Core Insights - The GEP Global Supply Chain Volatility Index increased to -0.17 in June 2025 from -0.46 in May, indicating a recovery in global supply chain activity despite ongoing tariffs [1] - European manufacturers returned to full capacity for the first time in over two years, driven by strong demand from US customers and a rebound in domestic and export demand, particularly in Germany [1][8] - North American manufacturers significantly increased their purchasing activity ahead of a potential end to the tariff pause, leading to a rise in the index to -0.06 from -0.24 [2][9] Demand Conditions - Global factory purchasing activity rose in June, marking the strongest demand in over a year, particularly driven by North America [7][8] - The index for Asia improved to -0.27 from -0.40, indicating a pick-up in activity, although overall supply chains in Southeast Asia remain underutilized [9] Supply Chain Capacity - The index for Europe rose to 0.01 from -0.30, signaling full capacity utilization across European supply chains as the industrial sector recovers [9] - In the UK, the index increased to -0.41 from -0.97, indicating improvement but still reflecting a significant level of slack in supply chains [9] Inventory and Material Shortages - There was a notable increase in stockpiling among businesses due to concerns over prices and supply, with the highest mentions of safety buffers in warehouses for 2025 [15] - The global item shortages indicator remains historically low, suggesting robust availability of materials [15] Labor and Transportation - Suppliers' workforce capacity is sufficient to handle current order loads, with stable reports of manufacturing backlogs due to staff shortages at historically typical levels [15] - Global transportation costs aligned with long-term averages in June, with logistic cost pressures remaining stable [15]

Core Insights - The GEP Global Supply Chain Volatility Index fell to -0.46 in May from -0.39 in April, indicating increasing spare capacity in global supply chains due to tariffs and trade tensions [1][5][10] Group 1: Regional Analysis - Asia reported the greatest degree of spare capacity in 18 months, with raw material purchases declining for the second consecutive month, primarily driven by a decrease in Chinese factory demand [2][8][10] - North America's supply chains remain underutilized, particularly in Mexico and Canada, although U.S. manufacturers increased raw material purchases to bolster inventories against potential price hikes [3][9][10] - Europe's industrial sector showed signs of recovery, with activity levels stabilizing, particularly in Germany due to fiscal stimulus, while the U.K. faced significant underutilization in its manufacturing sector [4][10] Group 2: Supply Chain Metrics - Global demand for raw materials and components remained weak, marking the lowest level year-to-date, with procurement activity in Asia experiencing its sharpest decline in nearly 18 months [8][17] - The North American index improved to -0.24 from -0.34, reflecting a slight increase in purchasing volumes in the U.S., while the European index remained stable at -0.30, indicating underutilized capacity [10][17] - The U.K. index rose to -0.97 from -1.12, still indicating significant slack in supply chains and ongoing weakness in the manufacturing industry [10][17]

Investment Rating - The report indicates a clear opportunity for investment in AI within the procurement sector in Latin America, highlighting the potential for growth and competitive advantage as organizations adopt AI technologies [6][34]. Core Insights - Latin America is lagging in AI adoption in procurement, with only 15% of procurement leaders utilizing AI compared to 29% globally, indicating a significant gap in readiness and implementation [5][7]. - The region has the potential to unlock a $100 billion opportunity over the next decade by fully leveraging AI in knowledge-based service sectors, with Mexico's AI market projected to reach $450 million by 2025 [8][6]. - Key challenges include low data maturity and a cautious culture that hinders investment in AI without clear proof of value [16][17]. Summary by Sections Current State of AI in Procurement - Only 15% of procurement leaders in Latin America are using AI, with many organizations stuck in early-stage pilots due to poor data maturity and cultural resistance [5][6][15]. - Brazil, Mexico, and Argentina are identified as emerging leaders in AI initiatives, with Argentina launching national programs to become a global hub [6][3]. AI Adoption Phases - The report outlines a five-stage AI maturity model for procurement: Exploration, Pilot Testing, Partial Integration, Broad Implementation, and Full-Scale Transformation, with most organizations still in the early phases [9][10][11][12][13]. High-Impact Use Cases - Three high-impact AI use cases in procurement are identified: Payments and Invoice Management, Category Management, and Vendor Management, which can automate routine tasks and improve efficiency [27][28][26]. Roadmap for AI Adoption - A phased approach is recommended for AI adoption, starting with assessing readiness, cleaning data, piloting use cases, and gradually integrating AI into procurement processes [29][30][31]. - Success factors for scaling AI include executive alignment, culture and training, strategic partnerships, and regulatory readiness [32][33]. Regional Strategies - Specific recommendations for countries include leveraging AI for demand forecasting in Brazil, enhancing agricultural supply chains in Argentina, and improving logistics in Colombia [38][34].

