AI in Procurement: Benefits and Use Cases

Few teams pull together more enterprise data than those in procurement, which operate at the nexus of supply chain management and finance. As such, procurement teams are primed to apply AI to that data to analyze it faster and more insightfully than ever. To use AI wisely, procurement teams must understand the various types of AI, its benefits and challenges, and best practices for success.

What Is AI in Procurement?

In procurement, AI can perform some tasks once handled by humans, such as requesting supplier bids and analyzing costs, with the goal of doing those things faster and with fewer errors. Procurement managers are starting to see how AI automation can help them predict and react to market conditions, mitigate supply chain risks, and manage supplier relationships. Ultimately, AI can be applied to help inform better procurement decision-making, reduce costs, and improve operational efficiency.

Key Takeaways

• Most procurement teams are still in the early phases of AI adoption.
• There are many potential uses of AI in procurement, including researching and managing suppliers and automating key aspects of the buying process.
• Over the next few years, procurement teams will increasingly depend on AI to help improve efficiency, cut costs, and predict rapid supply network shifts.

AI in Procurement Explained

Procurement teams require a wide range of data, from both internal and external sources, to understand spending, demand patterns, purchasing specifications, market conditions, and much more. They also need more powerful ways to analyze their data and set the best course of action. According to a 2024 Deloitte study, 92% of chief procurement officers have evaluated GenAI capabilities, with nearly 11% spending more than $1 million of their annual budgets on AI sourcing and procurement tools. Priorities include automating supplier management, more accurately identifying risks, and more thoroughly evaluating current and potential partners. These and other improvements have the potential to help chief procurement officers reduce cost and mitigate risk in procurement and throughout the supply chain.

Benefits of AI in Procurement

By automating certain procurement tasks, AI can help boost efficiency while helping decrease costs and mitigate risks. AI can also be deployed to help improve demand forecasting, spend analysis, and vendor management. Read on for more on these and other benefits.

• Improve efficiency
  AI can be used to automates procurement tasks to help improve productivity and reduce administrative burden, freeing procurement staff to focus on more strategic activities. AI can help cut the time it takes to complete basic procurement tasks by up to 80%, according to a 2023 KPMG study. The same research concludes that well over 50% of procurement labor can be automated to help increase efficiency and reduce cost. One commercial property developer reportedly uses AI to collect procurement data 92% faster compared to manual methods.
• Reduce potential for human error
  AI can be used to automate repetitive tasks, which can in turn reduce the potential for human error. Such tasks include closing supplier contracts and submitting and approving purchase orders. If and when mistakes do inevitably occur, AI error detection capabilities can help flag them. One water treatment company reportedly uses AI to improve the accuracy of how its procurement spending is classified by more than 90%.
• Enable scaling
  AI procurement systems can scale to process data in response to changing business needs and market conditions. That scalability is key as procurement teams move from siloed, manual operations to connected, automated ones that use much higher volumes of data, enabling these teams to share information faster and make more informed decisions. For example, insights culled from sources of historical spend and demand forecasting—each conceivably a sizable data set—could guide decisions that reduce unnecessary costs.
• Cut costs
  By helping procurement teams choose and manage suppliers, AI usage can lead to better relationships and cost savings. One example: a global printing company uses an AI-supported procurement application to negotiate volume discounts from approved indirect suppliers in return for early payments. AI data analysis and pattern recognition can give teams deeper insights into spend across categories, recommending specific steps to lead to lower costs. Procurement managers can use AI to get fast answers to any number of queries to gather information to make more informed decisions, such as the share of spend at risk due to extreme weather events in certain regions and recommendations on suppliers in other parts of the world.
• Reduce reactivity
  By providing insights faster, AI can help procurement teams avoid unpleasant surprises. In the past, procurement was mostly reactive due in large part to a lack of spending visibility across data silos and time-consuming, error-prone manual processes. By boosting visibility and driving smarter, faster workflows, AI can help create more time for strategic tasks such as spend analysis and financial forecasting.
• Enhance decision-making
  Procurement decision-making can be guided by AI and analytics applied to data pulled from numerous sources—such as general ledgers, purchase orders, and supplier transactions—faster than ever before. Moreover, AI systems can adapt and learn, producing ever more precise analysis and impactful recommendations.

Types of AI in Procurement

AI comes in different forms, including the subsets of machine learning, natural language processing, and computer vision, as well as robotic process automation, a complementary technology. More on these below.

