Artificial IntelligenceInformation TechnologyProcurementSupply Chain

AI-Powered Procurement: Transforming Strategic Sourcing and Supplier Management

By Emily Newton is a tech journalist with over five years of experience covering how technology is changing industry and business. Keep up with Emily by connecting with her on LinkedIn

Artificial intelligence (AI) is a natural fit for supply chain management. An effective supply chain requires quick actions based on complex relationships between multiple factors, which AI excels at. Implementing AI in procurement is an excellent way to make the most of this practice.

Many organizations are changing their sourcing strategies in light of recent supply chain disruptions and regulatory concerns. Doing so effectively can be challenging with conventional means, but AI provides the edge businesses need to get ahead.

Use Cases for AI in Procurement

Capitalizing on AI is key to staying competitive — 31% of high-performing supply chains use AI in supply planning, compared to just 12% of lower performers. You can join these organizations at the head of the pack by applying AI in procurement in a few ways.

Comparing Potential Suppliers

One of the best ways to hone your procurement strategy with AI is to automate supplier analysis. It is not always immediately evident which supplier of a given resource is the most cost-effective or reliable. Machine learning models can simplify and streamline this decision-making.

AI can predict suppliers’ long-term costs or likelihood of delays to give a more accurate picture of what a partnership with a given company will look like. You can then decide who to source from according to your specific goals. Feeding these models real-time data can unlock further benefits by alerting you when one supplier is no longer the most affordable or reliable.

This AI analysis can also reveal when it’s best to handle things in-house. Starbucks saved over $500 million in supply chain costs by cutting ties with some third parties.

Automating Risk Assessment

Similarly, machine learning models can automate risk assessments to prevent supplier-side disruptions. Understanding these risks is becoming increasingly important as supply chains evolve. AI can identify warning signs humans may miss to provide more reliable assessments.

Predictive analytics engines can analyze past supply chain disruptions to learn which factors make a supplier risky. That could include being in an area prone to socioeconomic disruption, a history of delays or a lack of data sharing with partners. AI also analyzes these factors faster than you’d be able to manually.

You can also tweak these algorithms over time to look for new risk factors. For example, rising environmental regulations may make high emissions a concern, so you can adjust your AI solution to monitor ESG reports.

You can gain a lot from AI in procurement. As more businesses catch onto this potential, more will embrace the technology, so capitalizing on it now will help you compete in the future. The first step is learning where you can apply AI for maximum benefits.

Boosting Supply Chain Transparency

You can also use procurement AI to improve supply chain transparency. In many cases, a lack of visibility isn’t a matter of not having enough data but struggling to sift through all the information to find what you need. AI solves that problem by automating the tedious parts of the process.

AI-powered supply chain mapping tools pull data from supplier portals, news updates, third-party databases and other sources to provide as much context as possible. They also standardize this data’s format and provide a single point of access to make it easier for you to get the big picture. Many update in real-time for added visibility.

While AI is fairly new, supply chain mapping has existed for over a decade, so this is already a tried-and-true process. At its core, the analysis process is the same as conventional methods. However, AI provides the agility, standardization and accuracy required to keep up with today’s data volumes and fast-moving supply chains.

Automate Back-Office Tasks

Not all uses for procurement AI seem quite as disruptive on the surface. Back-office automation may be a less glamorous application, but it’s easy to implement and yields significant time and cost savings.

AI can reduce the time you spend on some procurement tasks by up to 60%, especially when you apply it at scale. Some of the most impactful processes to automate include billing, invoice factoring, accounting and scheduling.

While each of these tasks may not seem too time-consuming on their own, they take considerable hours out of the week across your organization. Automating them through AI reduces the time you spend on them and frees your employees to complete other work in the same time frame.

Streamlining Emergency Response

AI’s impact on supply chain transparency and supplier assessments will make disruptions less likely. However, unexpected situations can still happen. When they do, AI monitoring can make them more manageable.

The same factors your AI solution uses to assess risks and map your supply chain can serve as warning signs. Predictive analytics models can monitor real-time data from you and your suppliers to identify these signs, estimate their impact and warn you of the situation. These alerts give you a headstart in responding to the issue.

You may need to order more from another supplier when predictive models show one is experiencing a shortage. Alternatively, you could order less of an item when your AI expects a demand drop. Whatever the specifics, AI’s speed and accuracy give you time to minimize losses when disruption occurs.

Best Practices for Implementing Supply Chain AI

However you implement AI in procurement, you should keep a few best practices in mind. Failing to account for common obstacles will make it harder to achieve a positive ROI within an acceptable timeline.

First, you should manage your expectations about what the AI journey will look like. The biggest barrier to AI adoption in this sector is a lack of available AI tools, with 47% of supply chain executives citing it as a challenge. In light of this gap, you may have limited off-the-shelf options or may have to develop a proprietary AI model.

Start by determining which procurement AI application will benefit you the most, then compare that to available solutions. An off-the-shelf option will likely be best if you find one that serves that use case. Developing your own will be better if no third-party tools have what you need and you have the in-house talent or a reliable AI partner to build your model.

As you train your AI solution, make sure its training data is relevant to your intended end use. That means gathering data from your business and similar supply chains or the kinds of risks you want to watch out for. You need a lot of data to achieve optimal results, but too much variety can make it difficult to refine for your needs.

Cybersecurity should also be at the top of your mind. There were 242 supply chain attacks in 2023 alone, so you should restrict access to your AI’s data and monitor it closely to keep your sensitive information safe.

Finally, remember that your model will require ongoing optimization to reach its full potential. Review it at least once a year to see if you must adjust any parameters or otherwise patch it.

There Are Many Applications for AI in Procurement

You can gain a lot from AI in procurement. As more businesses catch onto this potential, more will embrace the technology, so capitalizing on it now will help you compete in the future. The first step is learning where you can apply AI for maximum benefits.