New IFS research finds 77% of organizations have delayed strategic initiatives due to capacity constraints, while investment in agentic digital workers accelerates.
Industrial organizations are increasingly turning to agentic AI-powered digital workers to address growing workforce capacity shortages, according to new research commissioned by IFS and conducted by The Futurum Group. The study reveals that industrial employees spend 41% of their time on repetitive manual tasks, limiting their ability to focus on higher-value activities and creating a widening gap between operational demands and available workforce capacity.
The findings highlight a growing challenge across manufacturing, utilities, transportation, and supply chain sectors, where experienced workers are retiring and organizations are struggling to maintain productivity while pursuing digital transformation initiatives. As a result, 77% of decision-makers surveyed said they have delayed or abandoned strategic projects because their teams lacked the capacity to execute them.
To bridge this gap, enterprises are increasingly investing in digital workers, AI-driven systems capable of autonomously monitoring, analyzing, and executing operational tasks while escalating exceptions to human employees when judgment or approvals are required. The report found that for every organization not planning to invest in digital workers over the next year, two are actively preparing to do so.
“The capacity gap is a high-stakes problem in industrial operations. Digital workers built for the job, integrated into the systems already running the business, are what let teams reclaim capacity instead of simply working around the shortfall,” said Somya Kapoor, CEO, IFS Loops.
While adoption remains at an early stage, momentum is building rapidly. Only 10% of enterprises currently operate mostly autonomous AI environments, yet 66% of organizations say they are likely to invest in digital workers within the next 12 months. The primary challenge is no longer technology readiness but trust, with only 5.7% of respondents fully trusting AI to operate autonomously without oversight.
The research suggests organizations are favoring a human-in-the-loop model, where digital workers handle high-volume, rules-based tasks while employees retain responsibility for critical business decisions. This approach is reflected in the IFS Loops Agentic Platform, which automates approximately 60% of agentic transactions end-to-end while keeping human review and approval processes in place for the remaining workflows.
Several IFS customers have already moved beyond pilot programs into production deployments. Companies including Kitron Group and KLN Family Brands are using digital workers to automate procurement and order-to-pay processes, significantly reducing manual workloads. During a recent rollout, Kitron’s digital worker identified a decade-old part-number error that had gone undetected through traditional manual processes, demonstrating how AI can improve operational accuracy while increasing efficiency.
The study indicates that sectors facing significant operational complexity are moving particularly quickly. Transportation and logistics organizations are among the fastest adopters, while energy and utilities companies report some of the highest levels of capacity-related project delays.
Organizations surveyed said the time freed by digital workers will primarily be redirected toward cost reduction, growth initiatives, faster execution, and strategic projects that teams previously lacked the capacity to pursue.
As industrial enterprises continue to balance labor shortages, rising operational demands, and digital transformation goals, agentic AI is increasingly being viewed not as a replacement for human employees, but as a practical mechanism for augmenting workforce capacity. The findings suggest that digital workers may become a critical component of industrial operations over the next few years, helping organizations improve productivity while enabling employees to focus on more strategic and value-driven work.
