Something interesting is happening in procurement teams that have adopted AI agents. The technology is working — savings are being captured, tail spend is getting managed, category strategies are being built faster. But the bigger transformation isn't in the technology. It's in how people work.
Procurement professionals who spent their careers building expertise in specific categories are discovering they can now cover three times as many categories with AI support. Generalists who were hired for their communication skills are suddenly producing category analyses that rival what specialists took weeks to build. And the teams that are thriving aren't the ones with the best AI tools — they're the ones that have figured out how humans and agents actually work together.
AI agents aren't replacing procurement professionals — they're creating a new way of working that demands different skills, different team structures, and a fundamentally different relationship with technology. This is human-agent teaming. And it's less about the technology than most people think.
Key takeaways:
- The shift to agentic AI in procurement isn't about automation replacing people — it's about building hybrid teams where humans handle judgment, relationships, and strategy while AI agents handle scale, speed, and synthesis.
- McKinsey identifies human-agent teaming as one of four foundational shifts in the "rewired procurement model," noting that procurement staff will need new capabilities including prompt engineering, scenario evaluation, and change management.
- Suplari, rated 4.8/5 stars on Gartner Peer Insights, is built for this hybrid model — with embedded prompt libraries, contextual AI suggestions, and a conversational interface that helps procurement teams learn to work with AI agents without requiring technical expertise.
- The organizations that invest in adoption and upskilling now — not just technology — will be the ones that capture the compounding value of human-agent teaming over the next three to five years.
Why this isn't just another automation story
Procurement has been through several waves of automation — from e-procurement platforms in the early 2000s to robotic process automation (RPA) in the 2010s. Each wave promised to free up procurement professionals for "more strategic work." Each wave delivered efficiency gains. And each wave left the fundamental way people worked largely unchanged.
Agentic AI is different. Not because it's more powerful (though it is), but because it changes the nature of the collaboration between humans and technology.
Previous automation replaced discrete tasks: matching invoices to POs, routing approvals, generating reports. The human still owned the entire decision-making process — they just had fewer manual steps to complete. The workflow was the same; some steps were faster.
AI agents don't just execute steps. They reason, recommend, and act across complex, multi-step processes. They can analyze a supplier's performance history, cross-reference it against market benchmarks, identify that pricing has drifted above market rates, draft a renegotiation strategy, and prepare the communication — all before a procurement professional has finished their morning coffee.
This creates a fundamentally different dynamic. The human is no longer the sole thinker who delegates mechanical tasks to software. Instead, the human and the agent are thinking together — with different strengths, different roles, and a need for genuine coordination.
McKinsey's recent research on agentic AI in procurement frames this as one of the four foundational shifts in what they call the "rewired procurement model":
"In a rewired procurement function, humans and AI agents will work side by side. Procurement staff will guide and coach their digital counterparts, while agents will take over most repetitive transactional work, freeing up people to focus on strategic decision-making, orchestration, and oversight."
The key phrase there is "guide and coach." This isn't a manager-to-tool relationship. It's closer to a senior professional working with a very capable but junior colleague — one that needs direction, benefits from feedback, and improves with every interaction.
The skills that matter now — and the ones that don't
If the work is changing, the skills procurement professionals need are changing too. This doesn't mean existing expertise becomes worthless — far from it. But the weighting shifts, and some entirely new capabilities become essential.
Skills that become more valuable:
Judgment and contextual reasoning. The ability to evaluate an AI agent's recommendation against business context that the agent may not have — organizational politics, supplier relationship history, strategic priorities that aren't captured in data. This has always been valuable, but it becomes the primary differentiator when the analytical work is handled by AI.
Prompt craft and AI direction. Knowing how to frame questions, set parameters, and guide an AI agent toward useful outputs. This isn't "prompt engineering" in the technical sense — it's more like knowing how to brief a capable analyst. What context do they need? What are the constraints? What does a good answer look like? Gerber describes the learning curve as surprisingly short:
"Usually, it's like one — 'Well, how about this?' — and then that person is off on a tear. They have all the knowledge. They just need to be able to get started."
