AI in Procurement: Key Benefits, Uses Cases and Future Trends

Understanding AI in Procurement

Artificial intelligence is a subfield of computer science that focuses on creating intelligent agents capable of performing tasks that would typically require human levels of intelligence. These tasks include problem-solving, speech recognition, and decision-making, among others.

In the context of procurement, AI is regarded as a strategic technology that can automate and enhance procurement processes, such as contract negotiation and sourcing. Equally, AI can lead to important efficiency gains, cutting procurement costs down and improving business decision-making. Finally, AI technologies are also critical to reducing procurement-related risks and creating robust supply chains.

Below you can find a list of some of the most relevant AI technologies for procurement:

Machine learning

One of the most relevant topics in AI is machine learning, a subfield that focuses on how computers can learn from data and make decisions without being explicitly programmed. Think of it as teaching computers to learn from experience, much like how humans do.  In essence, machine learning is the method by which AI gets the "intelligence" part of its name. You can learn more about the topic in our Understanding Machine Learning course.

Deep learning

Another important domain in AI is deep learning. Deep learning is a type of machine learning that focuses on a type of machine learning called neural networks, which mimics how our brain works. Neural networks allow computers to learn from experience and understand the world in terms of a hierarchy of concepts. Thanks to neural networks, researchers have been able to solve some of the most complex problems, including image and video processing. Equally, a type of neural network called the transformer is key to understanding the development and rise of generative AI.

Natural language processing (NLP)

A field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and generate human languages in a way that is both meaningful and useful.  Check out our Introduction to Natural Language Processing in Python course to find out more.

Robotics

AI is key to the development of robotics. AI technologies can be integrated into so-called RPA (robotic process automation) to enhance their capabilities and enable them to perform more complex tasks. AI in robotics allows robots to learn from experience, adapt to new situations, and make decisions based on data from sensors.

Generative AI in Procurement

When we talk about AI for procurement, we must speak of generative AI. Generative AI systems, like ChatGPT or Google Gemini, can significantly boost productivity and efficiency in business processes (check out this conversation with Bernard Marr in our DataFramed podcast to learn how generative AI is changing business and society). In the context of procurement, AI can be used in a variety of contexts, including:

• Automating contract drafting and review. Thanks to its capabilities to generate text, generative AI can be used to rapidly create procurement contract drafts, as well as check legal documents to ensure correctness and spot potential risk and non-compliance issues. 
• Generating procurement reports and insights. These tools can seamlessly analyze vast amounts of data and summarize large documents to generate insightful information in the form of reports, briefings, or presentations.
• Enhancing supplier negotiations through scenario simulations. Generative AI can also simulate human-like decision-making, drafting negotiation strategies, and generating responses that closely mirror a seasoned negotiator’s tactics. For example, gen AI can play different roles in mock negotiations to plan a strategy by examining arguments and counterarguments.

AI Use Cases in Procurement

In the following sections, we will cover some of the most promising use cases of AI in procurement.

Procurement negotiations

When it comes to sourcing, traditional procurement negotiations often mean repetitive, time-consuming tasks, data-heavy analysis, and endless interactions between suppliers and procurement teams to get the right deal.

AI and, particularly, generative AI, are rapidly changing how procurement experts address these negotiations. Tools like ChatGPT can be used to automate proposal evaluations, extract key insights from large volumes of data, and pull relevant and up-to-date information in preparation for negotiations.

If well-designed, AI-powered assistants can even conduct autonomous negotiations with suppliers based on historical data and predefined parameters. In practice, generative AI assistants can enhance internal and external relationships by eliminating friction due to different time zones, busy schedules, and overwhelming data. Instead, it aids conversations with talking points that better reflect the counterparty’s priorities. 

Together, these applications enable more informed, efficient, and dynamic procurement negotiations, significantly reducing the manual effort involved during negotiations.

Spend analysis and optimization

Spend analysis is the process of analyzing spending data to find patterns, identify bottleneck and cost-saving opportunities, improve performance, and optimize procurement strategies. AI and machine learning can help automate and speed up every step of the spend analysis, from collecting and cleansing data from various sources to classifying and analyzing data to extract valuable insights.

Supplier risk assessment and management

One of the most illustrative use cases of AI in procurement is the assessment of suppliers’ risk profiles. Machine learning and deep learning models are used to create granulated categories of suppliers and predict outcomes based on data, such as security and privacy controls, finances, ESG practices, corporate policies, incident response programs, third-party relationships, and other factors that may affect the supplier’s continuity and resilience.

Demand forecasting and inventory management

Another valuable application of AI is inventory management. Machine learning models analyze historical sales data, customer trends, and other factors to predict future demand and minimize the risk of overstock. In the same vein, AI can predict customer demands based on previous records, market trends, and even real-time information.

Procurement fraud detection

AI is also enhancing fraud detection and prevention in procurement processes. By leveraging historical records of suppliers’ data, machine learning models can analyze millions of transactions to detect subtle patterns indicating any fraud faster and also more accurately than humans. Companies can use these analyses to catch fraudulent transactions in real time, reducing fraud losses.

Case Studies of AI in Sourcing and Procurement

Companies worldwide are starting to implement AI solutions to improve their procurement operations. Below, you can find two use cases from companies in different sectors:

Zara

Zara is an international fashion retailer based in Spain. The company has integrated AI in various aspects of its business operations. That includes not only consumer behavior, but also procurement. In particular, it has adopted an holistic approach, that involves using AI in every step of the supply chain.

