Industrial companies are discovering that their biggest AI opportunity may not be replacing workers, but giving them back the hours consumed by repetitive work. New research commissioned by IFS and conducted by The Futurum Group finds industrial workers lose 41% of their time to manual, repetitive tasks, while enterprises increasingly turn to agentic AI and digital workers to address the resulting capacity gap.

The problem is bigger than wasted hours. In industrial environments, capacity shortages can delay procurement, maintenance, production and supply-chain decisions—creating downstream costs that are difficult to recover.

The new Futurum Group research, commissioned by IFS, suggests companies are increasingly looking toward agentic AI-powered digital workers: software agents capable of monitoring processes, making decisions within defined boundaries and executing operational tasks, while escalating exceptions to human employees.

The shift is happening even as enterprise confidence in fully autonomous AI remains low.

According to the research, 77% of decision-makers have delayed or avoided a strategic initiative because their teams lacked the capacity to pursue it. At the same time, only 5.7% said they trust AI to act completely autonomously.

That creates an interesting tension for industrial technology leaders. Companies recognize that conventional staffing cannot always absorb growing operational workloads, but many are not prepared to hand critical processes entirely to autonomous software.

The emerging answer is a human-in-the-loop model.

Under that approach, digital workers take responsibility for repetitive, rules-based transactions, while people remain responsible for approvals, exceptions and decisions requiring business judgment. The model resembles the way industrial automation has historically been deployed: automate predictable work while retaining human intervention where consequences are harder to anticipate.

The Futurum research indicates that this transition is gathering momentum. 66% of decision-makers surveyed said they are likely to invest in digital workers within the next 12 months, meaning roughly two businesses are preparing to invest for every one that is not.

Yet only 10% of enterprises currently operate mostly autonomous AI systems.

That gap between investment intent and current deployment could become one of the more important trends in enterprise AI over the next several years.

For industrial organizations, the pressure is not coming from technology alone. A generational workforce transition is underway across manufacturing, utilities and supply chains as experienced engineers and operators retire. Losing institutional knowledge at the same time that organizations face capacity constraints creates a particularly difficult operating environment.

Digital workers cannot simply replace that expertise. They can, however, take repetitive work away from experienced employees and preserve human attention for problems that require context, judgment and physical intervention.

IFS is positioning its IFS Loops Agentic Platform around that model. The company says the platform enables enterprises to create and deploy digital workers capable of automating 60% of agentic transactions end to end, with the remaining 40% incorporating human review and approval checkpoints.

That architecture is significant because industrial automation has different requirements from consumer-facing AI assistants.

A chatbot can generate an answer and still leave a user to decide what happens next. A digital worker operating inside procurement or order management can trigger a transaction with financial or operational consequences. Enterprises therefore need permissions, workflow rules, auditability and clear escalation mechanisms.

IFS says those controls are already being applied by customers.

Manufacturer Kitron Group, for example, is rolling out purchase-to-order digital workers across all 13 of its sites by the end of 2026. The digital workers operate within the company’s existing software environment, allowing established business rules, guardrails and approval processes to remain in place.

During the rollout, a digital worker also identified a part-number error that had persisted for roughly a decade—an example of how automation can potentially expose data-quality problems while performing operational work rather than requiring a separate data-cleanup initiative.

Other IFS customers are applying digital workers to procurement and order-to-pay workflows. IFS says Kitron Group and KLN Family Brands are reclaiming dozens of hours each week through these deployments, with some processes moving toward full automation without manual intervention.

The speed of deployment is another part of the story.

IFS says Ependion brought its Supply Order Manager digital worker live across the company through a 10-week progressive rollout, while Kitron is targeting deployment across its 13 sites in less than seven months.

That is a different proposition from lengthy enterprise AI programs built around model development or large-scale infrastructure changes. Industrial digital workers are increasingly being positioned as an operational layer that can sit on top of existing enterprise applications and workflows.

This puts IFS in competition—and in some cases potential overlap—with a broad ecosystem of enterprise automation and AI platforms. Microsoft, Salesforce, SAP, ServiceNow and other major software providers are adding AI agents to business applications, while cloud platforms such as Amazon Web Services, Microsoft Azure and Google Cloud provide the infrastructure underneath increasingly sophisticated agentic systems.

The differentiator for industrial software vendors is likely to be context.

A generic AI agent can reason across information, but an industrial digital worker needs to understand purchase orders, suppliers, inventory, maintenance schedules, field service processes, production constraints and enterprise approval policies. The closer those capabilities are integrated with operational systems, the less work organizations need to do to make automation useful and governable.

For enterprise teams, that means the question is shifting from whether to deploy AI agents to where autonomy is safe and economically valuable.

The most attractive starting points are likely to be high-volume workflows with clear rules, measurable outcomes and relatively predictable exceptions. Procurement, order management, invoice processing and supply-chain coordination fit that profile.

The longer-term opportunity is broader. As enterprises become more comfortable with supervised digital workers, autonomy could expand into increasingly complex operational workflows.

For now, however, the research points to a pragmatic phase of agentic AI adoption. Industrial organizations are not necessarily looking for machines that run everything themselves. They are looking for digital workers that can absorb repetitive workload without taking humans out of the decision loop.

That may prove to be a more realistic path toward closing the industrial capacity gap.

Market Landscape

The enterprise AI market is moving from generative AI copilots toward agentic automation, with industrial companies becoming an important proving ground.

The attraction is straightforward: industrial organizations often have large volumes of structured, repetitive transactions running through ERP, supply-chain, asset-management and field-service systems. These workflows can offer clearer boundaries for AI agents than open-ended knowledge work.

But autonomy introduces a new layer of enterprise risk. An AI system that can execute a transaction needs identity controls, permissions, monitoring, audit trails and escalation policies. That makes the competitive landscape broader than AI models alone.

IFS is competing for this opportunity from an industrial-software position, while companies including Microsoft, Salesforce, SAP and ServiceNow are embedding agents into horizontal enterprise applications.

The emerging battleground is therefore likely to be trusted operational autonomy: which platform can automate meaningful work while giving enterprises enough control to understand, supervise and reverse what AI agents do?

For CIOs, COOs and operations leaders, the practical strategy is likely to begin with bounded workflows and measurable capacity gains rather than attempting full enterprise autonomy immediately.

Top Insights

• IFS-backed research finds industrial workers lose 41% of their time to repetitive tasks, intensifying pressure on manufacturers, utilities and supply-chain teams.
• Sixty-six percent of surveyed decision-makers expect to invest in digital workers within 12 months, despite limited trust in fully autonomous AI.
• IFS Loops combines agentic automation with human checkpoints, targeting industrial workflows where predictable transactions can be automated without eliminating oversight.
• Kitron is deploying purchase-to-order digital workers across 13 sites, illustrating how agentic AI can operate within existing enterprise software and approval controls.
• The rise of digital workers puts IFS alongside Microsoft, SAP, Salesforce and ServiceNow in the emerging enterprise market for governed AI agents.

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