As part of my coverage of the year in supply chain 2025 and a look ahead at 2026, I summarized some of the predictions for logistics in 2026 and beyond from Gartner, which you can find here: Supply Chain Guru Predictions for 2026.
I promised then that I would be back soon with highlights of another set of what Gartner terms "predicts"- which I am just getting to this week, with Gartner's predicts for Realizing the Supply Chain AI Opportunity.
That sounds interesting enough - let's take a look.
I'll start with this: Gartner analyst Mel Mohmednur predicts that by 2030, supply chains automated by AI will have replaced 50% of domain-expert roles with generalists who combine AI skills and business acumen.
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Gilmore Says.... |
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Interesting - most of us will be AI-empowered supply chain generalists before long.
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What do you say? |
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Mohmednur starts with this observation: Today, supply chain roles are designed for specialization to achieve economies of scale and efficiency -for example, domain-expert roles in sales and operations planning (S&OP) and demand planning in planning, or supplier and category management in procurement.
Gartner data, in fact, shows that 50% of roles today are highly specialized, and an additional 27% are moderately so.
Mohmednur adds that AI can progressively handle tasks that once required deep supply chain expertise, freeing employees to take on more responsibilities with greater breadth at scale quickly. As AI continues to rapidly reshape how supply chain work gets done, the business need for new and different skills is evolving just as quickly.
Focusing on just one part of the supply chain makes it hard for employees to pick up skills in other areas without spending a lot of extra time. This limited experience also makes it tough for them to understand the bigger business picture.
As a result, Mohmednur says, in the future supply chain job descriptions will likely be more flexible, since AI will take over many routine tasks. This will let employees take on broader and more impactful work. Companies may start using more versatile job titles, like "supply chain generalist" or "supply chain practitioner."
Supply chain organizations will increasingly rely on talent profiles such as generalist or versatilists who can adapt to various roles, learn new skills quickly, and navigate the changing landscape with flexibility and speed.
Mohmednur adds that "AI literacy will become a basic expectation for supply chain because understanding and leveraging these technologies will be essential for success in increasingly dynamic and versatile roles. As expectations for AI skills keep rising, employees will need to learn quickly and adapt to new technology as it evolves."
What to do? Among Mohmednur's recommendations is this: Develop talent to build a broad skill set that enhances AI effectiveness.
In addition to just developing the technical skills of using AI, encourage cross-functional rotations and experiential learning to develop broad skill sets.
And this: Redesign incentives for development. Update reward and recognition systems to incentivize employees who develop or demonstrate targeted skills that a generalist or a versatilist role would require. Make these must-have competencies for all applicable supply chain roles.
Interesting - most of us will be AI-empowered supply chain generalists before long.
 Next up: Gartner analyst Pierfrancesco Manenti, who predicts that by 2030, 40% of supply chains will use AI to shift from reactive cost cutting to proactive, data-driven cost management.
Am I the only one who doesn't know what "proactive, data-driven cost management" is? Let's find out together.
Manenti starts by noting that "CSCOs are trapped in a cycle of aggressive cost-reduction mandates. The resulting reactive, short-term focus on cost cutting is proving ineffective, as evidenced by 75% of supply chains exceeding their cost targets by more than 5% over the past six months."
He adds that while most organizations already capture extensive data across their end-to-end supply chain, siloed data pools along the partner ecosystem, functionally siloed organizations, and static analytical methods fail to capture the complex interdependencies required to utilize data effectively. As a result, organizations struggle to identify cost management opportunities.
Menenti says that "AI capabilities are evolving to help experts make complex decisions by quickly analyzing large amounts of data in real time. By managing complex interdependencies that humans cannot easily process alone, AI can process end-to-end supply chain data in real time, revealing cost-to-serve opportunities and isolating margin leakage that remains invisible in traditional methods."
I have no doubt he is correct.
In fact, innovators are already using AI for cost management, Manenti says, citing Johnson & Johnson as deploying a GenAI tool that allows planners to "converse" with data. This enables the company to proactively ask questions to identify specific SKUs where safety stock policies could be adjusted, minimizing excess inventory and reducing costs without hurting service levels. This approach reduced analysis time from hours to minutes and revealed that small, targeted changes to specific SKUs could capture over 50% of the total inventory reduction opportunity.
Among Manenti's recommendations is this: Build and scale a robust data platform to serve as the foundation for AI to analyze and simulate future scenarios and cost management opportunities - for example, by creating a comprehensive digital twin of the entire supply chain
In addition: leverage AI to simulate future scenarios and understand their implications on supply chain cost structures in order to achieve long-term structural cost management. Use this capability to identify the implications of cost-cutting opportunities and analyze trade-offs to explicitly quantify what is gained or lost -such as service reliability or agility.
So there you have it, two of Gartner's five predicts on AI and the supply chain. Good stuff as usual from Gartner
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