Our new article series, “The Path to AI-First Procurement: Closing the Gap Between AI Ambition and Execution,” based on new research from Ardent Partners and sponsored by Ivalua, continues today with a look at how procurement’s AI journey is being constrained less by a lack of interest in AI than by the tactical workloads, fragmented processes, legacy systems, and data challenges consuming organizational capacity. The path to AI-first procurement therefore begins with reducing that operational burden, thereby creating capacity for higher-value work and enabling greater speed, intelligence, and strategic impact.
The Operational Reality Gap
When I look at procurement’s readiness for AI, one issue stands out: most organizations are entering this new era carrying a substantial operational burden. For the majority of procurement teams, tactical execution still consumes a meaningful share of staff capacity.
Only 13% of CPOs report that tactical work consumes less than one-quarter of their team’s overall capacity. For the remaining 87%, tactical demands continue to occupy a significant portion of the workday. That near-universality matters. Tactical overload is not a problem affecting a small subset of the market. It remains a baseline operating condition for procurement in 2026.
I see this creating a central tension in procurement’s AI evolution. AI’s value proposition is highest where the conditions for its success are often hardest to establish. The organizations most burdened by tactical work, and therefore the organizations with the most to gain from intelligent automation, are frequently operating with fragmented data, disconnected processes, and legacy systems. In other words, the gap between AI ambition and AI readiness can be widest exactly where closing it matters most.
For most procurement organizations, I do not expect early AI deployments to transform operations overnight. Instead, I expect them to reduce approval-cycle times, remove friction from supplier data management, improve spend visibility, and increase process consistency. Those improvements may initially appear incremental, but their strategic significance is much greater. The efficiency AI creates can free procurement professionals to focus on higher-value work and create the capacity required for broader transformation.
The path to AI-first procurement therefore starts with an honest assessment of how procurement teams actually spend their time. Before organizations can determine where AI should be deployed, they need to understand where tactical work is consuming capacity and where intelligent automation can create the greatest operational leverage.
For me, that is one of the most important lessons emerging from this research: AI readiness is not simply a technology question. It is an operating-model question. Procurement organizations that want to scale AI will need to address the processes, data, systems, governance, and workloads that surround the technology.
The organizations that make that shift will be better positioned to move beyond isolated AI use cases and build a procurement function capable of operating with greater speed, intelligence, and strategic impact.
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