Why are enterprises spending so much time comparing AI models when their strongest competitive advantage may already be sitting inside their own data?

My guest today is Emma McGrattan, Chief Technology Officer at Actian. Emma argues that model choice is becoming less distinctive while data quality, shared business meaning, governance, and culture increasingly determine whether AI works in production.

We discuss why executives often rate data maturity more highly than the teams closest to the data, what changes when an autonomous agent becomes the consumer, and how data products and enforceable contracts can help organizations move beyond impressive prototypes.

Emma also shares a remarkable example of an expense agent approving a $300,000 spend through one thousand individually permitted transactions. If your organization is trying to move AI from pilot to production, this conversation offers a practical place to begin.

Episode description

Why are so many enterprise AI programs still stuck in pilot mode when powerful models are widely available?

In this episode of AI at Work, I speak with Emma McGrattan, Chief Technology Officer at Actian, about why model selection may be receiving more attention than it deserves. Emma argues that models are becoming increasingly interchangeable, while an organization’s proprietary data, business definitions, governance controls, and data culture remain much harder to reproduce.

Emma explains why familiar terms such as customer, revenue, and churn can mean different things across sales, finance, tax, and operational teams. AI cannot reliably fill those gaps without context, ownership, lineage, and measurable quality. We discuss why enterprise data designed for dashboards and human interpretation must be treated differently when an AI system or autonomous agent becomes the consumer.

The conversation examines Actian’s data governance research, including the finding that 83% of organizations face governance and compliance challenges and that executives rate data maturity 12 percentage points higher than operational managers. Emma describes what this gap looks like in practice, from duplicate customer records and undocumented pipelines to a business field called “Revenue Final Version 7 Verified and Revised.”

We also discuss data products, data contracts, executable governance, and BARC research suggesting that organizations using data products and contracts were 3.4 times more likely to report success with AI at scale. Emma recommends beginning with one valuable use case, defining the data product it requires, agreeing the contract around it, and proving that the organization can deliver reliable results before expanding.

Agentic AI raises the stakes further because the human who previously handled ambiguity may no longer be present at every step. Emma shares the example of an expense agent that approved one thousand $300 purchases. Every transaction sat below its individual limit, yet the combined spend reached $300,000.

Finally, Emma explains what a strong data culture looks like. Employees need permission, access, and skills to question an AI answer, trace its lineage, and recognize when the result does not make sense.

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