From Generative to Agentic AI
How Data Leaders Build and Scale an AI Strategy That Works
Summary
- Durable data foundations are holding strong—now it’s time to turn stability into scale.
- Agentic AI is emerging as the next phase of enterprise intelligence, with 68% of organizations planning to invest.
- Unified governance and open platforms are what separate high-performing data teams from the rest.
From Foundation to Scale
AI is advancing fast, and enterprise leaders are proving that progress is possible when strategy comes first. According to new MIT Technology Review research, most organizations have built durable data foundations that have held steady through the generative AI surge. The next frontier is clear: turn that stability into scale.
Nearly two-thirds (65%) of enterprises have already deployed generative AI, and momentum is building to operationalize it across the business. What’s working? Teams that align their data, governance, and AI initiatives are moving faster—and achieving measurable impact.
As Rafael Cavalcanti, Chief Data and AI Officer at Bradesco, put it:
“Generative AI made clear that organizations that combine their data and AI strategies can more easily deploy sophisticated AI approaches than those where data and AI are managed separately.”
The Next Phase: Agentic AI
While generative AI unlocked creativity and productivity, the next leap is agentic AI—systems that can reason, decide, and act autonomously. Only 19% of enterprises have begun deploying it, but 68% plan to invest within two years, focusing on operational efficiency, insights, and decision acceleration.
Early adopters are starting small but thinking big. At 3M, for example, data and AI leaders are using agentic systems to drive internal efficiency and R&D performance.
As Nithin Ramachandran, Global Vice President, Data and AI, 3M explains:
“We’ll use [agentic AI] to drive efficiency across the organization in ways that deliver competitive advantage, including by improving our R&D processes.”
Enterprises are moving deliberately—not because of hesitation, but because governance, explainability, and data quality now matter more than ever. Agentic AI magnifies the value of trusted, high-context data and clear accountability across every layer of the stack.
What Leaders Are Doing Differently
Across industries, from Fox and SAP to Workday, Reckitt, and E.ON, executives share a consistent blueprint for scaling AI responsibly:
- Unify data, analytics, and AI on open, collaborative platforms.
- Double down on governance and quality. More than half of enterprises are prioritizing analytics modernization and data transparency to unlock new value.
- Empower teams with automation. 67% are using AI-powered data management tools to improve data lineage, observability, and speed.
The result? 32% of the world’s largest enterprises are now “data high-achievers,” demonstrating that scale and impact are within reach.
The Leadership Imperative
As AI systems evolve from generative to agentic, leadership focus is shifting from experimentation to execution. The challenge isn’t whether to invest—it’s how to orchestrate data, governance, and AI together for sustainable advantage.
For CIOs, CTOs, and CDOs, the mandate is clear: Build on your foundation. Govern for trust. Scale what works. Because the next wave of AI leadership won’t be defined by who moves first, but by who scales best.
