As a Fortune 500 company generating nearly $10 billion in annual revenue, Quest Diagnostics operates one of the world’s largest clinical laboratory networks. Its vast data ecosystem supports more than 50 million patient interactions each year, producing billions of data points that power diagnostic innovation, clinical research, and operational excellence.

In this concluding installment of the three-part series, Jim Kruger, Chief Marketing Officer at Informatica, speaks with Mark Clare, Chief Data Officer at Quest Diagnostics, about how data and analytics leaders can keep pace with the exponential transformation driven by generative and agentic AI.

Part 1 explored how the CDO role is evolving and why governance must adapt. Part 2 examined how CDOs can engage business teams, measure outcomes, and design governance frameworks that create enterprise value.

“We’ve never seen this pace of change”

Reflecting on the speed of technological evolution, Clare highlights how quickly the enterprise data landscape has transformed. “Agentic and GenAI combined are the eighth generational change I’ve lived through in the last 12 years,” he says. “Fifteen years ago, we could plan strategies in 18-month cycles. Today, the AI innovation cycle is about a hundred days — and accelerating.”

To stay ahead, Clare relies on an extensive professional network spanning venture capital leaders, CTOs, and founders. “It’s a constant conversation, nearly every day of the week, about what’s changing this quarter,” he explains. “A year ago, hardly anyone was using agentic AI. Now, it’s dwarfing GenAI in terms of potential.”

From dashboards to conversations

Clare believes agentic AI represents a fundamental shift in how analysts and engineers work. “Imagine an analyst agent that doesn’t just serve dashboards but can have a full conversation — drilling down into follow-up questions without building new dashboards every time,” he says. “Our roles are changing. We need networks of technologists who can learn and adapt at a speed we’ve never been challenged to before.”

He also notes the emergence of an AI-native workforce. “This is the first generation coming out of university already thinking AI-first,” he says. “The questions and insights from our interns were impressive — and that’s the mindset we must cultivate across our teams.”

Clare underscores that the CDO’s role in talent management has doubled in importance. “We have to manage talent that’s willing to learn new technologies, architectures, and ways of partnering with the business,” he explains. “And we have to invest in them more than ever before.”

He views this as part of a broader generational shift. “The original innovators in data management, those who built the first data warehouses, are now experimenting with AI,” he observes. “Meanwhile, younger generations are AI-native, and the rest of us in between are learning fast.”

Analytics as easy as Search

Clare envisions a near future where analytics becomes as intuitive as web search. “For 25 years, CEOs and board members have asked why insights can’t be as easy as search,” he says. “We’re finally getting there. In the next three to five years, at least a few companies will achieve analytics that’s as easy as search.”

This transformation, he predicts, will compress the time from insight to action, creating a measurable business edge. “When insights arrive faster, companies can act on twice as many of them,” Clare adds. “Those who get there first will have a first-mover advantage.”

Looking ahead, Clare believes the Chief Data Officer’s role is becoming more entrepreneurial. “CDOs need to take some level of P&L accountability — whether measuring what they enable or managing full data-product portfolios,” he says.

He also sees CDOs evolving into AI innovation leaders. “Given our understanding of data and analytics, we’re best positioned to lead the charge,” he adds. “But we must ensure the data is ready and the governance is right for responsible AI use cases.”

Building AI responsibility by design

Clare emphasizes that many long-standing data management principles are resurfacing with renewed importance — especially metadata and lineage. “Concepts like contextual metadata are now rounding mechanisms for AI,” he explains. “They’re critical for explainability and transparency — two things I see too many people overlooking.”

Drawing parallels to the GDPR era, he advocates for responsibility by design: “As CDOs, we can embed responsible and reportable AI directly into our architectures. It’s up to us to lead that.”

Further, Clare sees the next frontier as designing organizations where humans and agents work together. “A peer of mine is already thinking about how agents can scale across markets with different skill bases,” he says. “That’s the kind of innovation we’ll need — figuring out how to govern agents, define human-assist levels, and ensure control.”

He points to early examples where agent-driven quality assurance and control systems have delivered unprecedented scalability and personalization. “CDOs should be leading that experimentation,” Clare concludes. “If we can’t figure it out in data, how will our businesses figure it out in other domains?”

CDO Magazine appreciates Mark Clare for sharing his insights with our global community.