Investment Rating - The report emphasizes that AI is transforming procurement from a tactical function to a strategic core, indicating a positive investment outlook for AI applications in procurement [2]. Core Insights - The report identifies 101 practical AI use cases across the procurement lifecycle, highlighting the significant role of AI in enhancing efficiency, compliance, and supplier collaboration [2][3]. - Autonomous AI systems are emerging as a key development, enabling real-time monitoring, automation of repetitive tasks, and intelligent decision-making throughout the Source-to-Pay (S2P) lifecycle [5][7][8]. Summary by Sections Spend Analysis and Category Management: Use Cases 1-10 - AI unlocks deeper insights into spending patterns and category performance, enabling smarter strategies and faster analysis [10]. - Use Case 1: Automated spend classification using NLP and machine learning improves accuracy over time [11]. - Use Case 2: Predictive spend forecasting helps procurement plan activities and align with financial goals [13][15]. - Use Case 3: Spend anomaly detection identifies unexpected peaks and duplicate payments in transactions [17]. - Use Case 4: Category opportunity identification reveals potential savings through bundling and competitive sourcing [19]. - Use Case 5: Market price benchmarking assesses whether payments are above or below market averages [21][23]. Procurement and Contracts: Use Cases 11-20 - AI accelerates procurement cycles and enhances supplier negotiations [37]. - Use Case 11: Automated supplier discovery expands procurement reach and ensures diverse supplier inclusion [38][40]. - Use Case 12: Intelligent RFx generation streamlines the creation of procurement documents [43]. - Use Case 13: Supplier bid evaluation provides ranking suggestions based on various criteria [45]. - Use Case 14: Contract term extraction enhances contract searchability and audit readiness [47][49]. Supplier Management: Use Cases 21-30 - AI enhances supplier evaluation, management, and collaboration capabilities [71]. - Use Case 21: Supplier risk monitoring detects risk signals using internal and external data [72]. - Use Case 22: Supplier performance scoring creates dynamic scorecards based on various metrics [74]. - Use Case 23: Document verification automates the review of supplier submissions for compliance [76]. Purchasing and Receiving: Use Cases 31-40 - AI simplifies purchasing processes and enhances compliance [100]. - Use Case 31: Guided purchasing assistants provide real-time suggestions during demand creation [101]. - Use Case 32: Purchase request classification automates routing and policy checks [103]. - Use Case 33: Emergency request triage identifies high-priority requests for expedited processing [108]. Invoicing and Payments: Use Cases 41-50 - AI reduces friction in invoice processing and payment workflows [128]. - Use Case 41: Intelligent invoice data capture improves accuracy and reduces manual entry [129]. - Use Case 42: Duplicate invoice detection flags potential duplicates for review [134]. - Use Case 43: Invoice and purchase order line matching optimizes matching accuracy [136]. Compliance and ESG Monitoring: Use Cases 51-60 - AI shifts compliance work from passive to proactive, revealing ESG risks [161]. - Use Case 51: Contract compliance violation detection identifies deviations from contract terms [162]. - Use Case 52: ESG risk scanning categorizes suppliers based on environmental and social governance risks [164]. Procurement Intelligence and Planning: Use Cases 61-70 - AI empowers procurement teams to adapt strategies based on market conditions [190]. - Use Case 61: Category spend forecasting models predict future spending based on various factors [191]. - Use Case 62: AI-driven savings opportunity detection uncovers unexploited savings [197]. Data and Analytics: Use Cases 71-80 - AI enhances data quality and accelerates analysis [225]. - Use Case 71: Procurement data quality scoring engine assesses the accuracy and completeness of records [226]. - Use Case 72: Master data deduplication identifies and merges duplicate records [228]. Chatbots/Help Desk/Assistance: Use Cases 81-90 - AI assistants improve responsiveness and efficiency in procurement queries [255]. - Use Case 81: Procurement policy assistants answer user questions about procurement guidelines [256]. - Use Case 82: Guided purchasing chat assistants help users create requests [258]. Workflow Orchestration and Intelligent Agent-Based AI: Use Cases 91-101 - Intelligent agent-based AI enables goal-driven automation across workflows [285]. - Use Case 91: Cross-system procurement agents coordinate actions across various systems [286]. - Use Case 92: Exception management agents detect process anomalies and suggest solutions [293].