Industrial companies have been slow to trust AI with mission-critical operations, and for good reason. IFS Loops is betting that governance, not sophistication, is the unlock. With millions of dollars in proven ROI and a new no-code studio for deploying Digital Workers, it may have a point.

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Ask any operations manager at a large industrial company whether they trust AI to run their dispatch queue, manage their parts inventory, or advise their field engineers in real time, and you will get a version of the same answer: not yet, not without oversight, and not without understanding exactly what it is doing and why.

That hesitancy is not irrational. In environments where a wrong decision can ground a fleet, halt a production line, or leave a field technician stranded without the right part, the cost of AI failure is not an embarrassing hallucination, it is a measurable operational catastrophe.

It is precisely this reluctance that IFS Loops is trying to dismantle. The industrial AI software company has announced the launch of IFS Loops Agent Studio, a no-code platform that allows non-technical employees to configure, govern, refine, and expand AI-powered Digital Workers for their specific business contexts. The announcement arrives alongside new pre-built Digital Workers designed specifically for field service, a sector where the consequences of poor automation are felt immediately and in the field.

The Governance Problem That Has Been Holding Industrial AI Back

The distinction IFS Loops is drawing — between deploying AI and governing it — is not cosmetic. Most enterprise AI projects fail not because the underlying technology is inadequate but because organisations cannot maintain visibility and control once agents are in production. Who is the AI escalating to? What rules is it applying? Why did it make that decision rather than a different one? When those questions cannot be answered confidently, adoption stalls.

IFS Loops has built its Digital Worker architecture around this insight. The platform arrives pre-configured with what the company describes as enterprise-grade AI Trust controls: security permissions, governance guardrails, and auditability baked in from the outset rather than added as an afterthought. The Agent Studio allows businesses to set context, define processes, design actions, and — critically — test behaviour safely before any Digital Worker touches a live production environment.

Photo: Somya Kapoor, CEO, IFS Loops

"Building agents is easy — governing how they operate is the hard part," said Somya Kapoor, CEO of IFS Loops. "Digital Workers are not something you deploy once and forget. Like any workforce, they improve over time. Organizations start by building or modifying a Digital Worker, testing it in real workflows, refining the rules, guardrails, and decisions it makes. Then they monitor how it performs in production. That continuous cycle of change, test, and monitor is how Digital Workers become more capable and more trusted over time. It's how enterprises move from experimenting with AI to operating with it every day and seeing real ROI within weeks."

The framing of a Digital Worker as something that evolves under human supervision, rather than something deployed once and left to operate autonomously, is a deliberately conservative pitch. It is also, given the appetite for control inside most industrial organisations, probably the right one.

The Numbers That Are Making Sceptics Pay Attention

Abstract arguments about AI governance only carry so much weight. What has given IFS Loops credibility in conversations with industrial enterprises is a growing body of concrete, verifiable results from customers who have already committed.

Ependion, a global manufacturing company, was processing more than 150 purchase order confirmations per week entirely by hand — a slow, error-prone operation that consumed significant employee time. After deploying the Supplier Order Manager Digital Worker, the company is expecting to achieve a 60 per cent gain in operational efficiency and recover 20 hours per week. The results were persuasive enough that Ependion moved immediately to a second Digital Worker.

"We spent years solving this problem in traditional ways," said Joakim Stolt, Chief Information Officer at Ependion. "One Digital Worker did what we couldn't. We saw the value, added a second, and we're not stopping there. Having a human in the loop at every critical decision point is how enterprise AI should work, and IFS Loops built that in from the start."

The case from Kodiak Gas Services is even more striking. The company operates 4.5 million horsepower of compression across the United States, with 800 field technicians who were previously spending significant time simply searching for parts. IFS Loops deployed a Material Replenisher Digital Worker that allows technicians to locate and order parts through natural conversation.

The reported outcome: three million dollars in annual ROI and 90,000 hours returned to the workforce. These are not pilot-programme projections — they are the kind of figures that make procurement committees rethink their timelines.

At Kitron, the electronics manufacturing services provider, Digital Workers are handling supply chain workflows including inventory replenishment and supplier coordination. Jonatan Gustafsson, Business

Application Manager at Kitron, described the integration as a natural extension of existing infrastructure. "IFS is the backbone of our global operations, connecting procurement, production, logistics, and finance in one platform," he said. "IFS Loops Digital Workers are a natural extension of that foundation. With structured operational data already in place, we can apply AI where it matters most. Automated supplier order confirmations save significant time, and early shortage prediction means we can protect production schedules before problems occur."

Field Service: The Next Frontier for Agentic AI

Beyond the Agent Studio announcement, IFS Loops is expanding its Digital Worker portfolio into field service — a sector that has historically been resistant to automation because of the sheer unpredictability of work in the field. Three new Digital Workers address distinct pain points across planning, dispatch, and knowledge management.

The Service Planning Assistant continuously evaluates service demand, technician availability, and operational constraints to support predictive scheduling — helping organisations anticipate demand rather than merely react to it. The Dispatcher Assistant monitors service queues, identifies scheduling conflicts, and recommends optimal dispatch decisions, escalating only the exceptions that genuinely require human judgement. The Knowledge Manager, perhaps the most immediately practical of the three, provides field technicians with contextual guidance drawn from asset data, service history, and enterprise documentation in real time — at the precise moment it is needed during a job, rather than requiring technicians to search across disparate systems.

The common thread across all three is exception-based supervision: the system handles the routine, escalates the complex, and keeps humans informed throughout. It is a model of AI deployment that prioritises operational continuity over full automation — and one that is likely to prove more durable in regulated, asset-intensive industries than more aggressive approaches.

What the Analysts Are Saying

Industry observers have noted a broader shift in how enterprise AI platforms are being evaluated — a shift that IFS Loops' approach is specifically designed to capitalise on. Keith Kirkpatrick, Vice President and Research Director for Enterprise Software and Digital Workflows at Futurum Group, was direct in his assessment.

"Agentic AI platforms are increasingly being evaluated not on model sophistication, but on their ability to operate safely within complex, high-volume enterprise workflows," Kirkpatrick said. "By defining digital workers as governed operational entities, complete with role boundaries, exception handling, and performance metrics, risk is reduced while adoption accelerates. From a technical perspective, the emphasis on lifecycle management, auditability, and incremental capability expansion reflects a maturing view of AI as a long-term operational asset rather than a standalone technology experiment."

That phrase, a long-term operational asset rather than a standalone technology experiment, captures the bet IFS Loops is making. The enterprise world is full of AI experiments that never graduated to operations. The companies that are winning are those helping organisations close that gap.

With verifiable results, a governance-first architecture, and now a no-code studio that puts Digital Worker customisation in the hands of the people who understand the business best, IFS Loops is making a credible case that it understands what industrial AI actually needs to look like, not just what it could theoretically become.