AI can generate answers. The real challenge is ensuring organizations can trust those answers enough to act on them.

Every technology event has a theme. A few years ago, the conversation was cloud migration. Then it became data modernization.

Today, it is AI.

The problem is that most organizations are still treating AI as a technology initiative. It isn't. 

Most organizations don't have an AI problem. They have a trust problem.

Models are becoming easier to access. What remains difficult is ensuring people trust the answers those models produce.

As I prepare for the Data + AI Summit, that is the question I am most interested in exploring. What separates organizations that are experimenting with AI from those that are operationalizing it?

Healthcare May Be AI's Most Important Test Case

Healthcare may be one of the first industries where AI success will be determined less by model sophistication and more by governance maturity. Patient records. Clinical histories. Claims data. Provider networks. Operational metrics. Data availability has never been a problem. The challenge has always been turning that data into decisions while maintaining privacy, governance, and trust.

What makes healthcare particularly interesting is that it is being forced to solve problems that every industry will eventually face.

• How do you make data accessible without compromising trust?
• How do you scale access without weakening governance?
• How do you empower users without losing control?

Recently, I reviewed a healthcare transformation initiative involving more than a hundred healthcare tenants and over a thousand data assets. The technology modernization itself was important. What stood out to me was something else.

The organization was not trying to build another reporting platform. It was trying to build a trusted foundation for decision-making.

That distinction matters.

Because AI can only be as effective as the trust organizations place in the data that powers it.

FMCG Is Solving the Same Problem Differently

In FMCG, a delayed decision can mean stockouts, excess inventory, or missed revenue opportunities. The business impact becomes visible almost immediately. The focus is on consumers, inventory, distribution networks, and demand signals. How do you move from reporting what happened yesterday to influencing what happens next?

In healthcare, that could mean identifying trends in patient populations earlier. In FMCG, it could mean detecting changes in demand before they impact the supply chain. Both require the same thing.

Reliable data. Trusted governance. And faster access to insights.

This is one of the reasons I am interested in understanding how organizations are using modern data platforms to shorten the distance between information and action.

What Makes Genie Interesting

One of the conversations I am looking forward to at the Data + AI Summit is around Databricks Genie.

Not because it is another AI interface. But because it changes who can interact with data.

For years, organizations invested heavily in dashboards, reporting layers, and self-service analytics tools. Yet many business users still depended on technical teams to answer relatively simple questions.

Genie challenges that model. 

• A healthcare operations leader can ask: "Which clinics are experiencing the fastest patient growth?"
• An insurance leader can ask: "Which claims categories are generating the highest rejection rates?"
• An FMCG executive can ask: "Which products are showing unexpected demand patterns this week?"

The value is not that AI generates an answer. The value is that business users can access trusted information without waiting for a report, a dashboard update, or a data engineering request. What interests me most is how governance remains intact while access becomes easier.

As AI adoption expands, questions around accountability, stewardship, and investment become just as important as questions around technology.

Because trust is not created by platforms alone. It requires ongoing ownership.

The Conversations That Matter

Every summit will showcase new features, new capabilities, and new announcements. Those conversations are important. But they are not the conversations I am most interested in.

I want to understand what happens after the pilot.

• How are organizations moving AI beyond pilots?
• How are they expanding access without compromising governance?
• How are they building trust in AI-driven decisions?

And how are they balancing innovation with the controls required to operate at enterprise scale?

Healthcare, FMCG, and many other industries are all asking versions of the same question: how do you make data more accessible while keeping it trusted? Those are the conversations I will be looking for at the Data + AI Summit.

Because AI can accelerate decisions. Trusted data determines whether those decisions are right.

Let's Continue the Conversation at DAIS

As organizations move from AI experimentation to AI execution, the challenge is no longer access to models. It's building the trusted data foundation required to operationalize AI at scale.

If you're attending DAIS and exploring questions around Data Intelligence, AI governance, enterprise AI adoption, or trusted data architectures, we'd love to compare notes.

Meet Mitul Vyas, Sr. Director, Data Engineering & Analytics at BOT Consulting, during Summit week in San Francisco. Book a time to meet.

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