Unlocking Efficiency: Ten Prominent Uses of AI in Procurement

Procurement professionals face the daunting task of balancing cost savings, supplier relationships, and risk management. Artificial intelligence (AI) is emerging as a transformative technology that is reshaping traditional procurement processes, enhancing efficiency, and enabling strategic decision-making.

Positioned at a crucial juncture, procurement handles vast amounts of data from internal sources (such as spend data and demand patterns) and external sources (including suppliers and market insights). Effectively utilizing this data and developing advanced tools is essential for making informed sourcing decisions. By mastering this data, procurement teams can advance their strategic objectives, encompassing more than just traditional metrics like cost, quality, and delivery.

“AI combined with IoT, machine learning, and robotics process automation could revolutionize the way that procurement is performed,” said Michael Wheeler, Avasant Partner and Global Supply Chain Practice Leader, based in Los Angeles. “Procurement professionals can now focus on strategic decision-making and supplier relationship management. In the future, I believe AI will continue to enhance procurement processes, making them more efficient, cost-effective, and automated. The potential implications include increased profitability and competitiveness for companies that effectively implement AI in their procurement operations.”

10 Prominent Uses of AI in Procurement

1. 1. Automated Spend Categorization: AI algorithms meticulously categorize spending data, simplifying category management. Accurate spend categorization empowers sourcing decisions, leading to cost savings and better supplier negotiations. By automating this process, procurement teams can focus on strategic tasks rather than manual data sorting.Bloomberg developed BloombergGPT, a large language model with 50 billion parameters, specifically tuned for financial tasks. The model is designed to enhance various natural language processing (NLP) tasks such as sentiment analysis, named entity recognition, and news classification. By meticulously categorizing financial data, BloombergGPT simplifies data management and supports better decision-making, aligning with the concept of automated spend categorization in procurement.

1. 2. Forecasting and Optimization: AI enhances demand prediction accuracy by analyzing historical data, market trends, and external factors. This enables procurement teams to optimize sourcing strategies, inventory levels, and supply chain control. Accurate forecasting minimizes the risk of stock outs and overstock situations, ensuring smoother operations.Walmart uses AI-powered demand forecasting to analyze data sources such as historical sales, market trends, and external factors that influence demand fluctuations. These forecasts help the procurement team optimize sourcing strategies by making informed purchasing decisions based on anticipated needs. Additionally, AI-powered forecasting helps maintain optimal inventory levels by reducing the risk of stock-outs and overstocking. Furthermore, accurate forecasts contribute to efficient resource allocation and supply chain control.

1. 3. Risk Management: AI monitors data to identify supply chain risks and implement mitigation strategies. AI systems track internal and external data for signs of disruptions, such as geopolitical events or natural disasters. This enables proactive measures, such as diversifying suppliers, adjusting inventory, and renegotiating contracts. AI enhances supply chain resilience and reliability.Siemens uses AI to monitor supplier performance and market trends, enabling proactive risk management. The Siemens AI platform integrates with Microsoft’s Azure services to provide real-time insights and predictive analytics. The platform evaluates supplier performance against key indicators and analyzes external factors that impact delivery and costs. This helps Siemens make informed strategic decisions and enhance supply chain efficiency.

1. 4. AI Interfaces for Data Exploration: Procurement leaders can query vast datasets effortlessly using AI interfaces. These interfaces uncover hidden insights related to climate events, oil price fluctuations, and alternative suppliers, informing strategic decisions. This capability allows procurement professionals to stay agile and make informed choices based on comprehensive data analysis.Siemens and IBM developed an AI-powered procurement platform called “Autonomous Procurement” that enhances data exploration for procurement specialists. Users can use natural language queries to access and analyze data on suppliers, contracts, and performance, reducing the need for complex data manipulation. The AI engine uncovers patterns and relationships that inform strategic decisions, such as diversifying suppliers, adjusting inventory, and negotiating flexible contracts. Siemens’ use of AI in procurement shows how this technology can support data-driven decision making.

1. 5. Autonomous Trade Decisions for Standardized Items: Bots autonomously execute pre-defined trade decisions, ensuring efficiency and cost-effectiveness in competitive markets like transportation or temporary labor. This automation reduces the need for human intervention in routine tasks, allowing procurement teams to focus on more complex negotiations.Uber Freight uses AI to streamline logistics for standardized goods in competitive markets. AI bots match shippers with carriers based on data on needs, availability, and conditions. This reduces human intervention, turnaround times, and costs. It also frees up procurement teams to focus on strategic initiatives like complex logistics or contract negotiations. This highlights the capabilities of AI to improve efficiency and cost-effectiveness for standardized services.

