With all the noise around AI in procurement, I set out to cut through the hype, especially the bold claims made by AI evangelists, and understand the ground reality.
This is an ongoing series where I break down one use case at a time. Each installment looks at the major claims made by AI advocates, what’s actually happening on the ground, and how procurement teams can realistically use AI to improve their processes.
The use cases covered so far
AI in Supplier Discovery →
The claim is autonomous sourcing that finds and vets suppliers on its own. The reality: AI is a strong scout for surfacing candidates you’d never find manually, but it can’t replace the judgment and relationship capital a buyer builds over years.
AI in Supplier Evaluation →
Vendors promise data-driven, bias-free supplier scoring. In practice, adoption for supply risk management sits at just 17%, and most sustainability programs still have visibility into only about half their Tier 1 suppliers, so the data behind the score is often thinner than the claim suggests.
AI in Price Benchmarking →
This is one of the few use cases where purpose-built AI-native tools actually lead, at 67% adoption versus 37% for embedded features. AI is a strong pricing radar, but it isn’t a pricing oracle.
AI in Contract Management →
The single most AI-adopted process in procurement, at 49% combined pilot and production adoption. But 74% of that runs through embedded suite features, not purpose-built contract AI, which changes what you should actually expect from it.
AI in PO Processing →
The highest embedded AI adoption of any use case measured, at 96%. Most of what’s branded AI here is rules-based automation that predates the current AI wave; the real AI value is concentrated in handling freeform, unstructured requests.
Coming up in this series
Future installments will cover spend analytics, savings tracking, intake management, autonomous negotiation, payables management, and sourcing event management, working through the same claims-vs-reality lens.
To be continued…
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