Procurement’s role has expanded sharply in recent years as supply risk, price volatility, and multi-tier transparency demands intensified. Yet new research from SAP Taulia shows that the function continues to receive less AI investment than other corporate domains, even as automation becomes a necessary condition for managing growing complexity.
AI Adoption Trails Other Functions Even as Workloads Climb
SAP Taulia’s AI in Procurement Report highlights a clear investment deficit. Only 35% of surveyed business leaders rank procurement and supply chain as a priority for AI funding, compared with 43% for finance, 39% for data analytics, and 38% for cybersecurity. The misalignment is stark considering 72% of procurement professionals say expectations on their teams increased over the past year, while nearly half believe AI could meaningfully alleviate pressure.
Danielle Weinblatt, Chief Product Officer at SAP Taulia, notes that procurement sits at the center of business resilience and working capital performance, emphasizing that AI can materially strengthen risk, supplier, and cash management. Her comments reflect a broader shift underway: according to trade reports, major enterprises are now using AI to harden operational continuity as geopolitical risk and supplier fragility rise.
Procurement teams point to specific areas where AI could deliver immediate value. Risk detection and mitigation leads at 28%, followed by strategic decision support (26%) and spend analysis (25%). Sourcing, tendering, and invoice automation each follow closely at 23%. These priorities align with broader market trends, where automated anomaly detection and enriched supplier intelligence are gaining traction across procurement platforms.
Budget Constraints Persist Despite Clear Use Cases
The report also highlights marked regional differences. Only 20% of UK leadership teams prioritize AI investment in procurement, compared with 44% in Australia, 41% in Singapore, and 37% in the US. This uneven commitment complicates progress toward global operating models, particularly for companies trying to standardize supplier risk or contract governance across regions.
Still, many teams are moving ahead on their own. Over half of procurement professionals already use platforms such as SAP Joule, Ivalua, or JAGGAER, and nearly two-thirds have incorporated generative AI tools like ChatGPT, Gemini, or Microsoft Copilot into daily workflows. The payoff is clear: 90% say automation allows greater focus on higher-value tasks, 88% report more time for strategic work, and 87% believe AI-generated insights are strengthening procurement’s role in enterprise decision-making.
But progress remains constrained by persistent barriers. Data security and compliance concerns are cited by 36% of respondents. A lack of senior leadership understanding follows at 35%, alongside limited AI expertise and insufficient training (both 33%). Operational challenges, such as workflow integration hurdles and poor data quality—continue to impede momentum. Notably, 30% of professionals say procurement is still not viewed as strategic enough to justify larger AI investments.
John Roberts of NTT DATA stresses that AI is already elevating procurement from a transactional function to a strategic partner, particularly as organizations automate invoice processing and deepen risk analytics. TELUS procurement director Ashifa Jumani adds that leaders must champion AI as an augmentation tool that strengthens, not replaces, procurement capability.
Why AI Adoption Depends on Procurement’s Data Footprint
As enterprises move deeper into AI-enabled operations, procurement’s influence will increasingly hinge on the quality and completeness of its data foundation. Organizations that invest early in harmonizing supplier, contract, and risk data will be positioned to train more reliable models and feed upstream analytics that other functions now depend on. A growing number of companies are already finding that AI’s effectiveness in finance, planning, and supply risk monitoring is constrained by the gaps that originate in procurement datasets. This dynamic creates an opening for procurement to shape how enterprise AI evolves, not by claiming a larger budget outright, but by demonstrating that the function’s data architecture is becoming essential infrastructure for the rest of the business.