One of the most surprising conversations I have had on IFS Voices of Industry so far happened in Episode 3, when Martin Harris told me that engineers spend roughly a third of their working time just looking for information. 

Not fixing things. Not building things. Searching. 

A third. That was the number that set the tone for the rest of our conversation. 

IFS Voices of Industry is a video series that grounds the conversation around Industrial AI in real experience. Each episode brings together people across IFS who have spent time in the field, in hangars, on construction sites, and who now shape how AI is built and applied through IFS.ai. 

For Episode 3, I sat down with Martin Harris, Principal Product Manager in our Service and Asset Management group in IFS R&D. Martin started his career in civil engineering in 1991, building roads and bridges. He moved into aerospace and defense, worked with one of the UK’s major defense contractors, and then spent over thirty years in IT working directly with customers across the world’s key industries. That range of experience shaped every part of how he talked about what industrial organizations are up against today.

What are the biggest challenges in asset management today? 

I asked Martin what he sees as the biggest challenges across the industries he works with. His answer was data quality, data volume, a retiring workforce, and clunky processes. 

These are definitely not new problems. These problems have been around for a long time. But the scale of them has changed. Organizations now sit on enormous volumes of data spread across systems, documents, and years of operational history. At the same time, the people who knew how to interpret that data are leaving the workforce. 

“It’s trying to unlock some of those challenges and make it easy for people to access information, the right information at the right time.” 

For asset-intensive industries like energy, aerospace, defense, and manufacturing, this combination of growing data and shrinking expertise creates real pressure. The information exists somewhere, but the problem is finding it fast enough to act on it. 

How AI copilots are changing the way field engineers work 

AI copilots reduce the time field engineers spend searching for information by surfacing relevant asset histories, maintenance records, and technical documents in context. Martin described engineers currently spending about a third of their working time just looking for data. Copilots can cut that significantly. 

When engineers spend less time searching and more time doing the work they were hired for, the impact flows through the entire operation. 

“If you can take time away from people trying to search information, that gives them time to do the work they really should be.” 

I hear this same frustration from customers and industry leaders all the time. The knowledge is there, but it’s buried in the data. So, the people on the ground just cannot get to it fast enough. 

How to start an AI journey in asset-intensive industries 

I closed by asking Martin for one piece of advice for organizations starting their AI journey. His answer was quite simple, surprisingly.  

Focus on the end goal first. 

Martin has seen many organizations experiment with AI, run prototypes, and play with new tools, but the ones that stall tend to share a common pattern: they started without a clear picture of what they were trying to achieve. The technology moved forward, but the business case did not. 

“Keep the end goal in mind and work out what data you need, what tools you need, what processes.” 

Always be ready to adjust. The end goal might shift as the organization learns more about what is possible. AI adoption in asset-intensive industries is not a one-time implementation. It is an ongoing process, and it needs room to change shape as the business does.