Cross-functional communication. As AI frees up bandwidth, procurement professionals are increasingly expected to engage with finance, operations, sales, and the C-suite. The ability to translate procurement intelligence into business language — and to sell procurement's value proposition internally — becomes a core competency, not a nice-to-have.
Change management. Someone on the team needs to drive adoption, manage resistance, build the prompt libraries, and continuously refine how humans and agents work together. This is a skill that most procurement teams don't currently develop or reward — but it's becoming critical.
Skills that matter less (not zero, but less):
Manual data wrangling. The hours spent cleaning spreadsheets, reclassifying spend, and building pivot tables are being absorbed by AI-powered data management. Analysts who defined their value by their Excel proficiency will need to redefine it.
Deep single-category specialization as a barrier to entry. Expertise in a specific category is still valuable — perhaps more valuable, because it can now be captured and replicated through AI. But you no longer need 15 years in a category to be effective in it. As Gerber explains:
"It's actually very quick for people to get up to speed in a category because they can work with an agent that has all that knowledge that maybe you lost or you didn't have. You can enable your team to take on this diverse set of categories that you wouldn't have been able to do before."
Report building and dashboard design. When you can ask an AI agent a question in natural language and get a contextual answer in seconds, the value of spending hours constructing the perfect dashboard diminishes. The insight matters; the format is increasingly handled by the tool.
The organizational design challenge
Beyond individual skills, human-agent teaming raises real questions about how procurement teams are structured.
Traditional procurement organizations are built around category ownership — a person or small team owns IT spend, another owns marketing services, another owns logistics. This makes sense when category expertise is the scarce resource and each category requires deep specialist knowledge.
In a human-agent world, the scarce resources shift. Category knowledge becomes more accessible through AI. What becomes scarce is the ability to manage AI effectively, the judgment to know when to override it, and the relational skills to manage suppliers and stakeholders.
This suggests a different organizational model — one where procurement professionals are more like portfolio managers who oversee multiple categories with AI agent support, rather than deep specialists who own a single domain. The 20-year veteran doesn't disappear, but their role evolves: they become the person who trains the AI, validates its recommendations in edge cases, and handles the highest-stakes negotiations and relationships.
McKinsey's research supports this shift, noting that the rewired procurement model requires "reskilling for strategy, exception management, and insight interpretation." The emphasis on exception management is telling — the day-to-day is handled by agents, and humans are called in for the cases that require judgment that falls outside the AI's training.
This has significant talent implications. Procurement teams may become smaller in headcount but broader in capability. The generalist with strong AI fluency and communication skills becomes highly valuable. And the function may start attracting talent from backgrounds — data science, product management, marketing — that it historically hasn't.
The adoption gap: why technology isn't the bottleneck
Here's the uncomfortable truth about human-agent teaming: the technology is ahead of the adoption.
McKinsey identifies "embedding change, not just tools" as a critical success factor, noting that "successful transformations pair technology with operating model redesign, new KPIs, and strong change leadership." Yet most procurement organizations are still focused on selecting and implementing AI tools rather than redesigning how their teams work with them.
The biggest barrier isn't technical capability. It's the blank cursor problem.
Gerber describes it vividly:
"Especially if you go to folks that are maybe used to doing category management or sourcing in a particular way and they're presented with just a co-pilot, you kind of get the 'I don't even know what to ask it' response."
Suplari's approach to this is instructive. Rather than dropping users into an open-ended chat interface, they embed contextual prompts throughout the product — when you're looking at a supplier, the system suggests relevant questions and actions specific to that supplier. When you're reviewing a category, it surfaces the opportunities the AI has identified and offers starting points for deeper exploration.
"Just by navigating in our user interface, we're showing you suggestions and interesting ways that you might want to interact with AI. Those starting points — that's really the thing you need."
They also built what they call a "prompt library" — a shareable repository where team members can save effective conversations, queries, and workflows. When someone leaves the team, their best prompts stay behind. When a new person joins, they inherit not just the data but the questions their predecessor found most valuable.
