Exploring Generative AI for Procurement 

AI in Procument | APD

Artificial intelligence (AI) has the potential to assist purchasing and supply chain professionals with one of their top pain points: an overwhelming amount of administrative tasks.  However, most of the purchasing leaders we talk to are at the beginning stages of understanding how they can use AI to “supercharge productivity” as many AI advocates like to say.  They simply haven’t identified the use cases for AI and put the resources in place to fulfill the potential. 

This article explores the top nine uses of generative AI for purchasing teams as described in several recent articles. 


We surveyed manufacturing purchasing leaders in November 2023, and the most common pain point cited as a high priority for their teams in 2024 was an overwhelming amount of administrative tasks. As Purchasing Leaders look to 2024, 53% of those surveyed reported this as one of their highest priorities.  This issue was also number one the last time we conducted this survey, eight years ago, when 46% cited it a high priority.   

Understanding the awesome potential that artificial intelligence has for addressing this persistent concern, we reached out to 25+ manufacturing purchasing leaders (Vice Presidents and Directors) through 1-on-1 interviews and roundtable discussions to find out how they and their teams were using AI.  In nearly every case, they had little to report – a few were beginning to apply generative artificial intelligence (mostly ChatGPT) to tasks such as drafting contracts or researching trends, but they were in the early stages of figuring things out. 

We surveyed recent articles available from online sources and found many good sources with meaningful advice for using AI in procurement.  Nearly all focused on generative AI – more specifically, Large Language Models (LLMs) such as ChatGPT and Bard.  These powerful tools are readily available and free to use and have the potential to offer great benefits to purchasing teams. 

Other types of AI, not addressed in this article, include Robotic Process Automation (RPA) and Machine Learning (ML).  As described by Precora, RPA automates simple repetitive tasks based on predefined rules, and ML makes predictions based on patterns found in historical data.   

An example of Machine Learning is the predictive pricing function in APD’s ProcureForce software.  ProcureForce orchestrates sourcing events for direct materials and organizes data in its proprietary Data Lake.  The Data Lake transforms supplier quote and cost details, enabling users to quickly access accurate cost estimates for new parts or design/volume changes based on physical attributes and volumes.  In addition, suppliers are provided real-time feedback on the probability their quote will be low cost for a given part.

Top Nine Uses for Generative AI for Purchasing Teams

Specific use cases described in eight recent articles are summarized into nine areas.  Links to the source articles are provided throughout, and the full titles and authors are provided at the end in case links become non-functional. The nine uses are:

  1. Sourcing
  2. Supplier management
  3. Spend analysis
  4. Strategy development
  5. Contract management 
  6. Negotiations
  7. Operational procurement
  8. Risk management
  9. Communication
  1. Sourcing 

Harvard Business Review explains how Unilever employs an AI tool to search through pitch decks and client types, aiming to find diverse suppliers and understand their capabilities. This tool is also utilized by both Unilever and Siemens for quickly locating alternative sources of supply, particularly for distributors of scarce patented products. 

Procurement Mag highlights two key applications of sourcing. The first involves using AI to rank suppliers and recommend them based on their performance metrics. The second application is leveraging AI to gain instant insights into sourcing strategies and discover new suppliers by analyzing market data and industry trends. 

Droppe identifies three distinct AI applications in sourcing: 

  • Gather and analyze external data to identify prospective suppliers more efficiently 
  • Generate RFQ documents based on simple questionnaires filled out by stakeholders 
  • Evaluate bids from numerous suppliers, weighing pros and cons based on predefined scenarios 

KPMG reports that approximately half the purchasing leaders they surveyed indicated they see generative AI impacting sourcing strategies in the next 3-5 years.  They paper identifies key AI applications in sourcing, Including RFx development, response review, and summarization.   

Precora’s article mentions two AI applications in sourcing: centralized access to supplier details and simplification of the vendor selection process.  For vendor selection, the article discusses how AI can more effectively pinpoint the best suppliers by analyzing supplier data. 

In another Precora article, specific AI use cases for supplier sourcing are detailed. These include identifying crucial factors for evaluating potential suppliers, incorporating sustainability and ethics in the supplier sourcing process, and creating RFPs that attract appropriate suppliers. 

Bain & Company identifies three specific AI use cases in Supply-to-Contract to aid sourcing: 

  • Drafting and developing RFx materials 
  • Finding potential suppliers for certain categories via supplier database reviews 
  • Extracting relevant information and providing initial ratings for reviews of commercial offers

Click here for our recent webinar on sourcing titled “Reducing Cost While Improving Value”

  1. Supplier Management 

KPMG emphasizes the AI’s capability to synthesize data from various internal systems. This includes monitoring changes in delivery performance, quality, and lead times. Generative AI can autonomously conduct scorecard readouts with mid-tier and smaller suppliers, thereby freeing up human managers to concentrate on strategic suppliers.  APD’s ProcureForce Data Lake not only stages all the data relevant to sourcing, but also is expandable to include other internal and external systems, making it a one-stop shop to be used by ProcureForce and other AI tools. 

