The top three agentic AI use cases for manufacturers so far are materials planning, customer orders, and inventory as organizations struggle with the capacity to invest in agentic AI, according to market research commissioned by industrial AI software provider IFS.  

The August 2026 report from technology research, advisory, and media firm Futurum also found that industrial workers lose 41% of their time to repetitive tasks, creating a capacity gap that companies are trying, with mixed results, to overcome.  

See also: IFS, Morgan Motor extend partnership in industrial AI 

Futurum's research aligned with IFS's agentic AI "digital worker" platform, IFS Loops, and draws on the experiences with the software of six IFS customers—KLN Family Brands, CDF Corp., Ependion, AirBoss, Sims Crane, and Kitron Group—that are already using the agentic digital assistants in their operations. 

Industries represented in the research include manufacturing, energy and utilities, transportation and logistics, construction and engineering, aerospace and defense and telecommunications, and the companies’ experience with agentic AI forms a library of sorts of use cases whereby other adopters can draw lessons already learned. 

Separating ambition from execution 

The research found that "digital worker" adoption appears to be accelerating, specifically that 66% of enterprises are likely to invest in digital workers over the next 12 months. 

However, the hunger for autonomous AI is ahead of organizations’ ability to execute using the technology, creating an adoption maturity gap that the report notes "separates ambition from execution."  

Despite the plans to invest, only 10% of enterprises run mostly autonomous AI operations today, according to the research.

And although businesses want to solve the worker capacity gap through agentic AI and digital workers, they often lack the capacity themselves to solve this issue. More than 75% of company leaders have delayed implementing a strategic initiative because their team lacked the capacity to pursue it, and less than 6% of leaders trust AI to act autonomously, according to the research. 

See also: Survey: AI adoption continues apace, despite uncertainties and failure risk

"Energy and utilities firms feel the capacity squeeze most acutely: 52% frequently delay initiatives due to limited capacity, the highest rate surveyed," the Futurum report reads. 

Use cases to be gleaned from the report 

According to IFS and Futurum, the maturity gap indicates a dependable bridge is missing between AI that enterprises can trust and the autonomous outcomes they want. 

Although the operational priorities differ by industry, each reflects a specific capacity constraint that AI can fill, such as repetitive tasks that take up workers' time. 

For manufacturers, the top use case was materials planning and coordination, in which AI could optimize materials, inventory, and production schedules. The next was customer orders, followed closely by inventory and replenishment.  

See also: How data, governance and organizational change define AI success  

Futurum identified some current and projected success rates that the IFS customers experienced while using digital workers: 

• CDF has a live inventory replenishment agent and a customer order manager agent pending. The measured result of these implementations was 20% of purchasing staff time was freed up for other tasks. 
• AirBoss has a customer order manager digital worker in use. The report identified that 40% of orders projected without human interaction, once the digital worker reaches full production.  

Outside of manufacturing, use cases vary by industry. For energy and utilities, for example, the top use case was knowledge and documentation—to capture and organize critical knowledge for the workforce—followed by asset performance and work order planning, according to the research.  

"The capacity gap is a high-stakes problem in industrial operations. When a purchase order, a maintenance schedule, or a supplier delivery falls behind because there aren't enough hours in the day for the worker, the cost shows up as downtime, missed deliveries, or idle equipment. Closing it takes more than general-purpose AI," said Somya Kapoor, CEO of IFS Loops. 

"Digital workers built for the job, integrated into the systems already running the business, are what let teams reclaim capacity instead of just working around the shortfall. Our customers prove that model in production, not pilots.” 

See also: IFS debuts package of ‘digital workers’ in next iteration of agentic AI industrial software 

IFS is highlighting this research less than a year after it released IFS Loops, its AI-based “workforce” programmed with over 50 agentic skills that assist industrial organizations, though the company aims to debut more.  

The agentic AI product is aimed squarely at manufacturing, service industries, energy and utilities, telecoms, aerospace and defense, and the construction industry, aiming to free the labor force of more mundane tasks.