What is artificial intelligence in procurement?

In simple terms, artificial intelligence refers to the capability of machines or computer programs to mimic certain aspects of human intelligence and perform tasks. AI systems can learn, problem-solve, understand human language, reason, and even “see” their own environment. AI in procurement is the use of this advanced technology to automate and enhance key procurement processes in an organization – such as contract management and strategic sourcing . Procurement teams are increasingly using AI to increase efficiency, cut costs, mitigate risks, and improve decision-making as they navigate new business demands and market challenges.

Types of procurement AI

There are five main types of AI used in procurement today:

1. Artificial intelligence (AI): blanket term for any software or algorithm that can be considered “smart”
2. Machine learning (ML): a subset of AI, machine learning algorithms can recognize patterns in data sets and use them to make decisions, forecasts, or predictions
3. Robotic process automation (RPA): algorithms that mimic human actions to perform repetitive tasks. RPA is not technically considered to be a type of AI but can be powered by it.
4. Natural language processing (NLP): algorithms that can understand, interpret, and generate human language—such as chatbots, copilots, and virtual assistants
5. Optical character recognition (OCR): algorithms that can recognize and extract text from images and scanned documents, such as paper-based invoices

Generative AI in procurement

Since ChatGPT went live in late 2022, generative AI has been the hot topic in boardrooms around the world. With its ability to create new content via a simple user interface, generative AI is poised to disrupt businesses and even entire industries. Some forward-thinking procurement teams are starting to use it to generate  RFP documents, create entirely new processes, and autonomously shortlist suppliers. Generative AI in procurement is in its infancy, but its potential is enormous.

AI in procurement use cases

Procurement is under intense pressure to deliver cost savings, mitigate risk, improve sustainability, and play a more strategic role in the business. To meet these goals, and keep up with the rapid pace of change, teams need to be incredibly agile—and to become more proactive, and less reactive, in everything they do. AI is helping in some key areas of procurement:

• Spend classification and analysis: Spend classification algorithms can rapidly search through line items and highlight keywords to tie to spend categories with near-perfect accuracy. AI-powered spend analysis can also help teams proactively identify opportunities for cost savings and form the basis for better sourcing, category, and spend management strategies.
• Global sourcing strategy: By analyzing large global datasets, machine learning algorithms can identify shifts in supply trends, predict future developments, and help inform global sourcing strategies.
• Guided buying: AI-assisted item recommendations bring together procurement policies to make it simple for users to find what they're looking for, encourage spending within the company's catalog to avoid unnecessary costs, and let the procurement department offer tailored help. It also provides quick access to preferred suppliers while incorporating helpful guardrails.
• Intelligent sourcing and supplier management: AI-powered software can analyze supplier databases, market trends, historical data, ESG reports, and other factors to recommend the best suppliers for specific needs. It can also provide comprehensive insight into a company’s supply base, helping to improve supplier performance and advance strategic priorities.
• RFX creation: AI can automatically generate request for proposals, (RFPs), request for quotations (RFQs), and other RF documents—from developing supplier lists to drafting key questions.
• Supplier risk management: AI algorithms can rapidly detect sudden changes with a supplier or vendor and assess how that change will impact risk. They can also mine millions of different data sources to alert companies to potential risk across the supply chain.
• Compliance: By using AI to structure contract, invoice, and PO data, companies can automatically compare payment terms, eliminate duplication, and identify non-compliance.
• Data extraction: Natural language processing can extract data from invoices and contracts to identify risk and fraud, provide greater insight into business spend, and speed up processes end-to-end. NLP can also capture data from external sources such as market indices, company credit ratings, social media, and publicly available information about suppliers to spot opportunities and risk.
• Contract lifecycle management: AI-powered tools can automatically generate first drafts of contracts, support negotiation, and flag potential risks in contract language. They can also monitor terms and conditions as well as deadlines to ensure compliance.
• AP automation: Intelligent RPA can eliminate manual tasks in accounts payable processes, speeding up invoice processing and approvals, improving accuracy, and ensuring compliance. Optical character recognition can read key information off paper-based invoices to improve the process and digitize documents.

Benefits of AI in sourcing and procurement

The integration of AI in sourcing and procurement-related processes offers many benefits, including:

• Smarter decision-making: AI can analyze large volumes of data quickly and accurately. This data-driven approach provides procurement professionals with actionable insights into spending patterns, supplier performance and market trends. AI-powered predictive analytics and scenario analysis can also help teams assess options, mitigate risk, and make better sourcing and spend decisions.
• Efficiency and automation: AI-powered automation of repetitive and time-consuming tasks —such as data entry and invoice processing—improves efficiency and frees up procurement professionals to focus on more strategic work.
• Cost savings: With AI, organizations can improve supplier selection, negotiate better deals, and more accurately forecast demand—for significant cost savings. They can also analyze spending patterns to identify, and act on, additional opportunities to cut costs.
• Risk mitigation: AI tools can proactively identify and assess risks associated with suppliers, market conditions, and regulatory changes—so procurement teams can mitigate supply chain disruption before it happens.
• Better relationships with suppliers: By clearly stating requirements and expectations in requests for proposals, and by monitoring and evaluating supplier performance, AI can contribute to the development of stronger, more reliable supplier relationships.

Challenges of implementing AI in procurement

While the implementation of AI in procurement processes offers significant benefits, it can pose some challenges as well.

• AI needs very large volumes of high-quality data to accurately train algorithms and guide decision-making—so companies need to invest in data quality and maintenance initiatives to avoid sub-par performance.
• AI solutions also need to be integrated with other procurement software and ERP systems, which often require middleware, APIs, and customization.
• In addition, the right skills and expertise are necessary to implement, manage, and run AI, and these are sometimes in short demand.
• And lastly, AI systems often rely on sensitive data, so robust cybersecurity, encryption, and data privacy are essential.

Navigating these challenges requires a thoughtful approach, but organizations that do it right can unlock the immense benefits of AI in procurement.

How to use AI in procurement: Best practices

Here are some best practices for successfully integrating AI into your procurement processes:

Step 1: Define clear goals

From cost-savings to greater efficiency and better decision-making, having clear goals will help guide your implementation strategy.

Step 2: Start with a small pilot project

Trying to transform all your procurement processes at once is a recipe for disaster. Find a simple, straightforward use case—such as automating your existing sourcing event process—to start. This way you can assess the effectiveness of your AI solutions in a controlled environment, identify any challenges, and make adjustments before scaling up.

Step 3: Ensure data quality and volume

Capture as much relevant data as you can—and clean and prepare it to make sure it’s high-quality, consistent, and complete—before feeding it into AI models. Addressing data issues upfront is key to the success of AI. Garbage in, garbage out, as they say.

Step 4: Bring in key stakeholders

Collaborate with fellow procurement professionals as well as finance and IT teams early in the process and assign an executive sponsor as a stakeholder. This step is crucial for building an understanding of key needs, ensuring alignment with business goals, and securing buy in.

Step 5: Integrate with existing systems

To minimize disruption and maximize the benefits of AI, it’s critical to integrate AI solutions with existing procurement systems, ERP, and other enterprise applications.

Step 6: Provide training and change management

Help procurement professionals become familiar with AI tools—and encourage user adoption—by providing training and demonstrating how AI can help with their daily tasks. Implement a robust change management strategy and show how your procurement team’s expertise can be augmented—and not replaced—by AI technology.

Step 7: Keep it ethical and secure

Regularly audit AI models and monitor with human oversight for fairness, compliance with data privacy regulations, and ethical considerations—especially bias in algorithms. Implement robust cybersecurity methods to protect sensitive data and build trust among users.

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