a replacement for human judgment—it’s a partner. One of the biggest myths in data science is that more data equals more clarity. But often, more data just means more noise.

The most powerful insights emerge at the intersection of human intuition and machine intelligence. Human judgment provides the context—the ability to see what isn’t in the data, to ask the unexpected question, to make sense of ambiguity. AI, in turn, offers scale, pattern recognition, and consistency.

We use AI not to override judgment, but to inform it—especially when dealing with dynamic systems like consumer behavior. Our best outcomes happen when AI augments decision-making, helping leaders become more curious, not just more efficient.

Q. You have worked with different people and have spoken on various platforms/forums on technology. While talking to clients, what topics did you feel needed more clarification in terms of AI?

Tabrizi: The biggest confusion isn’t about algorithms—it’s about assumptions.

Many organizations invest in AI, expecting it to deliver ready-made answers. But the real work lies in defining the right problems. Clients often struggle to articulate what success looks like or what they’re optimizing for. They may confuse correlation with causation, or expect AI to deliver certainty in systems filled with human unpredictability.

It is crucial to clarify what AI can and cannot do and what it needs from humans to succeed. Another common point of confusion is the difference between being data-rich and insight-rich. It’s not about having more data but identifying the data that matters. That’s where techniques like “minimum viable data” come in—using just enough of the right data to move forward with clarity.

Q. For young professionals/ leaders who are learning about AI, how would you recommend they begin their study? What topics should they focus on?

Tabrizi: Start with curiosity, not just code.

Of course, technical skills like statistics, programming, and machine learning are essential—but they’re not enough. What sets great AI leaders apart is their ability to connect disciplines, question assumptions, and understand human behavior.

I recommend starting with three pillars: First, foundational math and statistics. Second, machine learning and model design. Third—and often overlooked—is behavioral economics and decision science. Understanding why people do what they do is just as important as predicting what they might do next.

Also, practice asking better questions. The ability to frame a problem well is the most underrated AI skill. And finally, seek out real-world problems to solve. It’s in that messiness that you’ll learn the most.

Q. What is your opinion about the future of AI and the role of women leaders in shaping it?

Tabrizi: The future of AI is not just technical—it’s ethical, behavioral, and deeply human. We’re entering a phase where AI will increasingly shape decisions across sectors, from healthcare to finance to education. That means we need more diverse leadership than ever.

Women—and especially women from multidisciplinary backgrounds—bring invaluable perspectives to this work. They’re often trained not just to build systems, but to question them. To listen, synthesize, and design with empathy. That’s exactly what AI needs right now.

Leadership in AI isn’t just about optimizing algorithms—it’s about shaping the questions we ask, the problems we prioritize, and the systems we build. Women leaders are uniquely positioned to create AI that is not only powerful but principled and inclusive.