Artificial intelligence is moving from experimental curiosity to strategic priority in enterprise procurement, but a massive gap exists between those experimenting with AI and those successfully scaling it across their operations. According to The Hackett Group's 2026 Procurement Key Issues Study, 43% of organizations are actively pursuing AI deployment in procurement, nearly double the level from the previous year. However, only 12% report large-scale implementation, with most organizations still operating pilots or single-use-case deployments. This acceleration signals a fundamental shift in how procurement leaders view AI's role. Eighty percent of procurement executives now identify AI-enabled technology as the most transformational trend affecting the function over the next five years, outpacing automation, skills changes, and other long-standing transformation drivers. Yet the gap between ambition and execution reveals a critical challenge: knowing where to deploy AI and how to measure its impact. What's Driving the Sudden Surge in AI Adoption? The acceleration reflects mounting pressure on procurement teams. The research projects that procurement workloads will increase by 8% in 2026, even as headcount and operating budgets decline, intensifying pressure to move beyond traditional efficiency levers. For many organizations, AI represents the only viable path to handling more work with fewer resources. Organizations that have begun deploying AI in procurement report early gains primarily in cycle time reduction, productivity, and effectiveness. These improvements are helping procurement teams respond more quickly to business needs at a time when workloads are rising and resources are tightening. Current AI use is concentrated in areas such as contract management, market intelligence, and spend analytics, where AI augments decision-making and improves speed and visibility. Amy Hillcox, senior research director of Procurement Applied Intelligence at The Hackett Group, explained the shift: "Procurement is moving beyond isolated digital improvements and beginning to confront what AI really changes: how work gets done. The focus is shifting to redesigning processes, roles and decision-making so AI can deliver measurable value, not just incremental efficiency". Why Are Most Companies Stuck in Pilot Mode? The research reveals a critical insight: simply activating vendor-provided AI features is a missed opportunity. Sixty-nine percent of organizations access AI through capabilities embedded into their existing procurement platforms, particularly for transactional processes. However, leading procurement teams first assess where AI can drive measurable value in their specific organizational context before choosing between embedded capabilities, AI-native point solutions, or custom-developed solutions. This distinction matters enormously. Organizations experimenting with AI often treat it as a technology problem, deploying tools without fundamentally rethinking how work gets done. In contrast, leading teams anchor AI investments in procurement-specific priorities such as supply continuity, spend optimization, and strategic business enablement, while building the governance, talent, and process intelligence required to scale responsibly. The challenge extends beyond procurement. Across enterprise AI more broadly, organizations struggle to bridge the gap between AI pilots and sustained, measurable business outcomes. IBM and The Hackett Group's collaboration addresses this directly through Hackett AI XPLR, a platform designed to move organizations from directional ideas to actionable AI opportunities. The platform identifies where AI-driven enhancements are possible inside existing workflows and evaluates and designs business and agentic workflows where they deliver the greatest measurable value. How to Move from AI Pilots to Scaled Implementation - Assess Your Technology Landscape First: Evaluate every opportunity within the context of your organization's enterprise technology landscape, including core systems, point solutions, and custom applications. Determine whether required capabilities already exist or if enhancements would be needed, ensuring recommendations build on current investments rather than duplicating them. - Ground AI in Real Workflows: Use process mining outputs, digital standard operating procedures, and system data to identify where AI can deliver measurable value. One recent example involved a client seeking a rapid, evidence-based view of AI opportunities within its invoice-to-pay process. Using Hackett AI XPLR, teams identified seven prioritized improvement themes and eighteen detailed AI use cases tied directly to those themes. - Quantify Expected Outcomes Before Implementation: Develop a clear, actionable process improvement plan grounded in implementable AI-driven solutions with measurable enterprise value and ROI. This approach delivers visibility into meaningful improvement opportunities, surfacing levers for speed, efficiency, and experience. - Build Governance and Talent Capabilities: Leading teams are building the governance, talent, and process intelligence required to scale responsibly. This includes establishing clear accountability for AI initiatives and developing the skills needed to manage AI-driven workflows at scale. Christopher Sawchuk, principal and global Procurement Applied Intelligence practice leader at The Hackett Group, noted the strategic shift: "AI is enabling procurement to elevate the value it delivers to the business. By applying AI through an agentic enterprise lens, leading teams are extending their impact beyond cost reduction to improve speed, insight, and outcomes across the enterprise". The research highlights a growing divide between organizations experimenting with AI and those beginning to reimagine procurement through an agentic enterprise lens. This distinction will likely determine which companies capture significant competitive advantage from AI investments and which remain trapped in perpetual pilots. For procurement leaders, the message is clear: AI success depends not on technology alone, but on intentional strategy, process redesign, and measurable outcomes.
