Elli Peterson's TechCrunch

The procurement function stands at a critical inflection point. Organizations that have spent decades building Source-to-Pay infrastructure now face a fundamental question: how can artificial intelligence transform procurement from a transactional cost center into a strategic value driver? While early adopters have demonstrated compelling results—40% reductions in requisition cycle times, 30% improvements in contract compliance, and meaningful gains in supplier performance—the path to successful implementation remains littered with failed pilots and underwhelming deployments. The difference between transformative success and expensive disappointment often comes down to understanding the critical success factors that separate effective AI deployments from technology experiments. The promise of AI in Procurement extends far beyond simple automation. Leading organizations are leveraging machine learning to predict supplier risk before disruptions occur, using natural language processing ...

Procurement organizations evaluating artificial intelligence face a barrage of conflicting claims—vendors promising overnight transformation, skeptics dismissing the technology as overhyped, and practitioners uncertain which use cases deliver genuine value versus experimental distractions. This confusion stalls strategic initiatives and perpetuates inefficiencies that AI could resolve: manual requisition intake creating bottlenecks, limited spend visibility eroding negotiated savings, and slow RFx cycle times delaying cost reduction programs. Separating evidence-based AI capabilities from marketing exaggeration requires examining what leading enterprises actually achieve in production environments. Across hundreds of AI in Procurement deployments at companies like Unilever, Siemens, and Johnson & Johnson, consistent patterns emerge that contradict widespread myths. Organizations report specific, measurable outcomes: 40-60% reductions in contract review time, 95%+ accuracy in spend...

Three years ago, our category management team at a major FMCG company faced a recurring nightmare: we were consistently overpaying for packaging materials while simultaneously experiencing stock-outs that delayed new product introductions by weeks. Our procurement process relied heavily on spreadsheets, manual vendor negotiations, and gut instinct. Despite having talented buyers, we simply couldn't process supplier data fast enough to make optimal decisions. That's when we decided to pilot artificial intelligence in our procurement operations—a decision that would fundamentally transform how we approached everything from trade spend analysis to supplier relationship management. The journey into AI in Procurement wasn't smooth, but the lessons we learned along the way proved invaluable—not just for our procurement function, but across our entire supply chain collaboration framework. What started as a narrow experiment in automating purchase order generation evolved into a c...

When our procurement team first embarked on integrating artificial intelligence into our operations five years ago, we had ambitious goals but little practical understanding of what the journey would actually entail. Like many enterprise procurement organizations managing billions in spend, we saw AI as the solution to our persistent challenges around spend visibility, supplier risk management, and contract compliance. What we discovered through trial, error, and eventual success has fundamentally reshaped how we approach procurement transformation. These lessons from the frontlines of implementing AI in Procurement Operations offer practical insights that go far beyond vendor whitepapers and conference presentations. The initial push toward AI in Procurement Operations came from our executive leadership after a particularly painful quarter where supplier disruptions cascaded through our manufacturing operations. We had the data—purchase orders, supplier scorecards, contract terms, in...

When our procurement organization first embarked on implementing advanced analytics capabilities three years ago, we underestimated how fundamentally intelligent agents would reshape our approach to spend visibility and supplier management. The journey from fragmented spreadsheets to autonomous analytical workflows taught us lessons that no vendor presentation or white paper could have conveyed. These insights emerged from real implementations across category management, contract lifecycle management, and supplier performance evaluation—domains where data complexity meets operational urgency. Our initial pilot focused on spend analysis, where AI Agents in Enterprise Analytics promised to automate the labor-intensive process of classifying millions of transactions across diverse suppliers and categories. What we discovered went far beyond simple automation—these systems introduced a level of analytical depth and consistency that transformed how our sourcing teams approached strategic d...

In the fast-paced world of corporate law, where matter management intersects with vendor selection and compliance audits demand rigorous documentation, procurement operations have historically operated as a parallel universe to core legal functions. Yet firms like Baker McKenzie and Clifford Chance have discovered that the procurement of everything from litigation support software to expert witness services directly impacts billable hours, client satisfaction, and ultimately, competitive positioning. The integration of intelligent systems into procurement workflows is not merely about cost savings—it represents a fundamental reimagining of how legal organizations source, evaluate, and manage the resources essential to delivering exceptional client service. The mechanics of AI in Procurement Operations within corporate law environments reveal a sophisticated interplay between traditional legal processes and modern automation capabilities. When a partner at a firm like Latham & Watk...