Patrick Duroseau is Chief Data AI Officer at Under Armour Inc., where he leads enterprise data management, analytics, and AI across governance, data engineering, business intelligence, and advanced analytics. His career has been defined by a shift from delivering insights through reports and dashboards to building durable, enterprise-scale data and AI capabilities that drive sustained business value.
Early in his career, Patrick learned that even the most sophisticated analysis fails without strong foundations. Unclear definitions, fragmented data, and weak stakeholder engagement undermine trust and adoption. This insight shaped his focus on establishing robust data governance, quality, and operating models that make data genuinely usable, not just available.
As his leadership scope expanded, Patrick moved from project-based delivery to a product-oriented mindset. He aligned data and AI work to clear business outcomes, established strong ownership, and built repeatable patterns for intake, prioritization, and value measurement.
Patrick balances strategy, change leadership, and execution discipline. He builds teams that can partner credibly with business leaders while modernizing platforms and scaling advanced analytics and AI responsibly. His leadership philosophy centers on the belief that successful data and AI organizations are as much about operating models, culture, and adoption as they are about technology, ensuring insights translate into action and measurable impact across the enterprise.
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?
“The traits and skills I believe matter most are the ones that balance strategic clarity with execution discipline. These are not in order of importance, but they are required as a collective.
- Business empathy by understanding how decisions are made and where friction lives.
- Systems thinking by seeing data, technology, people, and processes as one ecosystem.
- Credibility and trust-building because adoption is voluntary.
- Product mindset by prioritizing quantifiable value and outcomes, not just outputs.
“On the skills side, I put the highest weight on operating model design (how workflows), governance and risk management (how trust is protected), and storytelling (how value is made legible to leaders). Technical depth is still important, but the differentiator is the ability to translate complexity into choices and tradeoffs the organization can act on.
“The most influential combination has been trust plus product thinking. Trust, earned through clear definitions, quality, and accountability, increases adoption. Product thinking keeps the team aligned to measurable outcomes, reduces ‘one-off’ work, and creates reusable assets that scale. When those two are paired with strong change leadership, data and AI stop being a function and become a capability embedded in how the business runs.”
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?
“Treat your career like relationship compounding, not skill accumulation. Technical strength may get you in the room, but trust keeps you there; and trust is built through consistency, clarity, and genuine service to others. Be the leader who makes other executives look good.
“If you want the C-suite, become bilingual: fluent in business outcomes and human dynamics, not just data and AI.”