IFS Loops Unveils Agentic AI-Driven Digital Workers for Industrial Operations

IFS unleased digital workers AI agents

Key Takeaways

IFS Loops has introduced digital workers designed to fill critical staffing gaps in industrial organizations, featuring 10 roles and 50 agentic skills, with many more expected.

Real-world use of IFS digital workers has demonstrated significant ROI, as Kodiak Gas saved 90,000 hours within two months of deployment, showing the potential of AI to enhance efficiency.

Effective deployment of AI does not require deep AI expertise; a clear understanding of business needs and focused outcome goals can lead to successful implementations.

With aging workforces that aren’t being replaced by the next generation, many industrial organizations are struggling to keep up the staffing levels they need to operate. To that end, IFS Loops announced the availability of digital workers for industrial operations in all supported versions of IFS Cloud at the IFS.ai Industrial X Unleased event in New York City on Thursday.

The out-of-the-box new digital workers cover 10 different roles, including supplier and customer order managers, operations and asset intelligence analysts, field technicians, inventory and material replenishers, dispatcher assistants, quality analytics, and reasoning analysts. The workers come with 50 possible agentic skills, with more than 100 expected by the end of the year. There are also more than 70 connectors to other applications and data sources to enhance digital workers.

For transparency, IFS digital workers detail every step along the way in reaching their decisions and actions, including reference materials such as knowledge articles. The agents can automatically fill out work orders based on natural language prompts and even recommend changes to knowledge articles based on the outcome of the maintenance task. The goal is that the digital workers can dig deeper into available resources faster than a human and ultimately contribute to First Time Fix Predictions (FTFP) in the IFS Cloud system.

“Something that would have taken hours or day runs in less than a minute while and its not depending on a technician being available,” said Vaibs Kumar, Senior VP of Technology at IFS, while speaking at the event.

Real-Life ROI

It’s one thing to talk about the possible, but Kodiak Gas CIO Pedro Buhigas joined the stage to share how his company has realized immediate benefits deploying IFS Loops digital workers just eight weeks ago on the heels of going live with IFS Cloud in August.

“We brainstormed the art of the possible and came up with 25 use cases,” said Buhiga. “We focused on material replenishers, or part finders.”

Before digital workers, the typical hunt for a part would be searching through the catalog of parts in the system or just calling up a coworker to see if they have it available. The part finder can cut at least 15 minutes of work between the two individuals in that scenario, according to Buhiga. He added that if the entire workforce engages with the coworker just one time a day, that’s worth $3 million in ROI. To date, Kodiak Gas has saved 90,000 hours in less than two months.

With “applied industrial AI” being the major theme across the event, Kodiak Gas provides a proof point that intentionality and specificity deliver results when deploying AI to solve problems and automate complex processes.

What This Means for ERP Insiders

AI expertise is not always necessary to reap AI benefits. While IFS did demonstrate a soon-to-be available tool for customizing digital workers, right now they provide out-of-the-box skillsets aimed at some of the most common industrial processes and roles. No need for AI expertise, only an awareness of the business to deploy AI agents in the best way is required.

Transparency is important for AI Agents. IFS Loops digital workers leave a trail showing what they did to arrive at their outcomes. Security leaders in many organizations will welcome that.
Focus on outcomes, not capabilities, when deploying AI. Kodiak Gas found success with digital workers because the focus was narrow, and the desired outcome was identified before the AI Agent was deployed.