AI cannot transform FM without reliable, accessible, structured, and operationally meaningful data. Across the Knowledge Café notes, participants repeatedly pointed to data access, data sources, smart building maturity, common taxonomies, system integration, institutional knowledge and data-driven decision-making.
This theme cuts across several of the original Knowledge Café questions. It connects to AI as a strategic gamechanger, because AI must be tied to business objectives and outcomes. It also connects to AI, ESG and sustainability, where participants discussed smart buildings, space optimization and asset life cycle management. Finally, it connects strongly to the AI native FM organization, where the future organization was imagined as a more integrated, data-based function.
Many FM organizations are interested in AI, but the Summit data suggests that the first challenge is not the AI tool itself. The first challenge is whether the organization has the data maturity to use AI meaningfully.
FM data is often distributed across multiple systems, functions, vendors, contracts, buildings, and service lines. It may sit in IWMS platforms, BAS systems, CMMS systems, spreadsheets, smart building platforms, helpdesk systems, energy dashboards, asset registers, occupancy tools and the undocumented knowledge of experienced staff. Without a clear understanding of what data exists, where it sits, who owns it, how reliable it is and how it connects to decisions, AI risks producing fragmented or low-value outputs.
This finding strongly aligns with IFMA’s The Rise of the FM Analyst, which emphasized that the future of FM depends not only on access to data, but on the ability to ask better questions, interpret patterns, and translate insight into value. It also connects to IFMA’s circular economy research, in which data was identified as essential for understanding asset life cycle value, resource use, reuse opportunities and sustainability performance.
FM leaders should begin by assessing the current state of their data ecosystem. This means identifying key data sources, data owners, data gaps, system overlaps and areas where data quality is weak or inconsistent.
A practical first step would be to create a simple FM data map. This does not need to be complex at the beginning. It should identify where critical data sits and how it supports decision-making.
FM leaders should also focus on data governance. This includes agreeing definitions, creating common taxonomies, clarifying data ownership, setting data quality expectations and ensuring that data can be used responsibly.
AI- enabled FM depends on a strong data backbone. Participants recognized that AI cannot work effectively if data is fragmented, inaccessible, poorly structured or disconnected from operational reality.
AI will only become meaningful in FM when organizations build the data backbone needed to turn operational information into trusted decisions.
International Facility Management Association (IFMA) supports over 26,000 members in 140 countries. Since 1980, IFMA has worked to advance the FM profession through education, events, credentialing, research, networking and knowledge-sharing.
