Vishal Talwar is Executive Vice President and Chief Digital and Information Officer at FedEx Corporation, and President of FedEx Dataworks. He brings nearly three decades of experience leading large-scale digital transformations and helping global enterprises apply data, AI, and technology to drive competitive advantage. His career has shaped a clear perspective: successful AI leadership is not defined by technical sophistication alone, but by strong business alignment, disciplined governance, and a human-centered approach to adoption.
Throughout his career, Vishal has focused on how data and AI can improve resilience, efficiency, and growth when applied to real-world problems. A defining moment came during his work with a life sciences company facing a critical imbalance between the growing demand for lifesaving plasma therapies and limited supply. The challenge extended beyond operations to the donor experience itself, with long wait times, unpredictable scheduling, and processes that discouraged repeat visits.
By applying predictive analytics to optimize staffing, deploying personalized collection technologies to increase plasma yield per donor by 11.5%, and using AI-driven forecasting to strengthen supply management, the organization transformed both operations and outcomes. The initiative improved efficiency while directly contributing to saving more lives, becoming a landmark example of purposeful digital transformation.
That experience reinforced Vishal’s belief that empathy and technology must be applied in the right sequence, starting with the human problem, not the solution. Known for his ability to translate complex technology into meaningful business impact, he emphasizes communication, trust, and strategic clarity as the foundations of transformation. Today, Vishal continues to lead FedEx’s digital and data strategy with a focus on turning technology into sustained, human-centered value at global scale.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership requires a blend of technical expertise, strategic vision, and human-centered skills. Empathy, often overlooked in technical fields, is essential. It helps leaders understand real problems before jumping to solutions, avoiding technology built in search of a use case. Empathy also elevates the quality of the solutions themselves. By understanding users’ daily frustrations, workflows, and emotional drivers, leaders and teams build AI tools that solve today’s problems while anticipating tomorrow’s. In the end, our job is to solve problems, and empathy isn’t a soft skill in that work; it’s a strategic one.
“Other core traits include adaptability, as the AI landscape evolves rapidly; strong communication to translate complex concepts into business value; and ethical judgment, ensuring responsible AI practices. Leaders must also excel in change management by driving cultural adoption and aligning AI initiatives with organizational goals.
“Technical skills like data architecture, machine learning, and governance frameworks are essential, but insufficient on their own. Collaboration across IT, operations, and business units is equally important. Focus on user experience is vital because AI succeeds only when people trust and embrace it. Our MOBIUS predictive maintenance platform illustrates this: while predictive models and IoT integration were technical milestones, success came from helping technicians see how the tool made their jobs easier. By pairing analytics with practical workflows, we build trust and accelerated adoption, proving that AI leadership is as much about people as technology.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“One non-traditional piece of advice I’d give is to master the art of storytelling. Technical skills will open the door, but earning trust and influence as a C-suite leader is about making complex ideas simple and telling real stories that resonate. Executives don’t buy algorithms; they buy outcomes. They want to understand how AI impacts revenue, risk, and customer experience, not specifics on how a model works.
“In my experience, framing data initiatives in terms of business value and human impact rather than technical jargon has been transformative. For example, when introducing AI programs, I focus on how they make employees’ jobs easier, improve reliability, and unlock growth. Storytelling bridges the gap between innovation and adoption, turning skepticism into sponsorship. If you can make people care about the ‘why’, the ‘how’ becomes much easier.”