"Someone can come in to a new role and as long as their predecessor had been using it effectively, they can pick it up and they're off to the races already with what to ask it."
This is organizational design disguised as a product feature. It solves the knowledge transfer problem that has plagued procurement for decades — and it makes the transition from individual expertise to team capability concrete rather than aspirational.
Measuring the hybrid workforce
If the work model changes, the metrics need to change too.
Traditional procurement KPIs — cost savings, spend under management, contract compliance — still matter. But they don't capture the value of human-agent teaming. A team that covers twice as many categories, responds to market shifts in days rather than weeks, and builds strategies that incorporate scenarios they'd never have had time to model — that team is creating value that traditional metrics miss.
McKinsey proposes "Procurement ROI" as a unifying metric: total value created divided by total cost to achieve it. Value includes realized savings, leakage avoided, working-capital benefits, risk reduction, and revenue enablement. Cost includes people, technology, data, and change management.
For human-agent teams specifically, consider tracking category coverage per analyst (how many categories can each person effectively manage with AI support), time-to-insight (how quickly the team can go from question to actionable recommendation), savings identification to realization ratio (are the opportunities AI surfaces actually being captured), and adoption depth (not just logins, but meaningful interactions — prompts saved, recommendations acted on, strategies built with agent assistance).
These metrics shift the conversation from "did we buy the right tool?" to "are we working differently?" — which is the question that actually determines whether human-agent teaming delivers on its promise.
A practical path to building your hybrid team
For procurement leaders ready to move beyond piloting AI tools toward genuine human-agent teaming, here's what the organizations that are doing it well have in common:
Start with the work, not the tool. Map your team's actual time allocation. Where do people spend hours on analysis that an agent could do in minutes? Where are the judgment-heavy tasks that should stay human? This audit tells you where teaming creates the most value and where to begin.
Pick your first AI-augmented workflow. Don't try to transform everything at once. Choose one workflow — tail spend monitoring, category strategy development, contract compliance — and redesign it for human-agent teaming. Define who does what, what the approval gates look like, and how you'll measure success.
Invest in prompt literacy, not prompt engineering. Your team doesn't need to write Python or understand transformer architectures. They need to know how to frame a good question, evaluate an AI's response critically, and iterate when the first answer isn't right. Budget for this training — it pays back faster than almost any other investment.
Build the prompt library from day one. Every effective interaction with an AI agent is an asset. Capture it. Share it. Make it discoverable. This is how institutional knowledge gets built in a human-agent world.
Redesign roles gradually. Don't restructure your org chart on day one. Let the teaming model reveal what's needed. As people free up time from analytical work, direct them toward the strategic and relational work you've never had bandwidth for. The new roles will emerge from practice, not from a planning exercise.
Create feedback loops. AI agents improve with use and feedback. Build processes for your team to flag when the agent gets it wrong, celebrate when it gets it right, and continuously refine the collaboration. McKinsey emphasizes this: "AI systems improve with use. Treat every cycle as a learning opportunity."
The bottom line
Human-agent teaming isn't a future concept — it's happening now in procurement organizations that have moved past the "let's buy an AI tool" phase into the "let's redesign how we work" phase. The technology is ready. The question is whether the people and the organizational structures are ready to meet it.
The procurement professionals who thrive in this model won't be the ones who can do the most complex analysis in a spreadsheet. They'll be the ones who can direct an AI agent to do that analysis in seconds, evaluate whether the output makes sense, and then use their freed-up time to build the supplier relationships, stakeholder alignment, and strategic vision that no AI can replicate.
As McKinsey puts it: this is a "once-in-a-generation opportunity to elevate procurement from a support function to a source of strategic advantage." But capturing it requires investing in people and ways of working — not just technology.
The agents are ready. The question is whether your team is.
To assess how ready your organization is for AI-powered procurement, take Suplari's AI readiness assessment.
This article draws on insights from the Procurement Software Podcast, hosted by James Meads, featuring Jeff Gerber, CEO and Co-founder of Suplari. Listen to the full conversation on Youtube.
