The Agentic AI Implementation Challenge is an Economist Enterprise report, sponsored by GEP, which explores the challenges of adopting artificial intelligence in procurement and supply chain.
The report is based on a survey carried out between January and March 2026 of 404 US or Western Europe-based business leaders in the position of director or above. All survey participants were familiar with their organisation’s supply-chain strategy and operations.
Economist Enterprise is solely responsible for the views expressed in this report and the content does not necessarily reflect the perspectives of the sponsor.
Executive summary
Rhetoric vs reality: while boards talk of “agentic autonomy”, AI runs on legacy software and incomplete data There is obvious enthusiasm for generative artificial intelligence (gen-AI) among executives—78% express some support for AI investments, and a further 16% show even greater enthusiasm. This underscores the technology’s perceived critical role in enhancing procurement and supply-chain strategies. Its potential to boost operational efficiency is particularly appealing, with 65% of CEOs anticipating that gen-AI could optimise or automate 26% to 50% of procurement and supply-chain operations within the next three years.
65%
of CEOs anticipating that gen-AI could optimise or automate 26% to 50% of procurement and supply-chain operations within the next three years.
The finance failure: while most firms acknowledge the scale of their data engineering challenge, few are investing to solve it. While 91% of firms acknowledge the need for greater investment in data engineering to unlock autonomous supply chains and 85% say their software stack requires modernisation, the vast majority plan no spending increase—or only a small one—to address these issues in the coming year. AI dreams are doomed to fail if they are not backed by capital. Rather than treating data engineering as an incremental IT spend, firms must view it as a transformational investment without which their business will not thrive.
Uneven autonomy: AI is advancing in procurement and supply chain, just not evenly AI is already entrenched in procurement and supply chain with structured, rules-based processes such as issuing purchase orders or chasing invoices largely automated and orchestrated by AI. But this falls far short of full autonomy, in which agents are trusted to make strategic procurement decisions such as supplier selection, without human oversight. Such autonomy would require a level of procurement-specific domain intelligence that AI agents have not yet achieved—for example, the ability to assess the value of existing business relationships, or to consider trade-offs when a decision benefits one part of the business but disadvantages another. As a result, highly context-specific tasks, including supplier negotiations or responses to geopolitical disruptions, remain human-led for now.
Compliance before creativity: AI’s most valuable insights will not be adopted unless they can be audited, tracked and explained AI agents make decisions that are, almost by definition, beyond human comprehension, as they churn unimaginable amounts of data from innumerable sources to spot patterns. This might produce productivity breakthroughs, but it also creates unease: procurement managers say they are uncomfortable with actions they cannot explain, justify or audit, while their regulators may reject them entirely. As a result, agentic AI is being accorded far less autonomy than its revolutionary capabilities might imply. This will remain the case unless AI systems are designed for governability, with explanations for actions embedded in tools.
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