1. 6. Automated Contract Generation: Industries are increasingly adopting AI for automated contract generation to streamline procurement processes and reduce administrative burdens. AI can draft, review, and modify contracts, ensuring consistency and compliance with organizational policies.DocuSign, known for its electronic signature solutions, has expanded its services to include AI-powered contract generation tools. These tools are intended to automate the drafting of standardized contracts, such as non-disclosure agreements (NDAs), non-compete agreements, and service agreements. By automating the creation of these agreements, DocuSign’s platform aims to streamline the process, potentially reducing the administrative workload for legal and procurement teams. Consequently, these teams can focus their efforts on reviewing and negotiating more complex contracts while the AI ensures that the generated documents adhere to pre-approved templates and clauses, maintaining consistency and compliance.

1. 7. Supply Market Analysis and Strategy Optimization: AI identifies high-risk sources and suggests alternatives, ensuring resilience by staying within defined price ranges and lead times. Strategic sourcing becomes data-driven and agile, enabling procurement teams to respond swiftly to market changes and disruptions. Companies are employing AI to develop digital twins of their supply chains, encompassing raw material suppliers, internal manufacturing networks, and logistic channels. This method offers near-real-time views of supply risks, costs, and carbon intensity, enabling procurement teams to simulate risk levels and make informed strategic decisions. This predictive capability assists in identifying high-risk sources and suggesting alternatives, thereby maintaining resilience within specified price ranges and lead times.Unilever utilizes an AI application from the German start-up Scoutbee to quickly identify alternative supply sources by compiling a list of potential suppliers using data from various online sources, including financial information, customer ratings, sustainability scorecards, and real-time alerts from social media and news feeds. Corporate buyers then manually instruct Scoutbee’s staff to request further information from selected suppliers. This technology supports Unilever’s commitment to purchasing €2 billion annually from diverse businesses worldwide by 2025, particularly in the US, by identifying small or medium-sized enterprises (SMEs) that might not be easily found through traditional searches, thereby enhancing supplier diversity and addressing supply chain disruptions.

1. 8. Combining Internal Data with External Market Reports: AI enhances profitability analysis and strategic decision-making by identifying patterns and trends in commodity prices through the integration of internal data with external market reports. This comprehensive view of market dynamics enables more accurate forecasting, aiding businesses in making informed decisions regarding procurement and logistics strategies.To improve customer engagement and supply chain management, Maersk has introduced several digital tools. The Maersk App allows customers to track shipments, receive notifications, and manage documentation from their mobile devices. The Maersk Logistics Hub provides real-time vessel tracking and AI-powered predictive analysis, while the Captain Peter technology offers digital visibility for reefer containers, monitoring cargo conditions and location throughout the journey. These solutions help businesses make informed decisions with relevant data, ensuring better preparedness for future disruptions. By integrating big data applications and AI algorithms, Maersk aims to enhance the accuracy and efficiency of their supply chain operations, maintaining crucial visibility and traceability in the logistics industry.

1. 9. Supplier Performance Management: AI continuously monitors supplier performance metrics, allowing for early identification of deviations. This proactive approach enables adjustments, risk mitigation, and stronger supplier relationships.Airbus uses an AI-powered supplier risk management platform developed by Tradeshift. This platform monitors delivery schedules, quality control data, and financial health, flagging potential risks like late payments or bankruptcy. The system fosters stronger supplier relationships through open communication and problem-solving, ensuring a more stable supply chain.

1. 10. Sustainability Insights and Risk Assessment: AI assesses supplier data for environmental and social impact, helping procurement professionals mitigate risks related to sustainability compliance and reputation.Unilever leverages AI for real-time supply chain visibility and risk management. Their AI applications include image capture for inventory management, satellite imaging for farm monitoring, and machine learning for product reformulation. These technologies help Unilever improve energy efficiency, predict deforestation risks, and reduce emissions, optimizing their supply chain operations. AI also analyses supplier data for environmental impact metrics like waste generation and energy usage, along with social responsibility considerations like labor practices.

The integration of AI in procurement is revolutionizing the industry by enhancing efficiency, accuracy, and strategic decision-making. From automated spend categorization and demand forecasting to risk management and supplier performance monitoring, AI-driven solutions are enabling procurement professionals to navigate complex data landscapes and make informed decisions. As AI continues to evolve, its applications in procurement will undoubtedly expand, driving further innovation and resilience in supply chain operations. Embracing these technologies is essential for organizations aiming to stay competitive and achieve sustainable growth in an increasingly dynamic market.

By Johann Rodriguez, Senior Consultant