Most AI programmes in FM stall before they start — not on the model, but on the data underneath it. This session shows you how to find out where you stand.
Part 1 cut through the noise around LLMs and mapped where AI genuinely helps across buildings, assets and operations. Part 2 gets practical: the first three moves every FM team needs to make before any serious AI programme — define the problem, build the case, and check the data honestly.
Start from a real operational challenge — repeat callouts, first-time fix, poor asset performance, energy waste, manual reporting — not a technology choice.
What decision or outcome are we actually trying to improve?
Not every opportunity is worth doing first. The strongest cases tie to measurable value across maintenance, energy, compliance, uptime and contractor performance.
Where would better data create the clearest business impact?
Then the honest bit: does the right data exist, is it accessible, and is it clean, structured, frequent and contextual enough to act on repeatedly?
Is the data reliable enough to make this repeatable?
How unclear problems, weak cases and poor foundations quietly cap the impact of AI in FM.
A practical way to spot use cases that are operationally valuable and technically realistic.
Connect AI opportunities to measurable outcomes across cost, risk, uptime and energy.
What to check across sources, telemetry, asset naming, gaps, ownership and system access.
How one operational layer becomes the foundation for analytics and automation.
A preview of exploration, simplified modelling, workflow pilots, governance and scale.
The full readiness journey stays in view, but we go deep on steps one to three. Highlighted rows are the core of the session.
AI in FM creates value when the right data is connected to the right problem and the right operational outcome. The earliest stage of readiness isn't model selection — it's business clarity and data honesty.
Data & Innovation Director, LoweConex
Senior Data Scientist, LoweConex
Sales Director, LoweConex
