Hard-Earned Advice for Scaling AI Inside the Enterprise

Last week, I had the opportunity to moderate the CXO Spotlight: Navigating AI Adoption session at the Emergent Ventures AI CXO Summit, a private gathering of 200 senior leaders from across enterprise and AI. The goal of the event was simple but ambitious: explore what it takes to move AI from pilot projects to meaningful business impact.

The conversation covered a wide range of challenges and lessons learned from the front lines of enterprise AI adoption. Executives discussed how they're navigating internal transformation, rethinking infrastructure, managing data realities, and accelerating organizational readiness.

Moderating this session was a reminder that no one has all the answers, but the leaders making the most progress are those learning from peers, sharing what’s working, and staying open to evolving.

Here’s what stood out from the conversation.

— Sahil Khanna, Sagetap CEO

Adopt AI Before Your Data Is "Ready"

Many enterprises wrestle with whether to clean and centralize data before starting their AI journey. But increasingly, companies are moving forward while their data is still messy, embedding governance and security into workflows rather than waiting for perfection. The most successful teams are focused on progress and are building a culture where AI is part of everyday decision-making.

Start With Internal Use Cases, Then Expand

The most impactful AI efforts often begin with internal use cases, not external initiatives. Rather than chasing vanity metrics, forward-thinking teams are applying AI to business problems and tracking measurable outcomes. Once that foundation is built, broader transformation becomes easier and more effective.

Rethink Your Infrastructure to Keep Pace

Supporting AI at scale requires more than just plugging in new tools. Enterprises are investing in modern infrastructure — from GPUs and bandwidth to security and network architecture — to support AI workloads and rapid development. But fluency matters as much as infrastructure, which is why some companies are holding internal hackathons to quickly upskill their employees.

Success Hinges on Organizational Readiness

A recurring theme throughout the session was that the real challenge of AI isn’t technical, it’s organizational. Governance, culture, training, and internal fluency all matter. The companies seeing real results are the ones building muscle now: testing, learning, and aligning teams before expanding AI across the business.

At Sagetap, we’ve seen firsthand how peer-to-peer learning accelerates adoption. Thousands of enterprise leaders are now part of a growing community that shares insights, compares tools, and trades lessons from real-world AI deployment.

If you're charting your AI strategy and want candid perspective from peers who’ve done it before, join Sagetap and explore what's working across the enterprise landscape.