The hype around AI in procurement is real. But so is the gap between ambition and delivery.
Having worked with organisations across private equity, transport, healthcare, and the public sector on AI transformation, we've gathered lessons that don't tend to appear in vendor brochures. Here are seven of the most important.
Unlike ERP implementations, AI doesn't arrive ready to operate. It requires training, iteration, and continuous feedback. The organisations that succeed are the ones that understand this upfront and build their delivery models accordingly. Think of it less like installing software and more like developing talent.
Generic AI training rarely sticks. The most effective education we've delivered connects AI directly to people's day-to-day roles. A procurement leader needs to see what their morning looks like differently, their emails, their briefings, their supplier reviews, not a theoretical overview of large language models.
If your organisation is at ground zero with AI adoption, the single highest-leverage activity is creating a shared prompt library. Map your team's recurring tasks to specific prompts. Share them. Standardise them. This alone can save individuals an hour or more per day and begins to normalise AI as part of how work gets done.
There is no one-size-fits-all AI strategy for procurement. A large, mature organisation with Ariba or Coupa already deployed needs a different approach to a lean team building a procurement function from scratch. Before you develop your roadmap, be honest about where you are and design accordingly.
Not every AI use case requires a bespoke build. A useful framework puts opportunities into four categories: those you can solve with a standard prompt today; those you can configure using tools like Copilot Studio; those that require a custom build; and those where a vendor solution is the right answer. Knowing which category each use case falls into dramatically improves prioritisation and speed to value.
One of the clearest patterns from real-world delivery: organisations that build AI capability alongside an external partner, rather than handing it over entirely, retain more knowledge, drive faster adoption, and are far better placed to scale. Delivery in waves, with shared ownership and regular knowledge transfer, is the model that works.
If your business case is built entirely around headcount reduction, it will underperform in delivery and in credibility. Procurement's value proposition is increased value and reduced risk. AI contributes to both. In a world where supply chain shocks arrive without warning, the ability to rapidly assess exposure is arguably more valuable than any efficiency saving. Make sure your CFO is hearing that story.
Underneath all of these lessons is a more fundamental change in how procurement leaders need to think. The question to ask isn't "how can we use AI to do what we already do, faster?" It's "what does a future procurement function look like- where AI handles the automatable, and our people focus on judgement, relationships, and strategic value?"
That question leads somewhere far more interesting.
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