Bain & Company identifies nine distinct AI applications in supplier management: 

  • Profiling suppliers by gathering market data 
  • Summarizing analysts’ reports 
  • Creating supplier onboarding materials 
  • Identifying potential sources of supplier innovation 
  • Automating communication with suppliers using chatbots 
  • Developing supplier scorecards and KPIs 
  • Tracking supplier performance against contracts 
  • Auditing supplier contracts for adherence to company guidelines 

Precora notes that generative AI aids in evaluating supplier performance, developing vendor management strategies, and enhancing relationships. In another article, Precora mentions AI’s ability to extract critical supplier information from documents, such as contact details, pricing agreements, product catalogs, and payment terms, thereby eliminating manual file searches. 

Droppe lists four key applications for AI in supplier management: 

  • Streamlining supplier onboarding 
  • Sending targeted communications 
  • Tracking supplier sustainability metrics 
  • Providing insights into supplier performance 

Procurement focuses on AI’s role in analyzing communication data, performance metrics, and feedback. These AI platforms can generate insights and recommendations to improve collaboration, identify improvement areas, and strengthen long-term partnerships.

  1. Spend Analysis 

MIT Technology Review emphasizes AI’s ability to provide data-driven insights that provide purchasing teams with a comprehensive view of spend as well as areas they might cut costs: “AI and analytics tools can provide greater transparency into overall procurement spending by automatically analyzing data and unlocking timely analysis.” 

Bain & Company lists three areas where AI can support spend analysis: 

  • Categorizing expenses and providing an overview of spending to category managers 
  • Validating invoices and classifying line items for the development of a spend cube 
  • Analyzing historical spend data to establish baselines and identify trends 

Procurement identifies two ways that AI can be applied for spend analysis: demand forecasting and price optimization.   The article states that accurate demand forecast can be generated through AI analysis of historical data and market trends.  It also points to AI platforms considering factors such as market demand, supplier quotes, and cost structures to generate optimal pricing recommendations. 

According to Precora, “AI algorithms provide procurement professionals with real-time and structured data that is ready for analysis.”  In another article, Precora describes how generative AI tools can not only categorize data but also provide insights into trends over time and identify noteworthy patterns or anomalies. 

Utilizing AI for Spend Analysis is now part of APD’s Cost Management Certification course – Click here to learn more..

  1. Strategy Development 

Precora describes uses for AI in planning inventory, budgets, and upcoming projects.  The article provides specific prompts for: 

  • Projecting procurement needs based on predicted demand 
  • Including specific considerations in vendor selection 
  • Developing realistic procurement budgets  
  • Optimizing inventory levels based on supplier lead times and demand patterns 

Bain & Co. lists three areas where AI can support strategy development: 

  • Reviewing internal documents to help identify the most critical points for procurement functional strategy development 
  • Identify macro trends affecting the business and purchasing team 
  • Summarizing market intelligence to develop and adjust category strategies 

KPMG points out that not only can generative AI assist with category spend planning and stakeholder-requirements gathering, but strategies can be updated in real time for every category, and personalized for every stakeholder. 

  1. Contract Management 

Procurement suggests that AI can extract key information from supplier contracts to flag non-compliance, suggest improvements, and highlight important clauses or deadlines. 

KPMG states that generative AI “Can read and understand contacts, recommend changes, and draft contract language.  It can also generate intelligence about the organization’s portfolio of contact and highlight compliance gaps and commercial risks.”   

Precora describes how AI enables faster and more accurate contract reviews by extracting key terms and conditions.   In another article, Precora highlights that generative AI can secure more favorable contract terms and assist in navigating disputes. 

Droppe shared “Contracts can be drafted efficiently using AI, ensuring compliance and highlighting risks. For example, you could implement AI to oversee your contract renewals, leading to fewer missed deadlines and breaches.” 

  1. Negotiations 

Harvard Business Review highlighted a software used by Maersk and Walmart to automate negotiations.  Maersk uses a chatbot to search for transportation rates withing existing agreements or secure a quote if needed.  Walmart uses the software to negotiate with representatives at suppliers for low-value items. 

Droppe describes using AI to prepare for negotiations analyzing current market trends, reviewing historical negotiation outcomes and assessing the financial stability of the supplier. 

Precora shares that buyers can use generative AI to prepare for negotiations by requesting suggested strategies suggestions, asking what potential objections might arise, or even simulating negotiation scenarios.  Also, they suggest buyers can get real-time insights on contract adjustments while at the negotiating table. 

