Bain & Company published new research this week on AI in procurement, covered by Procurement Magazine on October 15. Expert Partner Flavio Monteleone and Partner Thomas Lagner report that companies redesigning procurement around AI see up to 5x returns on their AI investment, 10% to 15% incremental savings, and productivity gains over 60%.
Those figures will grab the headlines. But one line in the article matters far more to anyone running a category team: Bain notes that category teams now face constant pressure to "operate at twice the pace with the same headcount."
Every CPO I speak with feels this acutely. Tariffs, severe weather, freight bottlenecks, energy spikes, and supplier distress do not wait their turn in line anymore—they hit all at once. Meanwhile, teams are still working with the same team members, the same quarterly review cycles, and the same static spreadsheets.
Bain's core insight is clear: the biggest payback isn't just basic efficiency—it's decision speed, supplier innovation, and risk-adjusted performance. These gains flow straight to EBIT rather than sitting inside a procurement software budget. I completely agree, and for direct materials teams, decision speed is where everything starts.
Why decision speed matters most in direct materials
In direct materials, market prices move long before your supply contract adjusts. A drought in a cocoa-growing region, a resin surge following a refinery outage, or a sudden tariff shift on tinplate all start as external market signals—and end up as cost increases on your P&L.
The gap between the signal and your response is decision latency. In most category teams, the process is painfully slow:
- A risk or price signal shows up in a market report, supplier email, or news feed.
- Someone catches it and manually pulls data across three or four separate systems.
- An analyst pulls together a custom exposure model in Excel.
- The category manager prepares a recommendation to pitch to finance and operations.
- A decision is finally made—often long after the optimal window to hedge, forward buy, or re-source has already passed.
Consider a real-world scenario: a $150 million ingredient category facing an 8% price increase. If your team acts in one week instead of six, you can secure favorable pricing and lock in coverage before the surge hits your contract. Every week spent waiting leaves more of that $12 million exposure exposed.
Real speed comes when market context and internal data arrive pre-assembled, allowing experienced people to focus on sound judgment and swift decisions rather than manual data hunting.
Your suppliers already move faster
Bain points out a critical trend that many procurement teams haven't fully recognized: suppliers are already using AI agents, automated pricing intelligence, and dynamic risk monitors. When an AI-equipped supplier negotiates against a procurement team relying on manual spreadsheets, the leverage shifts almost immediately to the supplier.
Consider what happens during your next price negotiation. Your supplier comes prepared with real-time models of their input costs, your switching friction, and your likely counter-moves. If your team shows up with last quarter's benchmarks and a gut feeling, the supplier controls the conversation before it even begins.
Pilots automate tasks. Redesign changes decisions.
Bain makes a key distinction between two ways to adopt technology. Companies that simply drop isolated AI pilots into existing workflows usually see modest 5% to 10% efficiency gains. On the other hand, organizations that rethink and redesign workflows around AI realize gains of up to 80%. The message is simple: the largest returns come from changing how decisions get made, not just automating repetitive tasks.
I wrote recently about treating AI like a free gym pass—you sign up, go twice, and decide working out doesn't work. Bain's research puts a clear price tag on this mindset. Tacking a pilot onto an outdated process delivers minor incremental improvements; building a modernized operating model delivers transformational results.
Bain highlights four key habits that set top-performing CPOs apart:
- Redesign before scaling.
- Commit to outcomes rather than pilots.
- Establish decision frameworks before granting AI autonomy.
- Earn C-suite commitment.
The third point is particularly critical. Before any AI system flags a supplier risk or recommends a forward buy, your team needs explicit governance rules. Which decisions are fully automated? Which require a category manager's sign-off? What thresholds trigger an escalation to executive leadership? Without these boundaries, fast decisions introduce unnecessary risk.
What "doing more" looks like for a category team
Bain projects that AI agents will handle most standard category analysis by 2027—gathering intelligence, running cost models, drafting RFPs, and evaluating responses. Category managers will transition to validating recommendations, applying strategy, and guiding outcomes. Bain estimates this will reduce nine hours of routine analytical tasks down to a single hour.
That freed-up time goes directly to high-value strategic work where human judgment is essential—such as assessing deep supply chain risks with executive leaders, crafting long-term strategic supplier partnerships, or rapidly rebalancing spend when markets shift.
For direct materials teams, operating effectively at twice the speed means:
- Continuously tracking cost drivers across your bill of materials, down to tier-2 and tier-3 inputs like fuel, fertilizer, and freight.
- Simulating dynamic scenarios before supplier discussions so you know your exact walk-away terms ahead of time.
- Keeping category strategies active and continuously updated as conditions change, rather than reviewing them once a year.
- Documenting every decision alongside the market signals and rationale behind it, building an institutional memory for the organization.
That final point is vital for managing team continuity. When key team members move on, their expertise shouldn't walk out the door with them. A team moving at double speed can't afford to constantly re-learn past lessons.
Where to start
Select one key category with significant spend and market volatility. Map out every major decision your team makes in that area each month, and track how long it takes from initial signal to final action. Focus your efforts on streamlining that decision cycle with clear rules and accountability.
You will gain far more valuable experience from transforming one core category this way than running half a dozen disconnected pilots.
At Kodiact, we build software designed specifically for F&B and CPG teams purchasing raw materials, ingredients, and packaging. We provide real-time category strategy, accelerate the workflow from signal to decision, and capture a complete record of every decision made. If your team is navigating the mandate to move faster with existing resources, I'd love to connect.
Source: Procurement Magazine, "Bain & Co: Is AI Making Procurement More Profitable?", October 15, 2026.