IFS Unleashed - data as the success key, the opportunity area, and unspoken friction

ERP, Preventative Maintenance, Service Management and AI vendor IFS had its annual user get-together in Orlando last week. There were easily a couple of thousand attendees, lots of partners, and, many of the world’s biggest systems integrators in attendance. Given the proximity to golf courses and the ability to get your own custom embroidered golf hat, I suspect numerous IFS prospects were being treated to a solid round of golf as part of their software ‘negotiations’. The weather for that was great and I bet all kinds of IFS deals and partner firm initiatives got closed all while the sales teams got their tans deepened this week. No surprise, but there were non-stop AI announcements, presentations, etc. Here the best bits from the show. Knowledge Base data is key to AI success Several of the customer case studies and demo sessions noted how the handling around key knowledge (e.g., product knowledge, process knowledge, institutional knowledge, etc.) is central to adding value to the enterprise and improving AI application successes. In the demonstrations, there were examples that documented how much time frontline workers, service professionals, etc. were spending updating or entering service data, product diagnostic tips, resolution disposition information and more. These time requirements were sometimes 40% of a worker’s total job time. Case study examples showed: How important accurate, current product, repair and service history information can be in predicting future product failures, estimating repair times, determining needed parts/supplies and serving up the most relevant diagnostic tools/tips. How AI can help capture some of this core knowledge information automatically and free up expert personnel to assist more customers. It could also assist them more accurately and quickly. We also saw how this knowledge data can cross from digital AI tools (e.g., Agentic or Generative AI apps) to physical AI tools. To that end, we saw how robotic devices can diagnose machine tool issues using the contents of these knowledge bases. Those robots can then trigger AI Agents to order or deliver spare parts, schedule a repair technician (or in some cases, the robot can affect the repair), perform post-repair tests/diagnostics, etc. Other agents can use the diagnosis to decide if the production needs to be shifted to other machinery or whether the entire master production schedule needs to be optimized anew if the delay is too lengthy. Bringing digital and physical AI together (and powering them with highly relevant and current information) doesn’t get a lot of press but it was the sub rosa star of the show for me. Customers, and IFS customers are mostly manufacturers, certainly noticed that they need quality shop floor and other data. For the last couple of years, we’ve being hearing that “data is the new oil” but there’s been scant discussion of this in most enterprise software shows of late. IFS didn’t club anybody over the head with its points of view re: data but it definitely showcased how great data with AI could really make a difference for their customers. Where the opportunity area is for now In the IFS CEO keynote, we learned that industrial firms operate at approximately 60% of their capacity. The full potential of these operations is challenged because of labor shortages, siloed data, unplanned downtime, etc. But other factors were also tagged including time spent by workers searching for needed data, entering data, etc. A fair bit of what IFS customers related to attendees fell into uses of IFS AI tech to get rid of this inefficiency. This meant that they implemented AI solutions designed to remove process friction and improve operational efficiencies and productivity. That’s interesting as many of these improvements (e.g., letting AI pre-fill forms, fix broken/brittle integrations, automatically approve routine transactions, etc.) are relatively tactical use cases. They aren’t necessarily big, newsworthy improvements from a tech perspective but they are moving the productivity needle for the companies implementing them. Additionally, these AI led efforts appear to possess a solid ROI and low risk. Customers will likely want a lot of this kind of AI work for the near-term. It might not create sexy outcomes but it does deliver low-risk/high-value results while helping firms get up the AI learning curve. Taken together, the different customers IFS showcased were using IFS AI tools to deal with a kind of business problem that fits their risk, financial and other needs. While some of IFS competitors are trying to showcase cutting-edge AI solutions (e.g., creating a new modern replacement application by letting an AI tool study a 30-year-old videotape of a user keying data into a green screen software application), their case studies feel overly ambitious and possess potentially dubious ROI. The unspoken friction discussion Friction comes in a couple of flavors and some of it can be minimized in a few ways. As noted above, AI can help get rid of a number of friction points by eliminating several non-value-added or low-value-added activities. What I wish the speakers would have addressed is that mid-managers and many experienced subject matter experts spend large parts of their day smoothing out the never-ending array of productivity impediments and unplanned efficiency events. Every day, all-new issues arise that, by themselves, might not ever warrant an automated or dedicated AI solution but it’s their volume and diversity that causes firms to have experts who know exactly how to navigate around these new time, economic and productivity sinks. AI is not going to eliminate all managers. It might permit some level of management de-layering but managers will persist. Managers deal with things like: bomb scares, wild animals getting into the building, short circuits caused by a rodent chewing on a power cable, an employee having a heart attack on the shop floor, etc. Their job is broad, highly irregular, hard to plan, harder still to train effectively, and, essential. Next IFS event, it would be great to get more information on what IFS technology will do to the management layer, not just the frontline worker, and how customers should prepare for this. My take I appreciated how this show was quite business focused. My shortlist of things to like included: Only a modest amount of discussion re: IFS’s AI architecture. EVERY vendor this year has spent way too much time talking AI architectures and way too little addressing the actual business value their solutions could be delivering. IFS had a good balance. Customers and prospects could relate to the use cases seen in the presentations. It was good to hear perspectives on the power of industrial-focused LLMs, semantic graphs, etc. Using general purpose AI tools won’t necessarily benefit an industrial firm as well and might require a lot more harness, person in the middle and other controls. I liked the Nexus Black progress. This group helps IFS customers deploy new AI capabilities and acts like forward deployed engineers. Because this is a service (not technology) led group, it may not scale as rapidly as software but the learnings can be transferred to other customers and/or added to existing IFS capabilities. For a detailed review of the show and its announcements see colleague Phil Wainwright’s piece here.