AI and data readiness in FM: How mature is your FM data infrastructure?

1. 1. Which statement most honestly describes where your FM operation sits today?

   • Pre-foundation — data is fragmented and unreliable: Our asset data, maintenance records and sensor outputs are held across multiple disconnected systems with no single source of truth. We know the data quality problem exists but haven't yet addressed it systematically — AI is not a near-term possibility.
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   • Building the base — data governance underway: We've begun consolidating data into a unified platform and are working through asset data cleansing, system integration and governance frameworks. AI readiness is on the roadmap but we're 12–24 months away from meaningful deployment.
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   • Piloting cautiously — isolated use cases only: We have sufficient data quality to run controlled AI pilots in specific areas — predictive maintenance on critical assets, energy optimisation in one building, or helpdesk automation — but we haven't scaled and aren't confident the foundations would hold across the full estate.
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   • Scaling with guardrails — AI embedded in core FM processes: AI tools are live across multiple functions and facilities, integrated with our CAFM/IWMS and producing decisions we act on. We've addressed data quality governance and have human oversight protocols in place for high-risk outputs.
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   • Leading edge — AI is reshaping our FM operating model: AI is not a tool layer sitting on top of our existing model — it has changed how we structure the FM function, how we contract with providers, how we allocate resource and how we report to the business. We're measuring FM outcomes we couldn't previously quantify.
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