Closing the AI Adoption Gap in Procurement

What you need to know: Nearly every procurement team has invested in AI, and far fewer run it in daily workflows. That gap is where the advantage sits. The teams reaching good results are not using better models. They pair AI with connected data, clear ownership, and a place inside the existing process. The move from pilot to production is a sequence of small, provable steps, and it starts with one high-volume workflow such as intake.

The procurement teams pulling ahead this year moved AI out of the pilot and into daily work. Their requests move faster, their spend is visible earlier, and their people spend more of the week on negotiation and strategy. Reaching that point depends less on the model a team picks and more on how ready the team is to run it. The recent research shows both the size of the opportunity and the specific moves that capture it.

Why adoption outpaces results

Intent is no longer the hard part, and neither is budget. Even under real pressure, procurement teams are investing. The Hackett Group's 2026 Procurement Key Issues Study projects that procurement workloads will rise 8% in 2026 while operating budgets stay essentially flat, and teams are responding by increasing technology spend by about 6% to close the gap. Eight in ten procurement executives now rank AI as the most significant force reshaping the function over the next five years.

The distance shows up between the pilot and the workflow. McKinsey's 2026 AI Trust Maturity Survey, which gathered responses from roughly 500 organizations across industries and regions, found average AI maturity rising to 2.3 out of 4, up from 2.0 a year earlier, while only about a third of organizations reached a mature level in strategy and governance. Capability is climbing. The operating structures that turn capability into results are still catching up, and that is the part a team can build.

What separates teams that get results from AI

The pattern in the research is consistent: the teams getting value pair the technology with clear ownership of it. McKinsey found that organizations with explicit accountability for AI, through defined governance roles or ethics and audit teams, reached an average maturity of 2.6, while those without a clearly accountable owner sat at 1.8. The gap did not come from better models. It came from someone owning the outcome.

Three markers separate teams with AI in production:

• Connected data and systems, so AI works from real context instead of a demo dataset.
• Clear ownership and governance, so people trust what the AI produces and act on it with confidence.
• AI built into the existing process, not bolted on as a separate tool that competes for attention.

Skills are the other half of readiness. Nearly 60% of the organizations McKinsey surveyed named knowledge and training gaps as their top barrier to putting AI practices into place, up from about half the year before. The teams that close that gap are the ones that move from pilot to performance.

How to move procurement AI from pilot to production

1. Start with one high-volume workflow. Intake and request routing are strong first candidates, because the value is immediate and easy to measure.
2. Connect the systems that workflow touches. AI performs only as well as the context it can reach, so integration comes before expansion.
3. Set the guardrails first. Define what the AI can decide, what needs a human, and who owns the outcome. Governance builds the trust that adoption depends on.
4. Measure what matters. Track cycle time, spend visibility, and stakeholder satisfaction, then use the results to make the case for the next workflow.
5. Expand from proof. Each workflow that works becomes the evidence for the next, and the program compounds.

How Levelpath closes the gap

Levelpath is built to make the shift from pilot to production happen quickly, because it is AI-native: AI is not a project bolted onto procurement, it is how the platform works. That design takes on the three barriers that usually hold adoption back.

Real context from day one. Levelpath connects to the systems procurement already runs, from ERPs to contract and e-signature tools, and enriches your existing supplier and spend data, so AI reasons over real context instead of a demo set. That is what makes its recommendations worth acting on.

Ownership that spreads beyond the central team. Requests come in through a plain-language front door, and Levelpath applies the right workflow, approvals, and policy automatically. Stakeholders self-serve and take ownership of their timelines, while governance and visibility stay intact.

AI inside the daily workflow. AI Agents handle intake routing, bid comparison, contract review, and negotiation prep at the point of work, on desktop and on mobile. That visible, everyday value is what makes adoption stick.

Turn intent into results

The recent research agrees on the headline: investment and intent are widespread, and the teams that operationalize AI first are setting the pace. The path from plan to production is not one big move. It is a sequence of small, provable steps, and it is available to any team ready to take the first workflow live.

Request a demo today to see how Levelpath puts AI to work across intake, sourcing, and contracts in a single workflow.