• Artificial intelligence (AI)
  The umbrella technology, artificial intelligence, refers to algorithms that exhibit “smart” or human-like behaviors, such as the ability to recognize patterns and make recommendations. Algorithms are simply rules for solving particular problems. AI applications in procurement perform predefined, specific tasks and thus are considered a form of “narrow AI.”
  
  Generative AI is the type of AI most frequently used in procurement. GenAI is able to generate content such as text, images, and videos. To do so, it processes large amounts of data to create content. The GenAI features embedded into some vendors’ procurement applications include an AI assist feature to tailor supplier communications or draft reports and contracts.
• Machine learning (ML)
  Machine learning is a subset of AI used to detect patterns and make predictions. Not all AI contains ML, but much of AI uses ML techniques. Within the context of procurement, an ML model can analyze past purchasing data and market trends to predict future demand.
• Robotic process automation (RPA)
  Robotic process automation uses bots to automate repetitive tasks such as filling out forms, generating reports, and processing transactions. RPA is not technically a form of AI, but it complements AI to make processes more efficient. For example, an automated procurement system might use RPA to create invoices and onboard suppliers faster and without the errors that often hamper manual processes.
• Natural language processing (NLP)
  Built on powerful algorithms, natural language processing—another branch of AI—enables computers to comprehend and manipulate human language. NLP can understand and analyze written or spoken language, allowing procurement teams to mine useful insights from textual data. In procurement, NLP can extract information such as key terms and conditions from RFP responses, allowing for deeper insights to inform the selection process.
• Computer vision
  Computer vision is a field of AI that enables computers to interpret and understand images, including videos. For example, it can examine images of products, logos, or invoices to detect procurement errors or situations in need of attention, such as low supply stocks. Companies can gain literal visibility into inventory to reorder key items or avoid unneeded purchases.

Procurement AI Use Cases

Because AI powers faster, effective processes, it has uses throughout procurement management. Automation is a common thread, helping organizations complete tasks almost instantly and with fewer human errors while providing data insights that can help reduce costs and mitigate risks.

• Predictive Analytics and Cost Optimization
  AI algorithms can analyze large volumes of procurement data—for instance, past sales, market trends, and weather or economic factors—to help predict demand and lower costs. Real-time reporting helps procurement professionals keep ahead of demand and adjust supplier selection, quantities, and spend. AI-based analyses also help set inventory levels and avoid stockouts, striking a balance between reducing costs and keeping stakeholders and customers happy.
• Task Automation
  AI can automate procurement tasks to help boost efficiency and savings. These tasks include researching, analyzing, and managing suppliers; and generating RFPs. By accelerating such tasks, AI can shorten procurement cycle times, which—depending on an organization’s size—can save it hundreds or thousands of hours a year and potentially millions of dollars. Relieved of manual tasks, employees can spend more time on higher-value activities such as refining supplier performance criteria or reworking procurement strategies.
• Purchase Order Automation
  Old-school purchase ordering is a manual effort, slow and often riddled with errors. AI can automate tasks such as sorting, prioritizing, and processing purchase orders to speed things up and reduce mistakes. It can extract and validate data from purchase orders and, if everything checks out, generate orders. Some AI tools keep customers informed as the purchase order process unfolds to set delivery expectations. One identity verification provider reportedly uses AI to monitor pending purchase orders and find mismatches and other issues.
• Virtual Assistants
  GenAI-based virtual assistants can understand and interpret general procurement-related queries, providing information on an array of topics. These bots can help boost productivity by creating category and market reports, summaries to share across teams, and other content, like describing key trends for management.

Uses of Generative AI in Procurement

With its ability to generate content, GenAI has numerous uses in procurement. Some are general, such as organizing data, while others are quite specific, such as identifying risk and sustainability improvements.

