Building the foundation for AI impact at scale

Over the past year, I’ve had the opportunity to spend time with CIOs and CDAOs across industries and geographies, from Gartner C-level communities to strategic customer partnerships to executive roundtables. Despite differences in maturity, size, and industry, the themes are remarkably consistent.

1. Organizations feel pressure to move faster with AI





2. They are challenged with scaling AI and analytics across the enterprise while maintaining trust and governance alongside innovation





3. Many are struggling to realize the promise of an AI reality with meaningful business impact

The real challenge behind AI at scale

Most organizations are not facing a lack of ambition or access to technology. They are struggling because AI exposes long-standing gaps in how data, analytics, and decision-making operate inside the business.

Simply centralizing data into a platform to feed AI is not adequate on its own to create effective AI solutions. Neither are point AI tools nor standalone copilots. Successful AI systems require quality data that’s grounded with appropriate , define logic, and operationalize insights while providing them with guardrails that lead to enterprise trust.

This is also where many organizations struggle. They either centralize too much, slowing innovation, or decentralize without a plan, which can lead to risks. The organizations that are realizing meaningful business impact from AI establish a governance framework and operating model that facilitates wide-scale innovation at the edge through their knowledge workers while monitoring and managing critical processes.

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