For years, digital transformation in supply chain was largely about visibility, connectivity and efficiency. Better systems helped organizations see more, plan better and respond faster.
AI is beginning to change that conversation.
We are moving from technology that primarily helps people understand what is happening to technology that can increasingly recommend what should happen next – and, in some cases, act on it. Across planning, procurement, sourcing, supplier management and risk, AI is beginning to influence not just how quickly work gets done, but what work needs to be done by people in the first place.
That distinction matters. If AI changes the work, organizations cannot simply introduce new technology and leave everything around it unchanged.
When Technology Starts Changing Decisions
Much of the early value of digital supply-chain investment came from improving information: better dashboards, better forecasts, greater visibility and more connected processes. AI takes this further. It can identify patterns across enormous datasets, evaluate scenarios, flag emerging risks and increasingly support decisions that once required significant human analysis.
Procurement is a good example. As AI takes on more transactional and analytical work, professionals potentially have more room to focus on supplier strategy, resilience, innovation and broader business outcomes. McKinsey describes agentic AI as accelerating this shift, moving procurement further from transactional activity toward a more strategic role in the enterprise.
But this raises a more interesting question: If technology changes how decisions are made, shouldn’t we also rethink the organization around those decisions?
The challenge is already becoming visible. Recent Gartner research found that 56% of supply-chain leaders cite integration with legacy systems and processes as a major obstacle to scaling AI, while 50% point to limited internal expertise or talent.
Putting AI on top of an existing workflow may make it faster. But making an old process faster is not necessarily the same as designing a better way of working.
The latter asks harder questions. Which decisions should machines make? Where should people remain accountable? Which roles need to change? And where does human judgment become more valuable precisely because technology is doing more?
This is why AI transformation in supply chain is increasingly an organizational question as much as a technology question. ISM’s reporting on 2026 procurement priorities reflects the same shift: AI-enabled technology has moved near the top of the agenda, alongside a broader rethinking of operating models as procurement organizations face increasing workloads and complexity without corresponding increases in resources.
A Different Kind of Supply-Chain Professional
We can already see this shift in the talent market. Gartner’s analysis of nearly 600,000 supply-chain job postings found that demand for roles requiring AI skills increased 387% between the first quarter of 2023 and the first quarter of 2026. Much of that demand is concentrated at the mid-to-senior level.
The number is striking, but what it represents is more interesting.
Organizations increasingly need people who can combine domain knowledge with technology fluency – professionals who understand the realities of supply chains but can also determine where AI should be applied, questioned or complemented by human judgment.
Some of the capabilities becoming more valuable are not new at all: commercial thinking, supplier relationships, risk awareness, scenario planning, cross-functional decision-making and judgment. What is changing is their relative importance.
As technology becomes better at processing information and identifying patterns, people may spend less time producing the answer and more time deciding what the answer means and what should happen next.
What Happens to the Next Generation?
This leads to one of the most interesting contradictions in the AI conversation.
If AI removes some of the analytical and transactional work traditionally performed by people early in their careers, organizations may understandably conclude that they need fewer entry-level roles. Many already expect that to happen.
Gartner found that 55% of supply-chain leaders expect agentic AI to reduce entry-level hiring needs. Yet in the same research, 86% said AI would require new approaches to developing future talent pipelines.
AI is changing not only what work gets done – but how tomorrow’s supply chain talent will need to develop.
Source: Gartner, 2026 supply chain talent research cited in the article.
Those two findings deserve to be considered together.
Many experienced supply-chain professionals developed their judgment by doing exactly the kind of work organizations are now considering automating. They analyzed information, managed transactions, dealt with suppliers, solved operational problems, made mistakes and learned from experienced colleagues. Over time, experience became judgment.
If some of those developmental roles disappear, where will the experienced supply-chain leaders of ten years from now come from?
This is not an argument against automation. The productivity opportunity is real. But perhaps the challenge is not preserving yesterday’s entry-level jobs. It is redesigning them for tomorrow.
Early-career professionals may need different experiences – working alongside AI sooner, solving more complex problems earlier, developing stronger commercial understanding and learning how to challenge technology rather than simply operate systems.
Organizations therefore have to think not only about which work AI can remove, but also which experiences people still need in order to develop. Gartner has warned that organizations that pause entry-level supply-chain hiring in response to AI could ultimately face talent shortages and higher premiums for early-career professionals.
That is a very different conversation from simply asking how much work can be automated.
Beyond the Technology
Supply-chain leaders are already navigating cost pressure, geopolitical uncertainty, supplier risk, resilience and increasingly complex operating environments. AI offers extraordinary possibilities for dealing with some of that complexity.
But perhaps we sometimes begin the conversation too narrowly.
We ask: Where can we use AI?
Perhaps the better questions are becoming: What should people stop doing because technology can do it better? Where does human judgment become more valuable because AI is present? Which roles should be redesigned rather than eliminated? And how will the next generation develop the experience required to lead increasingly intelligent supply chains?
These are not purely technology questions. They are not simply workforce questions either. They are business-design questions.
The organizations that gain the greatest advantage from AI may therefore not be those that deploy the most technology or automate the most work. They may be the ones that are most thoughtful about what they redesign around it – the work, the decisions, the roles, the skills and the way people develop.
Because the real transformation is not simply from human to machine. It is from one way of working to another.
Are we redesigning our organizations as thoughtfully as we are redesigning our technology?
Sources & Further Reading
McKinsey & Company. Redefining procurement performance in the era of agentic AI. February 5, 2026.
Gartner. Gartner Survey Finds Technology Integration and Talent Perceived as Key Roadblocks to Scaling AI in Supply Chain. April 29, 2026.
Gartner. Gartner Says There is an Outsized Need for AI Talent in Supply Chain. June 15, 2026.
Gartner. Gartner Survey Shows 55% of Supply Chain Leaders Expect Agentic AI to Reduce Entry-Level Hiring Needs. February 25, 2026.
Gartner. Gartner Predicts Supply Chain Organizations Pausing Entry-Level Hiring for AI Will Face Higher Costs by 2030. May 5, 2026.
Institute for Supply Management. AI, Risk and Productivity Pressures Are Reshaping Procurement Priorities. February 3, 2026.