CFOs are being asked to defend or reduce real estate spend with sharper financial evidence than property teams alone can produce. AI helps the most when you give it your own cost and utilisation data and ask it to build the case. Five practical use cases below.
Cost per occupied seat at different utilisation rates
Same office, same lease, very different cost per occupied seat depending on how much of the space is actually used.
Cost per occupied seat per day (£) for a 12,000 sq ft office at £65/sq ft
1. Real estate cost modelling
Tool: Claude or ChatGPT Code Interpreter
Build a simple model to calculate cost per occupied seat for our office. Inputs: annual rent £[X], rates £[X], service charge £[X], cleaning £[X], average daily attendance Y employees, working days per year 235. Calculate: total cost per day, cost per occupied seat per day, and cost per occupied seat at 40%, 60%, and 80% utilisation. Present as a table.
2. Board paper on office overhead
Tool: Claude Projects with cost and utilisation data
Write the office overhead section of a quarterly board paper. Include: current total real estate cost, cost per occupied seat versus benchmark, utilisation rate trend over the past two quarters, three options for cost reduction with estimated saving and implementation risk for each. Tone: direct, financial, evidence-based.
3. Lease renewal scenario analysis
Tool: Claude
Our office lease expires in 18 months. Current footprint: 12,000 sq ft at £65/sq ft. Current average utilisation: 58%. Model three scenarios: renew as-is, reduce to 9,000 sq ft, reduce to 7,500 sq ft. For each: annual cost, estimated savings versus current, utilisation required to justify the footprint, and key risks. Format as an executive summary table.
4. Vendor cost benchmarking
Tool: Perplexity or Claude with web search
Research current market pricing for integrated workplace management software for a 1,500-employee hybrid organisation. Include: per-user pricing ranges, typical implementation costs, common contract terms, and the three vendors most frequently shortlisted by UK financial services firms.
5. AI-powered financial dashboards via API
Tool: ChatGPT Code Interpreter or Claude with data files
Upload your quarterly cost reports as CSV:
Analyse this cost data and produce: a trend chart of total real estate cost per quarter, a breakdown of cost per category, a comparison of cost per employee year-on-year, and a forecast for the next two quarters based on the trend.
Charts and analysis that previously needed a finance analyst, done in 10 minutes.
Lease renewal scenario comparison
Three scenarios at next renewal: 12,000 sq ft office
| Scenario | Annual cost | Saving vs current | Utilisation needed |
|---|
| Renew as-is | £780k | £780k | - |
| Reduce to 9,000 sq ft | £780k | £585k | £195k |
| Reduce to 7,500 sq ft | £780k | £488k | £292k |
Risks every CFO must address
- Confidential financial data in public AI tools. Never upload board papers, unreported financial results, or M&A data to a public AI tool. Enterprise tiers with zero data retention or anonymised inputs are the baseline.
- AI-modelled scenarios are not audited outputs. AI-generated financial models contain assumptions that must be validated. They are starting points, not final numbers.
- Hallucinated benchmarks. AI invents market data when web search is not enabled or when sources are weak. Always verify against the source. Perplexity’s citations make this easier than pure Claude or ChatGPT for benchmarking work.
HybridHero for CFOs and finance leadersThe real estate evidence base your CFO has been asking for.
HybridHero gives finance leaders the cost-per-occupied-seat data, utilisation trends, and board-ready reporting that real estate decisions actually require. Already on another platform? The Switch Programme migrates you in 30 days.
Book a demo