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Artificial intelligence is no longer an experiment in procurement. Companies are using it to source suppliers, analyze spending and manage contracts. What started with pilot projects has matured into a business requirement for organizations that want to compete in today’s market.
With procurement teams being asked to cut costs, manage volatile supply chains and provide more visibility, AI helps by taking on routine work, processing large datasets and identifying risks earlier. For groups that relied on spreadsheets and manual processes, the shift is already changing how day-to-day procurement gets done.
For example, McDaniel College discovered during the pandemic that its paper-based purchasing process was unsustainable. Staff were overwhelmed by manual approvals and duplicate orders, and delays kept growing. By adopting an AI-enabled procurement system, the college eliminated paper forms, sped up approvals and created clear visibility into spending, making the process faster and more reliable for everyone involved. “When your workflows are more efficient,” says Julie Fisher, controller of financial services, in Goodbye to Paper-based Purchasing, “you can focus more on the larger, strategic goals.”
Expanding AI’s Reach in Procurement
A new report from ProcureCon Insights tracks AI’s progress on the procurement front. In AI Adoption and its Transformative Impact on Procurement, the company points to broad adoption of the technology across procurement functions, measurable benefits for early movers and gaps that are still holding some teams back. The research makes clear that while satisfaction is high, most companies are still in the early stages of moving from limited AI applications to strategically integrating the technology into their day-to-day operations.
According to the report, AI is playing an increasingly important role in procurement functions like supplier management, spend analysis and cost reduction. The survey also surfaced the key gaps that organizations need to address as they push beyond AI pilots and over to full adoption.
Here are some of the key findings:
- Adoption is maturing: 8% of organizations are conducting early pilots, 49% have reached moderate adoption and 38% are at a more advanced level. Only 5% are fully integrated, which shows that broad enterprise use is still limited.
- AI is meeting expectations: 92% of respondents report satisfaction with their AI tools, showing the technology meets expectations. Only 16% are very satisfied, which signals that functionality and transparency still need to improve.
- Cost savings and ESG benefits are tangible: 55% of organizations report spend reductions while 54% see sustainability gains. These results prove that AI can deliver financial value and support environmental goals at the same time.
- Investment momentum is healthy: 88% plan to increase AI spending over the next year, making AI a clear priority. Still, 90% admit low confidence in measuring ROI, which shows the urgent need for better evaluation methods.
- Cultural and technical barriers remain: 35% cite a lack of proven use cases, 29% face resistance to change and 23% struggle with unrealistic expectations. Success requires leadership support, education and realistic planning.
Next Steps
The survey findings reveal that procurement is at a turning point when it comes to AI adoption. While the early wins are real, the long-term value won’t emerge until AI shifts from tactical use to broad strategies that reshape the buying function. To build on early wins and unlock AI’s broader benefits, ProcureCon advises procurement teams and their CPOs to focus on these priorities:
- Develop comprehensive AI strategies beyond tactical implementations. Organizations should move from isolated AI use cases to integrated strategies that transform entire procurement functions.
- Invest in change management and workforce development. Successful AI adoption requires equal attention to technology implementation and cultural transformation through training and support programs.
- Establish clear return on investment (ROI) measurement frameworks. Organizations need standardized ways to evaluate AI’s financial impact and optimize investment decisions.
- Focus on foundational capabilities first. Prioritize operational efficiency and data visibility improvements that create the foundation for more advanced AI applications.