It’s vital to remember that successful application of AI, as with any new technology, requires a solid understanding of negotiation fundamentals – Click here to learn more about APD’s Strategic Negotiation courses..

  1. Operational Procurement 

Bain & Co. lists five areas where AI can be beneficial for operational procurement: 

  • Reviewing and identifying gaps in procurement strategies 
  • Assisting stakeholders in accessing and understanding the correct procurement policies and procedures 
  • Streamlining the process of completing purchase orders 
  • Enhancing the review and cleanup of supplier master files 
  • Automating repetitive tasks that involve unstructured data, such as exception management in invoice and accounts payable reconciliation 

KPMG focuses on the utility of AI in handling non-standard requisitions through an automated assistant..  They emphasize the role of intelligent, high-touch support in helping users “find and buy what they need from the right supplier at the right price in compliance with eh company’s existing supply agreements and policies.” 

Droppe points out the potential of AI tools in preemptively flagging policy violations before requisitions are approved. AI can ensure adherence to country-specific rules for invoices and assist in calculating complex taxes. 

Precora discusses AI’s role in optimizing supply chain operations by analyzing key factors such as demand, inventory levels, transportation routes, and production schedules. 

Harvard Business Review shares insights from Rolls-Royce and Koch Industries on using AI to identify additional sourcing options within the existing supplier base. They mention a tool that prepares and sends RFQs with suggested costs to suppliers, who can then accept or modify the quote. 

MIT Technology Review illustrates a scenario where an AI-based procurement tool proposes a shopping cart for specific tasks, like hosting a holiday party for 150 people, suggesting items that might be overlooked otherwise. 

  1. Risk Management 

KPMG highlights the ability of generative AI to gather and synthesize risk data from diverse sources to predict risks and recommend resolution strategies. The key sources of this risk data include: 

  • Direct outreach to suppliers 
  • Analysis of supply market data 
  • Evaluation of supplier performance 
  • Review of changes in demand 

MIT Technology Review discusses AI tools’ capacity to recognize patterns leading to supply shortages and recommend alternative products. This enables procurement teams to establish buying policies that anticipate and mitigate the risks of items going out of stock. 

Precora emphasizes AI algorithms’ potential in predicting risks related to suppliers, markets, and geopolitical factors. Additionally, AI can combat fraud by flagging unusual procurement activities such as atypical buying patterns.  

In another article, Precora notes that generative AI can analyze a company’s historical vulnerabilities and broader industry concerns to recommend risk mitigation strategies, including the identification of alternative suppliers. 

Bain & Co. points out that generative AI can efficiently summarize public and other available information to evaluate a supplier’s fitness for business. This includes assessing financial stability, regulatory compliance, and reputational factors. 

  1. Communication 

MIT Technology Review emphasizes the efficiency of generative AI-powered chatbots in procurement. These chatbots can swiftly search through multiple sources to answer complex queries about orders, providing quick and accurate responses to urgent questions. This application is particularly beneficial in resolving order-related inquiries efficiently. 

Precora describes how generative AI tools can be used for both drafting and editing emails to ensure that message to suppliers or team members are clear and professionally presented.  They point out that in addition to fostering better communications overall, a well-crafted email can expedite issue resolution and simplify negotiations. 

Bain & Co. identifies three specific areas where generative AI can aid in communication: 

  • Facilitating automated communication with suppliers through chatbots, useful for clarifying questions and aiding in negotiations 
  • Generating training content for new purchasing team members, including the creation of personalized learning plans 
  • Writing job descriptions that accurately reflect the roles and responsibilities required 

KPMG notes that tech-savvy buyers in many companies are already harnessing generative AI tools for routine tasks such as research, writing category strategies, and crafting requests for proposals (RFPs). They foresee a future where procurement processes increasingly leverage AI for content creation and other operational tasks. 


The recent articles on generative AI applications illustrate just how much AI is changing the way purchasing teams work. This technology has the potential to make an immediate impact in many purchasing activities.  As more companies start using AI, purchasing teams will become more efficient, smarter, and better overall.

We explored more AI uses in purchasing on our recent webinar “Supercharging Purchasing Productivity with AI”

References: URLs and Article Titles 

https://hbr.org/ : How Global Companies Use AI to Prevent Supply Chain Disruptions (Read this article)

https://www.technologyreview.com/ : Procurement in the age of AI (Read this article)

https://www.linkedin.com/ : Simplifying Procurement with ChatGPT: 25 Helpful Prompts (by Precora) 

https://www.bain.com/ : How Would Generative AI Be Used in Procurement? 

https://procurementmag.com/ : Top 10: Generative AI platforms in procurement 

https://droppe.com/ : How To Leverage AI In Procurement 

https://kpmg.com/ : Unleashing the Power to Generative AI in Procurement 

https://www.linkedin.com/ : AI in Procurement: Benefits and Common Misconceptions (by Precora) 

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