You Can't Scale AI on Fragmented Data

10 days. Two industries. One surprisingly consistent conversation.

The last 10 days took me from ITC Vegas, surrounded by insurance and InsurTech leaders, to conversations with one of the most innovative manufacturers in the country.

Different industries. Different business models. But remarkably similar challenges.

And one thing became very clear:

Insurance: From AI experimentation to execution.

At ITC Vegas, the question wasn't "What can AI do for us?" anymore.

It was "How do we put AI into production, securely, responsibly, and at scale?"

Agentic AI in claims. Intelligent underwriting. Fraud detection. Faster decisions.

The ambition is there. But nearly every conversation eventually came back to the same challenge:

You can't scale intelligence on fragmented data.

Manufacturing: From connected products to intelligent operations.

Connected products, IoT, and field telemetry are generating extraordinary amounts of data. But collecting data isn't the competitive advantage.

Turning that data into predictive service, proactive decisions, operational efficiency, and better customer experiences is.

My biggest takeaway?

The next wave of competitive advantage won't come from who experiments with AI the most.

It will come from WHO CAN OPERATIONALIZE IT: with the right data foundation, governance, and trust built in.

That's exactly what we're focused on at Summit Lake Data + AI.

As a Databricks partner, we're helping organizations bridge the gap between data platforms and real business outcomes, making enterprise AI more than another proof of concept.

A huge thank you to everyone who shared their perspectives, challenges, and ideas over these past 10 days. Some of the best insights come from conversations outside your own industry.

Curious to hear from others: What's been your biggest challenge moving AI from pilot to production?