Webinar Recap: Why Agentic AI Alone Won’t Close the Talent Gap
Summary
In our Future.Now webinar with our co-host Accenture, “Why Agentic AI Alone Won’t Close the Talent Gap: How People and AI Can Scale the Supply Chain Together,” we explored a challenge that runs counter to much of today’s conversation about AI and jobs. The common worry is that technology will make supply chain workers less necessary. Accenture research points the other way. Skilled people are becoming scarce, with a projected deficit of 1.1 million roles in the U.S. alone over the next decade, and neither hiring nor technology deployed on its own can close that gap.
We looked at what does, in fact, close it: redesigning the work, the workforce, and the technology all together, an approach that Accenture has found delivers three times the return. That means mapping how each new capability changes tasks and skills, then preparing people before the technology arrives. We also followed a single role, the material planner, to show how agentic decision intelligence shifts daily work toward judgment, policy, and oversight.
Key Takeaways
- People are becoming the scarcest resource in the supply chain.
Much of the discussion around AI assumes the human role is shrinking. Accenture research found something closer to the opposite: the people who run supply chain and manufacturing operations are becoming harder to find. A deficit of 1.1 million roles is projected in the U.S. alone over the next ten years. Retirements are accelerating, the digital skills that new technology requires are in short supply, and fewer people are entering the trades. The result is a highly competitive market for talent, where hiring is no longer a reliable lever and technology cannot fill the gap by itself. - Redesigning the work alongside the technology triples the return.
Organizations face mounting pressure to adopt AI quickly and capture its value. According to Accenture, companies that redesign the work and the workforce at the same time as they deploy technology at pace and scale are seeing three times the return on investment. When only the technology changes, results tend to disappoint, because the tools get procured but the work around them never gets redesigned. Every implementation therefore carries a second question alongside the first: how the tasks, the skills, and the people in each affected role will need to change. - Tasks change first, then skills, then roles.
Procurement offers a clear example. Accenture’s research shows that 63% of purchasing and procurement tasks are primary targets for automation or augmentation, and 71% of procurement workers hold roles where technology directly affects more than half of what they do. One in three procurement skills is also in decline. For a purchasing manager, much of the core work can be augmented or automated, yet nearly a quarter of the redesigned role consists of new tasks. Only three of today’s top ten skills remain on the list, which is why skills need to evolve ahead of deployment for the investment to pay off. - Workforce strategy should move in step with the technology roadmap.
Mapping a technology strategy against tasks and skills shows how it affects the workforce over time, including when capacity will actually be freed and when new skills must come online. Agentic decision intelligence makes it concrete: as Aera automates decisions, it brings people in where their judgment matters most, which defines the skills each role will need. Often, repurposing experienced people into adjacent areas makes more sense than releasing them, a call supply chain leaders make best alongside HR. If a role looks the same five years into a transformation, something was missed. - Agentic decision intelligence moves planners from “in-the-loop” to “on-the-loop.”
Today, a material planner translates demand into inventory requirements, works with suppliers and logistics, and fields a constant stream of emails, calls, meetings, and escalations. With Aera, the work of weighing a projected shortage, whether to expedite, transfer, increase production, or pull orders forward, happens as the data refreshes. The planner adds judgment where uncertainty remains, then increasingly sets and monitors the policies that govern decisions as conditions shift. The path mirrors the self-driving car, which progressed from lane-keeping alerts to vehicles with no steering wheel, overseen by operators watching the whole network. - Aera understands, recommends, acts, and learns.
Aera detects signals as data refreshes, recommends the option most likely to meet business targets, and either writes the decision back to the ERP or routes it for human review. Each outcome then feeds a learning loop that refines policies over time. In one supply and demand balancing skill, 120 recommendations had already been handled on a planner’s behalf, while business rules held 12 for review. A glass-box view of forecasts, demand drivers, and alternatives replaces work that once spanned 27 dashboards. All told, Aera ran more than 50 million decisions in 2025 across more than 150 skills. - Trust and change enablement make the transformation hold.
Organizations often discover that far more decisions are being made than they ever realized, which makes defining the human role all the more important. Getting full value from agentic decision intelligence means looking beyond technology and process to the people. With Aera, attention shifts toward exceptions, policies, and broader learnings, and away from day-to-day firefighting. But that shift depends on trust. Simply spending money on a platform doesn’t mean the people using it will believe its recommendations, so they need to be brought along deliberately. Open conversations about how roles will evolve help to build confidence that the system works in their favor and delivers quality insights.
Speakers
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Kristine Renker, Managing Director, Accenture Kristine leads Accenture’s global supply chain and engineering talent practice, bringing 27 years of experience across industrial and automotive supply chains. She is one of the authors of the research behind this discussion, including Accenture’s Shortage to Strength study and a follow-up report with the World Economic Forum on human-machine collaboration. |
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Joe Derry, Chief Customer Officer, Aera Technology Joe leads customer success at Aera Technology, bringing extensive experience in decision intelligence, process transformation, and applied AI. Joe previously held leadership roles at Western Governors University and Dell, spanning analytics, software engineering, and digital transformation. Joe is known for helping organizations turn AI ambition into measurable business results. |
