How CDOs can use AI to add business value

We are at a defining moment for data leadership. The chief data officer (CDO) is no longer a steward of information but an architect of intelligent enterprises. As AI moves from experimentation to execution, CDOs now face a clear mandate: translate innovation into measurable business value.

Build the right foundation

AI is only as good as the data beneath it. Small inconsistencies in definitions or data quality can scale into large, costly mistakes once AI systems are deployed. The CDO’s first responsibility is to create a trusted foundation—a “gold layer” of well-documented, accurate, and governed data that every model and dashboard can rely on.

Many organisations are adopting hybrid structures that balance centralised governance with embedded data experts in business units. This ensures agility without sacrificing trust. When the inputs are right, AI can become a true engine for better, faster decisions.

Make AI practical, not theoretical

AI delivers value only when it’s embedded in real work. Modern CDOs bridge technology and business by ensuring AI applications solve real problems for employees and customers alike.

For example, within our company, my team built a Go-To-Market AI Assistant that provides our sales and marketing employees instant access to everything from customer insights to competitive intelligence. It’s one of several internal AI tools now in use, collectively serving thousands of employees and handling tens of thousands of questions each week. What once took hours now takes minutes. More importantly, it’s reshaping how we sell, market, and make decisions. Each successful interaction reinforces trust in the system, fuelling broader adoption and a cultural shift toward AI-driven decision-making. Our GTM teams can now spend more time with customers instead of searching through data and context.

Measure what matters

AI should always tie back to outcomes that businesses can feel. The best metrics aren’t technical; they’re operational and financial. Time saved through automation, productivity gains from faster access to insights, and higher conversion rates from smarter decision-making all roll up into tangible ROI.

CDOs are ultimately accountable for both the output of their AI engines and the business value those systems create. Delivering on that accountability requires discipline in four areas:

1. Prioritise the highest-value use cases that align with business strategy.

2. Invest in strong data foundations to ensure quality, consistency, and trust.

3. Design and iterate with internal customer feedback to drive adoption and usability.

4. Measure impact relentlessly and use the results to improve continuously.

Continuous measurement keeps AI honest. Regular feedback loops help tune prompts, adjust context, and refine agent instructions, ensuring systems stay accurate, relevant, and aligned with business needs. The most successful CDOs treat AI not as a one-time implementation, but as a living capability that learns and improves over time.

The CDO’s moment

From banking to retail and manufacturing, the leaders of India’s fast-digitising industries have a rare opportunity to leapfrog by embedding AI directly into the core of decision-making. The CDO’s role is to guide this evolution: to make sure AI is not just adopted but trusted; not just built but measured; not just technical but transformative.

The next era of business will belong to organisations that learn, adapt, and act in real time. And the CDO will be at the helm, turning data into intelligence, and intelligence into enduring business value.

(The author is Chief Data & Analytics Officer, Snowflake. Views are personal.)