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Your AI Readiness Checklist for Real Estate & Facility Management Operations

In Part 1 of this 3-part blog series, we explored how asking for AI as a blanket statement leaves too much room for confusion and is not a great approach to bringing AI-driven value to your organisation. Instead, you and your teams can add much more value by aligning in these three key areas first:

  1. Understanding your Data Foundation.
  2. Understanding your main pain points and challenges.
  3. Understanding the People & Processes that will be affected.

Here’s an AI Readiness Checklist that you and your teams can use to get started:

AI Readiness Checklist

Section A: Outcomes and Scope


✅ Can we state the 1–3 specific business outcomes we want to achieve (e.g., reducing energy spend, reducing reactive work orders, or optimising space)?

✅ Do we have documented baseline metrics from the last 6–12 months to measure our success against?

✅ Have we clearly defined the specific sites, the 6–10 week timeframe, and exactly what remains out of scope for this initial phase?

Section B: Data Foundation

✅ Have we identified all systems holding the necessary data (BAS, CMMS, utility meters, occupancy sensors, etc.)?

✅ Do we have the legal and technical means to extract data (via APIs or exports), and have we identified the "gatekeepers" who can grant access?

✅ Have we sampled the data to identify gaps like missing meters, inconsistent asset IDs, or unstructured notes that might hinder the AI?

✅ Can we map our assets, spaces, and work orders to a consistent structure that the AI can interpret, even if that structure is currently imperfect?


Section C: Governance, Security, and Risk

✅ Have we established clear rules regarding PII (Personally Identifiable Information), data retention, and where the data will be processed?

✅ Has IT/Security approved a "lightweight" path for this pilot to ensure vendors and integrations meet safety standards without stalling the project?

✅ Is there a designated person responsible for auditing and approving the actions the AI recommends, ensuring we avoid "black box" decision-making?

Section D: People and Process

✅ Do we have a defined process for how insights move from the AI to the frontline (triage → dispatch → verification)?

✅ Is there a simple plan for training staff (30–60 minutes) and a formal loop for technicians to provide feedback?

4 steps to get started in 90 days

  1. Inventory your pain points and data. This should include a short workshop with stakeholders from these areas: FM/RE, IT, and Finance.

  2. Pick 1–2 pilot use cases (e.g., predictive maintenance, space optimisation, ticket routing). Keeping the scope manageable is key! Here are some areas that many organisations are exploring for AI pilots:

    1. Adaptive Maintenance: Predicting equipment failures.
    2. Comfort Control: Automatic HVAC & lighting adjustments based on actual occupancy patterns.
    3. Energy Demand: Detections in energy consuption anomalies.
    4. Space Utilisation: Layout recommendations; booking improvements. Detection of underused rooms & space; recommendations on better usage.
    5. Workplace Services: Requests created and processed through short, converational messages vs lengthy, confusing forms.

  3. Partner with your platform provider to see what’s already available for you to use vs. what needs configuration.

  4. Define metrics and a learning loop. Don’t chase perfection; chase measurable improvement.

In many ways, the potential benefits of AI are forcing organisations to ask the hard questions about their current operations. But remember, you don’t need a 5-year AI masterplan to get started. You just need a first, well-chosen experiment that respects the bigger picture.

This is Part 2 of a 3-part blog series on the topic of “AI for RE & FM”. In our next blog, we’ll dive into the world of RFPs and share tips on how teams can shape their RFP questions to ask for the AI-enabled solution they need.

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