AI adoption looks very different when mistakes can create legal, financial, and reputational risk.

Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.

The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.

Practical Takeaways

• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.

• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.

• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.

• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.

• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.

Timestamped Highlights

00:47, Acrisure’s shift from insurance brokerage toward fintech and financial tools.

01:44, Why regulated industries face a trust gap with AI and need explainable decisions.

04:41, How data teams are evolving from dashboards to conversational data enablement.

08:28, The build versus buy question and where internal AI tools may still make sense.

10:52, Why AI experimentation can get expensive before companies know what works.

16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.

18:14, Why data governance and data quality need to be treated as day one requirements.

One Line That Stuck

“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”

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