IFS spent its annual user conference talking about data, and the quieter conversations were more revealing than the keynotes. The official message: data is the success key, the opportunity area, the foundation for AI-driven services. In the customer breakouts and corridor chats, a different picture emerged: most organisations lack the data discipline to deliver on that promise yet.
This is familiar territory for anyone working in industrial and asset-intensive sectors where IFS operates. Field service, aerospace, manufacturing, energy: these are businesses with decades of legacy systems, fragmented master data, and operational records that were never designed to feed machine learning models. The AI features IFS is building assume a level of data consistency that many customers are still working toward.
Hiring specifications in these sectors have grown more specific over the past year. A client in the aerospace supply chain asked us recently for a data engineer who could also read a maintenance log and understand why records might be incomplete. Another in energy wanted someone who knew how spare parts catalogues drift out of sync with warehouse reality. Domain knowledge at that level is harder to source than pipeline-building skill.
Conferences like this one surface a persistent tension. Vendors present AI as deployment-ready, while customers privately acknowledge they are still fixing the data underneath. The candidates who can run a data quality programme alongside an AI pilot, who have the patience for unglamorous cleanup work, are the ones clients keep circling back to. Technical fluency earns an interview. Operational context is what closes the role.
Prompted by reporting from Diginomica.