Probably the most innovative example of this approach is the use of microchips in the security tags of all its clothing items. These microchips enable real-time tracking of all the products, from production to sale, which allows Zara to have a comprehensive and precise idea of the state of its inventory. 

As a result, Zara can accurately control its stock levels, reducing oversocking and stockouts, and, at the same time, increasing operational efficiency. For instance, Zara can immediately locate an item that is running low in a specific store and quickly restock it from the warehouse or another store.

Coca-Cola

Coca-Cola recently partnered with Microsoft to leverage its cloud and AI capabilities to optimize its supply chains. 

By harnessing Microsoft’s Azure OpenAI Service, Coca-Cola is focusing on optimizing various supply chain processes. In particular, with this partnership, the company aims at forecasting demand, improving inventory management, and streamlining distribution logistics. All these upgrades will allow Coca-Cola to reduce operational costs and enhance overall supply chain efficiency.

Challenges and Considerations​​

While the benefits of AI in procurement are clear, implementing a successful AI solution is not always easy, especially in a sector like insurance, where there is a lot of sensitive data involved. Let’s analyze the most important challenges.

Ethical and regulatory challenges

Despite the unique capabilities of AI, it’s important to consider its potential risks and regulatory concerns in procurement. In the case of generative AI, despite its great promises, this technology also introduces new risks, such as misinformation and potential procurement scams. 

In addition, compliance with the rapidly evolving legal landscape is mandatory for procurement experts to ensure data protection and fair and safe use of AI. A great example of how the future of compliance will look is the recently approved EU AI Act, a comprehensive regulation that requires companies, including insurers, to advance strict regulatory measures. 

Check out our EU AI Act Fundamentals Skill Track to learn about this innovative and ambitious rule and how to stay compliant.

Integrations and scalability

How to integrate AI solutions with existing systems and make them scalable can be complex and requires modern infrastructure, which many companies lack. Instead, most companies normally rely on legacy procurement practices and software that may not easily interface with cutting-edge AI tools. 

Combining machine learning and artificial intelligence technologies into these established processes requires careful planning and customization to avoid disruptions. Insurance companies need to assess their current infrastructure, identify potential integration challenges, and invest in necessary upgrades to ensure that both systems work harmoniously with existing technologies.

If you want to know more about how to implement AI tools into your technology stack, we highly recommend you read our separate guide on AI Integration.

Upskilling teams and people

Building a successful AI strategy requires considerable effort and resources, but even companies with big pockets fail to implement AI solutions. Why? Because they lack enough skilled employees with AI literacy. 

According to a survey made by McKinsey, best-in-class companies place 22% of procurement employees in analytics teams. This suggests that companies will need to invest and increase the number of data profiles available to scale through external hiring of data-savvy profiles or reskilling of existing teams.

However, recruiting and retaining talent with the proper skill set can be challenging, especially given the competitive market for tech professionals. Investing in upskilling and reskilling for existing staff can also help bridge the skills gap and ensure that the organization can fully leverage AI capabilities. 

Fortunately, DataCamp is here to help procurement teams. With our DataCamp for Business solution, we can help your company become data and AI literate. With a scalable solution that can work for teams of any size, along with customizable learning paths and detailed reporting, DataCamp for Business can help you transform your business and become AI-ready. 

Future Trends in AI and Procurement

As we look towards the future, the importance of AI in procurement is set to grow exponentially. 

According to research by Gartner, published in January 2024, more than half of supply organizations have plans to implement Gen AI over the next year. This trend will likely persist in the coming years as current AI tools become more powerful and new, procurement-specific solutions reach the market.

The increasing interest of procurement leaders in AI is not only about operational efficiency; it’s also about sustainability and regulatory obligations. With more and more governments willing to advance sustainable goals through procurement regulations, AI is regarded as a key tool to assist businesses in evaluating suppliers based on environmental, social, and governance (ESG) parameters, ensuring that procurement methods correspond with sustainability goals.

Yet, the same Gardner report finds that just 14% of the surveyed procurement leaders have confidence in their teams’ abilities to incorporate AI in their procurement processes. We already know the reason for this. As we found in our State of Data & AI Literacy 2024 Report, the AI literacy skill gap is acute across sectors, with 62% of surveyed leaders believing their organization has an AI literacy skill gap. 

That’s why AI literacy is going to play a critical role in advancing and speeding up the integration of AI in procurement. DataCamp for Business can help your business bridge this AI skill gap, with customizable, scaleable learning paths, suitable for organizations of all sizes. Get started today by requesting a demo.

Conclusion

AI is one of the key drivers of change in the procurement business. The application of AI and machine learning in procurement has the potential to automate many time-consuming tasks, cut operational costs, make supply chains more robust, and strengthen procurement relationships.

As these systems become smarter, procurement teams with a solid grasp of AI fundamentals are likely to hold a significant edge over their competitors. We will tell you more about the importance of having a solid data and AI culture in our upcoming webinar Increasing Your Organization's Data and AI Maturity

Request a demo to learn how DataCamp can guide you through the process of upskilling your entire team and building a data-positive culture. In the meantime, check our dedicated materials on AI:

• AI Business Fundamentals skill track 
• How to Learn AI From Scratch in 2025: A Complete Expert Guide
• EU AI Act Fundamentals
• What is AI Literacy? A Comprehensive Guide for Beginners
• Introducing The State of Data & AI Literacy Report 2024 
• The Learning Leader's Guide to AI Literacy
• Leading platform for AI literacy across your entire business