Organisations embracing AI as foundational infrastructure are poised to secure competitive advantages in procurement, while those sticking to legacy systems risk disappointing results and strategic setbacks in 2026.
Organizations at the front lines of procurement and supply chain face a make-or-break year as artificial intelligence moves from novelty to expected business infrastructure. According to the original report in Supply Chain Brain, momentum has been strong: companies have rapidly adopted generative AI and enterprise copilots. But a growing body of industry research suggests adoption has not yet translated into consistent, CFO‑trusted returns , and that 2026 will separate the organisations that can prove measurable impact from those that cannot.
Industry data shows adoption is widespread but shallow. McKinsey’s 2025 state‑of‑AI research found 88% of organisations use AI in at least one business function, up from 78% the year before, but about two‑thirds remain in trials or pilots and only 39% report a measurable impact on enterprise‑level EBIT, typically below 5%. Deloitte’s 2025 survey similarly found that 85% of executives increased AI investment in the prior 12 months, yet typical payback for AI initiatives is stretching to two to four years rather than the seven to 12 months many technologists expect. Those figures together encapsulate what the lead article describes as the “GenAI paradox”: broad horizontal deployments that scale quickly but produce diffuse, hard‑to‑measure gains, versus high‑value vertical use cases that largely remain stuck in pilot purgatory.
The problem, the lead analysis argues, is not the technology itself but the foundations on which organisations attempt to deploy it. Legacy procurement and ERP systems were built for rigid workflows and periodic batch processing, not for the continuous data flows, real‑time analysis and context retention agentic AI requires. McKinsey’s operations research underscores the consequences: a 3.8x performance gap between AI leaders and laggards driven by differences in executive sponsorship, partner ecosystems, cross‑departmental collaboration and data investments. In practice, generic, bolt‑on copilots produce generic outputs; they do not systemise specific procurement workflows, adapt to institutional processes, or capture supplier and contract histories , all of which are essential to convert AI activity into boardroom‑credible savings.
Where measurable returns are being realised, the pattern is clear. Successful organisations treat AI as embedded infrastructure rather than an add‑on. They invest in platforms with AI natively built into the architecture, where models learn from prior sourcing events, remember supplier performance, and can execute multi‑step processes within defined governance boundaries. The lead article points to early adopters reporting tangible outcomes: BNY has deployed more than a hundred agentic solutions across the bank, and Vinod Bidarkoppa, CTO at Walmart International, is quoted saying, “AI is a force multiplier that lifts productivity across all lines of business.” Such examples align with investor expectations: PwC’s global investor survey found 73% of investors believe companies should deploy AI at scale and more than 60% expect generative AI to deliver productivity and profitability gains within 12 months , provided organisations also invest in people and upskilling.
A critical differentiator is how teams are organised and measured. The lead article emphasises that adoption accelerates when those doing the work drive implementation, not when tools are imposed from a central AI lab. Successful procurement functions track metrics that matter to finance: cycle times from request to signature, defensible cost‑savings reported to the board, and the accuracy and speed of supplier risk assessments. These are the measures that win CFO trust and protect budgets; without them, AI investment is vulnerable to reallocation. Deloitte’s findings that most AI projects take multiple years to pay back underline the urgency of choosing initiatives that can demonstrate impact sooner rather than later.
Practical, quantifiable use cases are already available. The lead article lists examples such as intelligent front‑door intake routing that cuts days off request processing, and automated RFP analysis that can turn week‑long bid reviews into same‑day decisions. Such wins show up in system cycle‑time reports and team calendars, and they are the sorts of short‑term wins procurement leaders should prioritise while building longer‑term process redesigns that shift organisations from reactive buying to strategic supplier relationships.
The prescriptions in the lead piece are consistent with broader research: define success metrics up front, terminate pilots that cannot show measurable returns within a set timeframe (the article recommends 18 months), and redeploy funding to proven pilots and AI‑native platforms. McKinsey’s work cautions that scaling will require not just technology but governance, data investment and cross‑functional change. PwC’s investor research also stresses that boards and markets expect companies to pair AI with upskilling, reflecting the view that technology and people investment must advance together.
The choices organisations make in 2026 will reshape procurement leadership. According to the original report, one path treats AI as foundational infrastructure , a strategic, architected investment that yields documented savings and faster cycle times and secures a seat for the chief procurement officer at the executive table. The alternative path continues to bolt generic solutions onto incompatible legacy systems and will likely produce disappointing results, budget cuts and constrained career trajectories for procurement leaders who cannot demonstrate impact.
The stakes are clear. The high current failure rates reflect implementation shortcomings, not permanent limits of AI. Those who act now to set hard metrics, eliminate unproductive pilots, invest in AI‑native platforms and align procurement metrics with CFO priorities will be best positioned to convert experimentation into lasting advantage. As the lead author notes from his perspective at an AI‑native procurement platform, the window to build that advantage is brief: 2026 will reveal which organisations have truly moved from hopeful investment to measurable return.
Source: Noah Wire Services
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