From anticipating customer demand to optimising delivery routes, from monitoring supplier performance to predicting maintenance needs, AI has the power to transform the supply chain as we know it. 

Read also: How Artificial Intelligence Is Reshaping Global Supply Chains

This, however, remains contingent on one critical factor: readiness. Many organisations are in fact still far from being structurally, culturally or technologically prepared to integrate AI at scale, and AI is too often seen as no more than a sleek dashboard or a clever algorithm that can be dropped into existing workflows. What too many businesses fail to understand is that without the right foundations, AI results are often disappointing, if not damaging. Poor-quality data, fragmented systems, untrained staff and weak cyber defences can in fact undermine AI’s promise, exposing companies to operational failures and reputational harm.

To harness artificial intelligence’s full potential, supply chain leaders must first buttress the their existing operations. Businesses must not rush headfirst into the latest innovation but need to ensure they have the infrastructure in which AI can thrive instead. This is built on data integrity, skilled people, strong governance and ethical oversight. 

Speaking a common language

Every conversation about AI should begin with data. In modern supply chains, information flows between suppliers, logistics providers, manufacturers and retailers across the globe. These streams however rarely align neatly and data languishes in silos, trapped in incompatible formats and fragmented across legacy systems that were never designed to speak to one another. 

Here’s where AI so often stumbles, since machine learning algorithms, no matter how advanced, rely on consistent and accurate data lo learn patterns and make predictions. The first step towards AI readiness, then, is the creation of a “Rosetta stone” for supply chains by cleansing, normalising and standardising data to create a common language that bridges systems, regions and partners.

Once data becomes interoperable, organisation can begin to see their supply chain as an integrated ecosystem rather than a patchwork of disconnected entities, and AI can truly start to reveal insights that lead to real competitive advantage.

The right tools for the right jobs

The temptation to adopt generic, “universal” AI solutions is strong, but these systems often struggle to grasp the nuances of supply chain management. A truly effective supply-chain AI must understand the industry’s specific dynamic, such as seasonal fluctuation in demand, the volatility of raw material prices, regulatory differences between markets and the interdependencies of global sourcing networks. Off-the-shelf AI tools may recognise patterns, but without sector specific knowledge their insights risk being superficial or even misleading.

Supply chain native AI solutions are built with these complexities in mind and are designed not just to process data, but to interpret it through the lens of operational expertise, whether in procurement, inventory optimisation or supplier risk management. 

Protecting the digital core

The same interconnectedness that allows for real-time visibility across the entire supply chain can also increase the probability of cyber-attacks, and AI, with its “deadly trifecta” i.e. when systems have access to internal data, are exposed to unreliable inputs and can communicate externally, introduces additional risks that traditional cybersecurity models struggle to contain. 

The ransomware attack on Jaguar Land Rover’s operations, widely regarded as one of the costliest in UK history, demonstrated just how depended modern manufacturing is on digital continuity, while cyber-attacks on airport check-in systems across Europe have caused widespread delays and cancellations, illustrating how vulnerable logistics and transport chains remain.

In this environment, cybersecurity can no longer be treated as a box to tick to achieve compliance, but as a strategic imperative embedded in every stage of AI adoption. 

The human equation

For many employees, AI remains an intimidating prospect, a fast-evolving technology that threatens to automate them out of relevance. However, AI should not be seen as a tool to replace human employees, but to empower them by automating repetitive tasks such as data entry or routine scheduling, freeing them to focus on problem solving, creative thinking, relationship building and strategic planning.

To achieve this, organisations must invest not just in the right technology, but in continuous learning to ensure employees understand both the mechanics and the implication of AI, such as data privacy, algorithmic bias and ethical deployment. When people grasp where AI is effective and where its limits lie, they can use it with confidence and accountability, ensuring a “human-in-the-loop” approach, crucial to add context and safeguard against errors.

Keeping AI honest

Even as AI grows more capable, it remains only as ethical as the frameworks guiding its use and in supply chain, bias in data can lead to bias in outcomes, skewing forecasts, supplier ratings or even workforce decisions. Automated recommendations without appropriate human oversight and intervention may inadvertently prioritise cost over sustainability, or even efficiency over safety.

This is why organisations must prioritise robust governance and ethical oversight, defining clear boundaries for how AI is trained, deployed and evaluated and making sure transparency is built into every layer, ensuring all decisions can be traced, questioned and improved.

Governance has the power to transform AI from “just a tool” into a collaborative partner. Machines bring speed and pattern recognition while humans bring common sense, context and moral reasoning and feedback loops between the two ensure that AI systems learn responsibly over time, in line with both business objectives and societal values.

From hype to maturity

The journey towards an AI-ready supply chain is a process of intentional transformation that touches every aspect of an organisation, from data architecture to workforce culture, security measures, governance ethos and technological choices. 

Organisations who treat AI as a quick fix instead are likely to find themselves trapped in cycles of trial-and-error spending time and money on systems that never quite deliver. Those who approach it with patience and purpose, on the other hand, will discover that AI can become much more than an efficiency tool, acting as a strategic enabler that strengthens resilience, enhances foresight and fosters collaboration across the entire supply chain ecosystem.

AI will keep advancing faster than most organisations can predict, but the principles of responsible innovation will remain clarity of purpose, quality of data, respect for ethics and investment in people. In the long run, it is not algorithms that will define the future of supply chains but the humans who design, guide and refine them.

Author’s bio

Eric Lefebvre, Chief Engineering Officer at JAGGAER

As chief engineering officer (CEngO), Eric sets and oversees technology strategy for Jaggaer. With more than 25 years’ experience leading technology teams, he is a strong proponent for establishing a corporate vision and then providing his teams with the room to work, ensuring they have the freedom to tap into their full potential.

Eric has a wealth of experience leading teams throughout his career that focused on critical, high-volume transactions. Most recently, Eric was the CTO at Sovos, a global provider of e-Invoicing compliance and 1099 reporting services. He also served as CTO of Fiserv’s core payment acceptance business unit where he led a global organization supporting the world’s largest payment card processing volumes.

Eric’s leadership style was honed during the six years he spent in the Army National Guard as a combat engineer and squad leader. There he learned the importance of professionalism, accountability and that the mission always comes first, but people always matter.