Artificial intelligence is opening up new avenues for growth and innovation.

For supply chains, AI offers new paradigms and approaches, delivering streamlined processes and results that are unprecedented. The technology reduces risks, operational errors, delays and waste, while simultaneously improving the customer journey. Through decision intelligence, it becomes possible to automate decisions, yielding insights that often aren’t obvious to the workforce.

AI is about more than fixing problems — it's fundamentally changing how supply chains operate.  

According to Forbes Market Insights, the global supply chain management market is projected to grow from $23.58 billion in 2023 to $63.77 billion by 2032, a compound annual growth rate of 11.7%. North America continues to dominates the market, with a 32.49% share in 2023.

Industry experts see increasing use of generative, agentic and predictive AI. These tools empower users to enhance teamwork between humans and machines.

AI-driven decision intelligence will be critical for faster optimization of business processes, coupled with better and more precise decision-making at all operational and tactical levels, but how does it work?

Agentic AI

Agentic AI creates "agents" that are capable of independently handling a variety of individual tasks, resolving issues or assisting in the pursuit of daily goals and key performance indicators. For example, they can support managers by processing large amounts of data, then analyzing proposed decisions to address complex operational and tactical challenges.

Semantic Search

Searches for information are changing from traditional results to more natural-language responses. The large language models that make up search engines will no longer need keyword searches, instead delivering well-articulated answers by using the semantic context of a query. For example, a dispatcher is responsible for purchasing components for pre-production and stocking of two new products. Historically, one would have to rely on experience for purchasing specifications — letting AI know that the two items are, say, a luxury watch and a cheap product. Simply by naming them, AI will understand, semantically and linguistically, how cheap and luxury products normally behave.  This leads to incredible precision in forecasting. 

AI-driven tools will be used to more quickly and accurately search documents and databases. Powerful enterprise searches will soon require the semantic indexing of data. and will work effortlessly and accurately via voice commands.

Natural Language Interfaces

Interaction with computers and machines will fundamentally improve through voice-based software control. It wouldn’t be a stretch to imagine natural language interaction becoming the new standard of operating mobile devices and desktops, and eventually complicated business software applications.

By enabling seamless communication, automating tasks, and providing predictive insights, generative AI can significantly increase the accuracy of estimated times of arrival, and overall performance across the logistics supply chain.  

A Case Study

A global automotive manufacturer adopted sophisticated AI technology for spare-parts distribution. The planning team selects specific items from a catalog of half a million-plus spare parts to be stored onsite, while optimizing both availability and lowest possible stock count. The parts are delivered mainly by rail, with inconsistent delivery times.

The company’s bespoke enterprise resource planning (ERP) system has been adapted to operational needs, but due to a lack of upgrades over time, requires manual processing of 8,000 to 10,000 items a week. The result is inaccurate forecasting of deliveries and corresponding safety stock levels.

By deploying intelligent AI-driven software, integrated with its ERP, the company was able to optimize forecasting through enhanced inbound planning visibility. The system is now helping to plan and manage the supply chain strategically and collaboratively. It calculates forecasts and key figures and orders proposals overnight, transferring them to the ERP system and making the forecasts ready for the next day. The items are sorted into groups for either automatic or manual planning, with results transferred into ordering forms for planners to process. As a result, ordering that previously could only be carried out once a week now occurs daily. And through the use of mathematical algorithms, items that can be easily forecasted are planned automatically.

Automation of the company’s planning stage is now around 30%, allowing decisions to be made faster, more precisely and intelligently. This allows the planning team to focus on items that are slower-moving or have longer lead times. 

Mapping AI Deployment

As with many new technologies, it can be tempting to jump on the bandwagon without a thoroughly developed plan. Mapping the supply chain should include both internal and external processes, to identify where AI can most impact business resiliency, productivity and competitive advantage.

After defining issues and setting objectives, data should be gathered from across the supply chain, ranging from sales to logistics. In the alternative, the business might choose one or two areas where the impact has been determined to be greatest. 

It’s important to partner with a vetted software provider to assist in mapping and measurement, prioritizing team acceptance and ease of use during testing, as well as the achievement of specific operational goals.

Monitoring is essential to measuring impact. By tracking KPIs, companies can ensure that the right tools are implemented and are flexible to react to a fast-changing business environment.

Optimizing AI with Human Intervention

AI is not a replacement for human intelligence, but a powerful tool to complement and it. The technology promises to free humans from repetitive tasks and decisions, while enabling a personalized experience for both content management and user guidance.

In the end, AI’s true potential lies in its collaboration with human expertise. Building effective processes demands not just data, but context — insights that only humans can deliver, to ensure intelligent decision-making in areas such as logistics, forecasting and operational process flow and optimization. 

In an increasingly intricate global landscape, the question for supply chain leaders is no longer whether to adopt AI — it’s how and when. The data unequivocally points to a future where AI-powered decision intelligence is a fundamental requirement for resilience. By strategically integrating AI — from agentic AI automating complex tasks to predictive AI sharpening forecasts, and natural language interfaces streamlining human-machine interaction — organizations can transform their supply chains from a series of vulnerable links into powerful, intelligent networks. It's about building a future where adaptability, foresight and unparalleled productivity are the new standard, ensuring continuous growth in a perpetually complicated world.

Justin Newell is chief executive officer of INFORM North America.