In 2025, leading research and advisory firm Gartner published several studies examining the application of AI in supply chain management. The findings highlight a significant shift: 74% of supply chain professionals believe AI capabilities will be one of the most important drivers of success over the next three to five years. While 94% of professionals in organisations that have already implemented AI are open to using the technology, only 36% have successfully integrated AI into their day-to-day workflows.
Beyond the data, this trend is also becoming increasingly evident in the hiring market.
As one senior Robert Half consultant observes:
"During interviews, employers are increasingly interested in whether candidates have used AI to support business decisions — whether that's analysing data, refining forecasting models, or contributing to critical decision-making. Candidates with such hands-on experience remain relatively scarce in the market."
For many organisations, mid-to-senior supply chain leaders are approaching an important turning point. The change is not necessarily in the role itself, but in the capabilities required to succeed.
Based on insights gathered from recruitment and talent assessment projects, today's supply chain leaders broadly fall into three emerging categories.
Execution-focused managers
These leaders have built strong careers around operational excellence. They are highly experienced in areas such as ERP systems, inventory management and supplier coordination, but may have had limited exposure to advanced analytics or AI-enabled tools.
In a stable operating period, this skill set has traditionally delivered strong results. However, as data-driven decision-making becomes a core business capability, their career paths are beginning to diverge.
Data-aware managers with limited strategic influence
This group understands both operational processes and data. They are comfortable using analytical tools and can provide valuable insights to support the business.
However, they still tend to operate primarily in analytical or execution roles, with less involvement in key business decisions. They are often more cautious when driving cross-functional initiatives, which may limit their broader organisational impact despite their strong technical expertise.
Decision-oriented leaders who continue to evolve
This group is attracting growing attention from employers.
They combine deep business understanding with data analysis and AI-enabled insights, while remaining willing to take ownership of decisions and outcomes.
In the consumer goods sector, for example, these leaders may use AI tools to analyse historical sales data, promotional activity, regional consumption patterns, co-branding opportunities, and social media trends to forecast future demand. They can then adjust inventory, replenishment and stock allocation strategies accordingly.
Rather than viewing AI as simply another tool, they use it as a decision-enhancement capability that supports stronger business outcomes.
The third category of leaders who can combine commercial insight, analytical thinking, and AI fluency is attracting growing interest from employers. While demand continues to rise, this talent pool remains relatively limited.
At the same time, AI adoption is steadily reshaping expectations across the profession.
As AI becomes increasingly embedded in analysis and decision-making, many managers are looking at how to further strengthen their expertise, judgement and strategic value in an AI-enabled business environment.
Will AI replace what I've spent years learning?
In today’s supply chain environment, many decisions have relied on experience, from setting inventory and planning replenishment to scheduling production. As AI becomes increasingly integrated into these processes, organisations can analyse larger volumes of data more efficiently, run multiple scenarios at scale and generate more accurate forecasts.
As a result, many managers are beginning to reflect on how their roles are evolving. If AI can provide faster insights and recommendations, where does human expertise add the greatest value?
What we are seeing is not the replacement of managerial judgement, but a shift in where that judgement matters most. AI is becoming highly effective at handling routine analysis and standardised decision support, while the most strategic and complex decisions remain firmly dependent on human insight.
In the past, organisations often prioritised process management, team leadership and cost control when evaluating supply chain leaders.
Today, many employers are placing greater emphasis on questions such as:
Have you used data to drive business decisions?Have you worked with AI tools or forecasting models?Have you contributed directly to high-impact commercial decisions?
As a result, the role of the manager is evolving.
The expectation is no longer simply to make decisions independently, but to interpret recommendations generated by increasingly sophisticated systems and determine the most appropriate course of action.
Leaders still need to assess whether a model is suitable for current market conditions, identify exceptions, balance risk against opportunity and make the final call when circumstances change.
These remain fundamentally human responsibilities.
What sets high-performing leaders apart?
Process management, team coordination and cost control remain essential skills. However, they are increasingly viewed as table stakes rather than differentiators.
