Did you miss the recent webinar “Procurement Metrics that Matter in 2025,” featuring Andrew Bartolini, founder and chief research officer for Ardent Partners, and Paul Blake, Senior Director, Engagement at GEP?
The webinar unpacked highlights from the eBook The Procurement Metrics that Matter in 2025, featuring the biggest trends and issues facing CPOs today.
Today marks the third installment of a three-part article series that highlights the key points from the webcast, along with a link to the event.
Making Sense of Messy Data: The First Step Toward Procurement Insight
One of the most common challenges procurement teams face is dealing with purchase data buried deep within general ledger (GL) systems, which are often without clear descriptions, categorizations, or structure. Organizations frequently assume that solving this requires large-scale systems integration or complex IT involvement. However, the recommended foundational step is far simpler: start with raw, flat data. Exporting GL data in the most basic “blob” format allows procurement analytics platforms or AI tools to process and interpret the information without the need for heavy integration work. For decades, spend analytics teams have relied on this method to gain initial visibility, and with the rise of agentic AI, the process is becoming even easier. These agents can ingest unstructured flat files and rapidly classify, contextualize, and interpret spend patterns, delivering insights comparable to fully integrated systems. What seems like a major IT burden can often be resolved with a straightforward data export and the right analytical tools.
Another recurring topic in procurement discussions is the definition of “spend under management.” In broad terms, it refers to the percentage of an organization’s total spend (direct, indirect, services, and sometimes capital) that is either influenced or processed by procurement. The key factor is visibility: spend under management is spend that procurement can track, touch, or influence through systems or processes. While the specifics differ by company, it generally reflects how much of the organization’s spend flows through formal procurement channels and how much is subject to governance, policy, and optimization.
Three Foundational KPIs for Procurement Teams Starting from Scratch
For organizations in a decentralized or hybrid environment, identifying the right early KPIs is critical. Savings is often considered the default first metric, but as the webcast highlighted, savings must be measured accurately and realistically. True savings are realized only when invoices reflect the contracted terms, rather than when the contract is signed. This requires full end-to-end transaction visibility and close alignment across finance, procurement, and field teams. Without compliance and visibility into how purchasing actually occurs, savings calculations become theoretical rather than measurable.
Beyond savings, two additional starter KPIs include procurement influence (the percentage of spend that procurement touches) and compliance with preferred suppliers or contracts. These metrics reveal whether teams across the organization are using negotiated contracts or buying independently. Establishing alignment with the CFO and business leaders ensures that savings are not just negotiated but actually delivered through compliant purchasing behavior.
Where AI Is Delivering the Most Value in Procurement
AI is rapidly transforming procurement, particularly in contract-related workflows. Document-based AI solutions now assist with contract discovery, clause analysis, and issue identification, helping teams uncover risks, obligations, and value opportunities embedded within complex agreements. AI can also support contract ingestion, automatically reviewing incoming documents for red flags or opportunities for better terms. Another powerful use case involves contract lifecycle events, such as renegotiations. As agreements approach expiration, AI agents can trigger reviews, gather market intelligence, and even prepare recommendations for alternative suppliers or pricing models. AI is also accelerating market research, supplier identification, and pricing analysis. Agentic AI systems are being developed to scan market conditions, analyze price points, and recommend sourcing priorities, allowing procurement professionals to make faster, more informed decisions. Collectively, these use cases show how AI can enhance visibility, expedite decision-making, and unlock significant value across the procurement lifecycle.
