AI is more than a fad; unlock its potential in supply chain  

The world of artificial intelligence can be tricky to navigate — especially as a supply chain professional. Now, with ChatGPT and generative AI added to the mix, finding the right way to incorporate AI into your supply chain setup proves even more challenging. Leaders should remain cautious and focus on driving tangibly better business outcomes, not just on introducing AI for the sake of AI.

ChatGPT makes AI more accessible than ever, offering human-like interactions. With a low barrier to entry, high marketing benefits, and positive reception from users, vendors across the board are rushing to augment user experiences with generative AI and leveraging commoditised tools that make it easy to create a prototype in a matter of days or a few weeks. A “breakthrough” for some has quickly become a breakthrough for all, and soon, conversational user interfaces will be the norm.

That said, AI is and will continue to be a significant differentiator that separates winners from losers in the coming years – just not in the way that we would assume. ChatGPT’s ability to answer any imaginable online question has captured our imagination and gives the appearance of the ability to reason. However, this does not make it intelligent. Instead, it is a master at correlation, processing masses of data, and giving answers with a high mathematical probability of being right. Generative AI is just one of four artificial intelligence methods available alongside supervised, unsupervised, and reinforcement machine learning. Unlike the bells and whistles of ChatGPT, these traditional AI methods are the quiet workhorses of modern supply chain management, creating new value beyond what people can do.

Use the right AI tool for the job

Regarding the different types of AI, one size does not fit all. One method is no better than another – it’s about using the right tool for the job. For instance, you wouldn’t use a hammer on a bolt or a spanner on a screw — it is crucial leaders see through the hype to choose the proper AI method(s) for each business challenge.

So, what are the different types of AI technologies, how can supply chain leaders deploy these effectively, and where should they prioritise?

Supervised learning finds patterns across disparate datasets. These systems are trained to recognise what “good” looks like and learn to predict outcomes accurately over time. Unsupervised learning discovers hidden clusters – many of which are not apparent to people – without training or guidance. Reinforcement learning explores different options through trial and error, learning which actions to take based on the best outcome. Finally, generative AI uses large language models to interpret masses of unstructured data and generate new content with similar characteristics, often featuring the human chat-like interaction that made ChatGPT famous.

Supply chains’ AI workhorses

Supervised and unsupervised AI has been deployed at scale for decades, working within advanced supply chain management applications in functional planning, logistics, and channel management domains. Supervised AI recognises patterns across many real-time demand signals to predict what customers will order, instead of simply what companies hope they will order, to enhance the forecasting of finished goods. This lets businesses build the right products and stock them in the right location at the right time, the first time, to serve clients at the lowest cost and capture growth opportunities.

As for unsupervised AI, this can be used to support new product launches, automatically identifying items with similar characteristics, and inheriting their properties. This ensures availability, captures market share, and raises innovation returns while limiting financial liability.

In channel data management, supervised and unsupervised learning cleanse dataflows from thousands of distributors, resellers, and retailers to automatically correct errors and augment missing fields, transforming noisy data into decision-grade information.

In logistics, supervised learning finds patterns hidden in a myriad of transport data, such as routes, loads, and equipment types, to predict and compare freight rates to industry averages. AI is also used to predict future transportation capacity requirements by lane and mode, allowing shippers to proactively identify gaps in capacity and secure transport with preferred carriers at the lowest cost.

In each example, traditional AI is the unsung hero of supply chain, a workhorse running quietly in the background, without human intervention or fanfare, unlocking new value beyond what people can do today. It takes all four methods to build a successful AI strategy, so don’t let the siren song of generative AI cause you to focus all your efforts on a one-size-fits-all approach.

AI is nothing without good data.

Types of AI aside, data and an embedded AI strategy are vital to success.

Ultimately, it doesn’t matter how good your AI is if you don’t have the right data — and lots of it. There is valuable information relevant to the decisions and actions taken in making, moving, or selling goods from your extended supply chain. These contain demand and supply signals that influence growth opportunities or risks — such as shifting consumer behaviour, materials constraints, logistics capacity shortages, and changing trade regulations.

This includes data from your internal operations and along the entire value chain of multiple tiers of partners, suppliers, distribution, transportation, customs, and border crossings. Access to this information relies on a network that can connect the dots and lower entry barriers, getting leaders and AI systems the data they need to make better decisions. The more data points you have aggregated, the better any AI solution will perform.

Embedded AI unlocks better day-to-day decisions

AI has become a commodity. Unlocking its value is no longer about having the best technology but how it’s applied to solve business challenges. The most significant win for supply chains comes from embedding AI in day-to-day decision-making, ensuring that the tools used to make, move, and sell goods have AI at their core. It cannot be an overlay or an afterthought; it must be used to drive behaviour at the time of decision.

Ultimately, embrace the conversational aspects of generative AI, but don’t stop there. Adopt a “yes and” approach, building a strategy that considers all four methods and uses the right tools for the right job. Make data the top priority – especially from your value chain – and embed AI in your day-to-day decisions. AI will shape the future of supply chain management; leaders need to know where and how to use it to unlock new value for their organisation.