How Agentic AI Helped IFS Quintuple Revenue in 10 Years

There are three key pillars I rely on when assessing which companies will benefit from the profit pool shifts that will result from AI: data, users and workflow. The first two are obvious as AI thrives on both. But people underestimate the value of owning the workflow. If a company owns the existing workflow and can embed intelligence, you can quickly bring AI benefits to customers. This AI can then be monetized either through usage tokens, by selling it as incremental modules or simply through pricing power given the increased customer value.

Our portfolio company, IFS, is a great example of this. The Sweden-based company provides software for enterprise resource planning (ERP), field service management (FSM) and enterprise asset management (EAM). Clients are generally large enterprises in asset- and service-intensive industries, which means IFS is tightly embedded in their core workflows and processes. For many of these clients, IFS is the first software they open in the morning and the last they close at night.

This creates a powerful control point for applying AI – embedding it in those existing workflows where customers already operate to make them more efficient. Since EQT first acquired IFS a decade ago, the company’s revenue has roughly quintupled, reaching an estimated €1.5bn in 2025. Operating profit has surged 12x.

IFS is building a library of industrial agents, from asset health monitoring to autonomous dispatchers. Its AI dispatch agents already dynamically schedule and optimize time and work for more than 300,000 field technicians – something that used to be done manually. Administrators shift from executors to supervisors of the system, and conversational “what if” replanning becomes possible. “Fifty technicians called in sick today. How do we cover the gap, ensuring our key customers get the best treatment?” The agent proposes a solution, the human reviews and provides additional guidance if needed. Once agreed, the AI agent updates all schedules and syncs them across field devices.

This reflects the wider industry trend: 73 percent of field service teams use automation today for scheduling, dispatch, and work order management, according to Salesforce. By handling routine admin and repeatable tasks, agents free up time and attention for administrators and field technicians, so they can focus on outcomes, strategy and customer value. But with agentic AI, you move from simple automation to interaction, using natural language to help you decide, iterate and act on what you need to do.

Using IFS in the field

For technicians using the IFS app in the field every day, it transforms the interaction from static software to having a coach in their pocket.

Agentic AI represents both a new user interface and a new co-worker. This is the future of how we will interact with a lot of enterprise software applications. Capgemini predicts that by 2028, 38 percent of organizations will have AI agents as team members within human teams. Such blended teams will become the norm, driving productivity. Right now, this technology is at the level of a junior analyst, junior software developer or junior customer service agent, but that capability is only going to improve.

AI is going to move profit pools a lot. Some companies will win and others will lose. In total, however, embedding agentic AI into sticky enterprise software on existing workflows will expose companies to a growing profit pool, particularly as AI has the potential to improve both inputs (efficiency, lower costs) and outputs (better service, higher value). Customer support is a good example: you can simultaneously reduce your cost-to-serve and improve quality with agentic AI. However, the bar for what you need to deliver will increase as a result, and you really need to stay ahead of the curve, because when technology evolves, customer expectations increase as well.