The Difference Between Using AI And Winning With It

Salvatore Lombardo is Chief Product and Technology Officer at Coupa.

With the advances in LLMs and agentic tools, it can be tempting to think that “smart” AI is all that is needed. But without real systems of record sitting underneath them, AI tools are essentially very smart strangers, lacking grounding in the day‑to‑day reality of a business.

Here’s the reality on the ground: Every business is now operating in the AI economy. AI is changing everything: how work gets done, how decisions are made, how customers are served, and how new products are built. I’ve seen this up close across our network, which has processed more than $10 trillion in enterprise spend, as well as inside our organization. And the pace of change is only accelerating.

For the many organizational leaders who have spent the last couple of years debating the merits and urgency of implementing new, ever-more-capable AI solutions, a more practical (but no less pressing) set of questions has begun to emerge: “What are we currently able to do—that is, what are our actual, meaningful capabilities?” And “What things do we want to be able to do, but currently can’t?” And most importantly, “How should we be using these AI tools we have to capture value and sustain growth?”

Many organizations have already been leveraging AI tools at large scale—within multiple departments, or even among partner agencies—but only realizing discrete tactical gains: say, improved procurement platforms, more dynamic customer management systems or chatbots that engage more meaningfully with clients. These tools all solve important problems, but they do not, even when taken together, represent a strategic shift.

Foundations For An Open Scope

Going from “using” to “winning” won’t be accomplished by declaring an enhanced AI vision. It requires building the right foundations to scale AI across the business and create lasting value:

• Clean, trusted, high-quality data as the foundation for reliable AI