Recruitment trends suggest that competitive advantage is shifting from accumulated experience alone towards more effective decision-making capabilities.
1.From process management to decision-making
Operational excellence remains important, but organisations increasingly value the ability to make evidence-based decisions.
Inventory management provides a good example. Historically, safety stock levels were largely determined through experience. Today, many organisations are using AI-enabled forecasting models to optimise inventory.
The manager's role is not to replace the algorithm but to understand its logic, recognise its limitations and make informed decisions when it matters most.
2.From data reporting to data-led action
Many managers can build reports, analyse trends and navigate BI tools.
What employers increasingly want to see, however, is the ability to convert insights into business action.
AI can predict demand changes, but it cannot decide whether production should increase, whether inventory risk should be reduced or whether channel strategies should change. Those decisions still require human judgment.
3.From functional expertise to system thinking
Supply chains are complex ecosystems.
While AI can optimise individual processes, such as forecasting accuracy or warehouse efficiency, leaders need to balance cost, speed, resilience and risk across the wider organisation.
The ability to align stakeholders, coordinate resources and optimise overall business performance is becoming increasingly valuable.
The supply chain talent employers want most
One theme now appears repeatedly in hiring conversations: human-AI collaboration.
This does not mean organisations expect supply chain leaders to become AI engineers.
Rather, they want professionals who understand how AI can be applied effectively to support business decisions.
For example, in consumer goods, AI may help forecast future demand by analysing social media trends, product launches or consumer engagement around brand collaborations.
But AI does not determine brand strategy, product portfolio decisions or inventory commitments.
For example, in consumer goods, AI can analyse factors such as the popularity of brand collaborations, social media trends and competitors’ product launch activity to forecast demand shifts for specific product categories in the coming quarter. However, the final decision still rests with leaders.
Is the opportunity aligned with the brand? Should the product mix be adjusted? How much inventory should be committed?
AI is a powerful tool, but managers remain at the centre of decision-making.
Effective human-AI collaboration comes down to three essential capabilities:
Understanding what AI can and cannot doApplying business judgement to make informed decisionsTaking accountability for outcomes
The leaders who can do all three are increasingly sought after.
How forward-thinking managers stay ahead
Based on hiring trends and employer feedback, successful supply chain leaders are typically investing their development efforts in four areas.
Step into decision-making opportunities
Avoid remaining exclusively in execution or support roles whenever possible.
Look for opportunities to improve processes through data, present recommendations to stakeholders, contribute to inventory strategy discussions or participate in cross-functional planning.
Decision-making experience is often built proactively rather than waiting for formal authority.
Learn to use AI as a business partner
AI can support demand forecasting, risk modelling and data analysis.
The real value lies not only in using the technology but also in asking the right questions.
For example:
How should inventory strategies change if sales fluctuate by 10%?What adjustments are needed if suppliers experience delays?How should risk be balanced against service levels?
The quality of the questions often determines the value of the answers.
Expand your commercial perspective
The strongest supply chain leaders rarely focus solely on supply chain operations.
They understand sales objectives, financial considerations and market dynamics because effective decisions require a broader view of the business.
Strengthen influence and communication skills
Decisions only create value when they can be successfully implemented.
The ability to communicate across functions, translate data into business language and build alignment around strategic priorities is becoming increasingly important.
AI can generate recommendations. It cannot secure buy-in, build trust or drive organisational change.
The role of the supply chain manager is not becoming less important in the AI era — it is being redefined.
AI is rapidly becoming a powerful decision-support tool, but organisations are not simply looking for people who can use new technology. They are looking for leaders who can understand AI and use it to improve efficiency. The real differentiator, however, lies in leading teams, driving collaboration, balancing risk and creating business value.
Perhaps most importantly, managers still need to inspire teams, understand people and bring stakeholders together. In an increasingly digital world, these human capabilities are becoming even more valuable.
The leaders who will thrive in the years ahead will not necessarily be those who know the most about AI. They will be the ones who know how to use it to amplify their impact.
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