Infrastructure & Strategies Driving the Next Wave of Enterprise AI

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AI has advanced quickly, yet only a small group of enterprises are converting early wins into meaningful advantage. Most have proven that generative AI can boost productivity and accelerate workflows, but far fewer have built the foundations required to scale that impact across the business. The moment facing senior technology leaders is decisive. The differentiator is no longer progress alone, but whether data, governance, and architecture are mature enough to translate AI momentum into enterprise-wide performance.

How are enterprise leaders approaching this shift? We partnered with MIT Technology Review Insights to uncover the biggest trends and shifts in enterprise AI strategies. Read insights from 800 senior data and technology executives on what it takes to build a high-performing data and AI organization.

Data and Governance Drive High-Quality AI

The 2025 MIT Technology Review research highlights several organizations making an infrastructure. One organization that found AI success through this approach is Fox Corporation, which set out to build Sports AI, a multi-modal chatbot capable of answering sports questions using live commentary and journalistic content. However, the team discovered that their legacy search foundation could not support the level of precision required. This hurdle prompted them to rebuild the backend using a semantic search architecture that could interpret content contextually and route it to the right models. This investment in data context, lineage, and model orchestration created a measurable improvement in performance and user experience.

This story is a reminder that competitive differentiation increasingly comes from the data and governance layers beneath AI, not the model alone.

At Databricks, we see this pattern across many of the global enterprises we work with. The organizations making real progress are the ones investing in unified data governance, semantic context, and a simplified architecture that allows models and agents to operate on trusted data.

The Differentiator: Unified Data, Analytics and AI

Across the MIT research, one trend is clear. Enterprises that unify data, analytics, and AI on an integrated foundation gain speed, reliability, and the ability to scale with confidence. Those that remain fragmented still experience friction: inconsistent controls, unclear lineage, and disconnected governance patterns.

None of these challenges are insurmountable. In fact, many organizations already possess the ingredients for success. They have capable analytics teams, modern cloud environments, and maturing data platforms. What is shifting now is executive intent. Leaders are prioritizing cohesion, clarity, and cross-functional alignment as the gateways to enterprise-wide AI performance.

Across our customer base, the same signal is consistent. When teams unify data, analytics, and AI on a single, integrated foundation, they remove friction and gain the reliability needed to scale.

Preparing for the Shift to Agentic AI

This foundation-first mindset becomes even more important as organizations explore agentic AI. While generative AI focuses on producing content or insights, agentic AI relies on goals, context, and the ability to take informed actions. That makes governance, lineage, and risk management essential rather than optional.

Enterprises that have started this transition are treating agentic capabilities as catalysts for discipline. Workday, for example, focuses heavily on surfacing the right data to agents, validating the authority behind agent actions, and ensuring governance patterns are consistent at every layer. Their approach reinforces that responsible autonomy is achievable only when data strategy and AI strategy move together.

3M offers another perspective. Their data and AI teams concentrate on building deeper metadata and business context before scaling agentic capabilities. By strengthening the semantic layer behind their data, they ensure that every model and agent has the clarity it needs to make dependable decisions. For them, context is not a technical detai, buta strategic asset.

Turning Data Foundations into Advantage

The organizations moving fastest are not waiting for perfect conditions. From our work with CIOs, CTOs, and CDOs, the organizations that move fastest are the ones that simplify architecture, centralize governance, and treat data context as a strategic asset rather than a technical feature.. Their progress shows that responsible scaling is not a constraint. It is the unlock that allows AI to perform reliably in production and differentiates the leaders from the rest of the field.

As executives plan for the next decade of AI innovation, the real question is no longer whether AI will transform their business. It is whether their organization’s data, governance, and architectural foundations are prepared to support autonomy, action, and long-term performance.

Where to Go Deeper

Download the full MIT Technology Review for detailed insights into the practices that separate high-performing data and AI organizations from their peers.