• Data Organization and Summarization
  GenAI can organize data faster and more logically than humans can on their own, often with fewer errors. This creates the foundation for more compelling data summaries that highlight key data points, insights, and recommendations. For example, a procurement manager can request a summary of prices and spend in crucial categories and have it in minutes to review instead of hours.
• Data Processing and Labeling
  GenAI can improve data processing by quickly cleaning raw data, removing outliers and inconsistencies to enhance analysis. Proper tagging or labeling of data is key to helping AI systems learn, make predictions, and generate useful content. For example, well-tagged data sets can help large language models deepen their understanding of vendor price comparisons.
• Risk Identification
  Per a 2023 Deloitte research report, 70% of chief procurement officers see procurement risks rising. GenAI can make it easier to pinpoint risks throughout the procurement process. It can be applied to help root out risk among suppliers by analyzing their past performance. It can also answer questions on the risk of supply disruptions and the effects they may have on sales.
• Sustainability Improvements
  As companies strive to meet sustainability goals, procurement teams must gather and analyze copious amounts of data—a process that has always been time-consuming and marked by guesswork. GenAI can be used to identify suppliers that may be able to meet sustainability requirements, helping mitigate compliance risk while making sourcing more efficient.

Challenges of Procurement AI

Deploying AI in procurement comes with its own cultural, technology, security, and other challenges, discussed in more detail below.

• Organizational Adoption
  If an organization tends to be slow in adopting new technologies, its procurement team may find it challenging to implement AI. Unfortunately, some executives still believe AI is a futuristic promise, not a smart investment that will pay off in the short term.
• Data Quality and Access
  Procurement data is often scattered throughout many sources. This can result in incomplete, inconsistent, inaccessible, and erroneous data—hardly the foundation for rigorous AI analyses. It doesn’t help when procurement teams can’t get information from other corporate departments due to incompatible legacy ERP applications.
• Integration with Legacy Systems
  Problems often arise when companies try to apply AI to data locked in legacy procurement systems. Such systems are usually an impediment to collecting rich data sets and acting on key insights. Procurement teams are more likely to benefit from AI analyses when their ERP systems integrate inventory and supply chain data with procurement data.
• Data Privacy and Security
  AI-based procurement systems, especially those connected to the systems of suppliers and other third parties, can create security vulnerabilities. The complexity of such networked systems can obscure the flow of data, making it harder to confirm that data is handled in compliance with applicable privacy laws.

Best Practices for Using Procurement AI

The following best practices can help organizations use AI to improve their procurement processes.

1. Establish clear goals
   Before applying AI to procurement processes, identify specific pain points and priorities for improvement, as well as organizational or technical barriers. Whether you’re automating purchase orders or trying to achieve more precise spend analysis, setting clearly understood and realistic goals will help.
2. Understand your data sources
   For AI systems to succeed, procurement teams must trust the tremendous volumes of data it uses. Follow stringent data governance protocols. Cleanse, normalize, and validate data from your sources to know what you have and how it can be used.
3. Stay centered on user needs
   What functionality do team members need from the procurement system to do their jobs better? Avoid unnecessary features that only add complexity. Choose a system with a simple and intuitive user interface.
4. Start with a narrow focus
   Instead of shooting for the moon, begin your AI implementations with small projects that offer quick wins. This will let you test and become accustomed to AI features, evaluate their effectiveness, and make tweaks before rolling them out on a larger scale.
5. Enable your teams
   Procurement professionals don’t need to be data scientists to use most AI tools, but they will need training, and time for trial and error, to use such tools effectively. If your budget allows, consider hiring additional staff with procurement AI experience.
6. Build trust and address concerns
   Procurement AI is a team sport, requiring collaboration among procurement, supply chain, and finance teams. By sharing goals, roadmaps, standards, best practices, and success stories, you can alleviate concerns, promote collaboration, and build trust.
7. Evaluate and iterate
   After establishing key metrics, monitor and evaluate the performance of your AI tools. Some companies measure performance by tracking the value of AI procurement across spend categories. However you measure, be sure to gather user feedback to identify ways to improve.

Enable your Procurement Team with Oracle

AI and GenAI features built into Oracle Fusion Cloud Procurement, part of the Oracle Fusion Cloud Enterprise Resource Planning suite of applications, can help procurement professionals predict shipping lead times, classify different types of spending, dynamically apply discounting, quickly identify and add qualified suppliers, and much more.

AI in Procurement FAQs

How can AI be used in procurement?
Procurement teams use AI to help predict and lower costs, automate key tasks, generate content, select suppliers, and manage supplier relationships.

Can procurement be replaced by AI?
AI augments the skills, experience, and judgment of procurement professionals. It doesn’t replace those skilled people. In fact, AI is expected to create new jobs for procurement pros skilled in the technology.

Which companies use AI for procurement?
AI procurement tools are deployed by companies large and small, including some of the world’s top retailers, food processing organizations, and consumer packaged goods companies.