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Gartner expects that by 2028, 90% of B2B buying will run through AI agents. Not software that reports on spend after the fact, but systems that watch it continuously and act on what they find, within limits someone sets. That's the shift underway right now, and here's the part worth understanding: procurement is about to hand real decisions to machines, and the only thing determining whether that goes well is how clean your spend data is before you do it.
Here's why this matters. Most procurement teams don't miss maverick purchases or slipping supplier performance because they aren't paying attention. They miss them because a person can only review so much. A purchase order sits in a queue too long. A supplier's delivery times slip for months before anyone checks. Tail spend piles up across dozens of small vendors, invisible until next quarter's audit.
AI agents close that gap by doing something automation and copilots never could, acting without waiting. Automation sped up what already happened, faster approvals, invoices that match themselves. Copilots got better at interpreting spend, flagging an anomaly or summarizing a category, but a person still had to decide what to do next. An agent skips that step entirely. It can hold a purchase order for review or reroute it to an approved supplier on its own, because someone already defined what it's allowed to do.
That's the opportunity. Here's the catch: an agent is only as good as the data it's reasoning over. Fragmented spend, missing contracts, half-classified categories, none of that slows an agent down. It just lets the agent make confident, wrong decisions faster than a person ever could. Which is why the procurement teams actually pulling ahead right now aren't the ones deploying the most agents. They're the ones who fixed their spend visibility first, using platforms like SpendXO to get spend classified, connected, and trustworthy before any agent starts acting on it.
That's the real story behind the agentic AI headline, and it's what the full article gets into: what these agents can do today versus what's still experimental, where the line between human and AI decisions should sit, and why spend intelligence has to come before any of it works.