Richard Leaton is Vice President of Data, Analytics, and AI at Novelis, where he leads the enterprise Data, AI, and Analytics Center of Excellence for the company’s global operations. With more than 20 years of experience, Richard has built his career at the intersection of manufacturing, enterprise transformation, and data strategy.
Richard began his career at General Electric (GE Appliances) in operations and quality leadership roles, where he led large-scale data and quality transformations. During this time, he implemented Oracle ERP and Informatica MDM solutions and drove manufacturing analytics at the plant level, developing a practical understanding of how data can deliver value on the shop floor.
He later joined Hertz Corporation as Director of Data Management and Enterprise Architecture, where he led a $55 million data transformation program and architected master data platforms that contributed to revenue growth.
Most recently, Richard served as Deputy Chief Data and AI Officer at Deloitte, where he led a $70 million enterprise data and AI program. In this role, he advanced data mesh architectures, supported generative and agentic AI adoption, and established governance and ethical AI frameworks at scale.
At Novelis, Richard focuses on unifying master data, modernizing governance, bridging IT and operational technology, and driving measurable productivity improvements across manufacturing, finance, and supply chain.
Richard’s leadership reflects a practical, business-first approach, grounded in the belief that data and AI deliver value only when they are embedded into real-world operations.
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 most critical traits and skills for effective data and AI leadership are:
- Business-first mindset combined with deep manufacturing fluency; the ability to speak the language of the plant floor, finance, and C-suite.
- Humility and curiosity. Willingness to listen, ask bottom-up questions, and challenge assumptions without ego.
- Cross-functional influence and change leadership by building trust and driving adoption in a matrixed, global environment.
- Pragmatic prioritization by ruthlessly focusing on high-ROI initiatives while balancing speed, governance, and cost constraints.
- Thoughtful risk management, especially balancing AI innovation speed with compliance and ethical considerations.
“At Novelis, humility, curiosity, and cross-functional influence have been the most influential. The business is hungry for data support (pull factor), but there is natural pushback from plant teams with an ‘I know it best’ mindset. My ability to listen first, demonstrate tactical value quickly, and earn credibility through manufacturing experience has been key to gaining traction. Leaders across the organization have repeatedly emphasized that success depends as much on how we collaborate and influence as on the technical solutions themselves.”
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?
“The single most powerful non-traditional advice I would give aspiring data and AI leaders aiming for the C-suite is this: Master the art of intentional engagement.
“Technical excellence will get you in the room, but influence and impact come from how intentionally you engage with people. In my experience, from the shop floor at GE Appliances to the C-suite at Novelis, the most successful data leaders don’t just deliver insights; they deliberately build trust, listen with genuine curiosity, and adapt their communication style to meet stakeholders where they are.
“Too many technically strong leaders fail because they push solutions instead of engaging intentionally. They overlook the human side: the plant manager’s “I know it best” mindset, the finance leader’s need for speed, or the cultural scars left by restructuring.
“By practicing Intentional Engagement (showing up with humility, asking better questions, and focusing on relationships before roadmaps) you create the psychological safety and trust required for real transformation. This is what separates good data leaders from those who truly reach the C-suite.”