Illustrative Scenario
Facilities & Building Services
How a $22M facilities management company used AI to increase EBITDA by 38% and achieve a 5.2x valuation multiple at exit.
Company Profile
Sector
Commercial facilities management
Services
Cleaning, maintenance, security, grounds
Revenue
$22M
Employees
145 (incl. 110 field staff)
Ownership
Founder-led, 18 years in operation
Exit Timeline
Owner targeting sale within 18 months
A well-established commercial facilities management business servicing office buildings, retail centres, and industrial parks across two states. The company had built a strong client base through the founder's relationships, but operational complexity had grown beyond what manual coordination could handle efficiently.
The Challenges
The business was profitable but underperforming relative to its revenue. Several structural issues were suppressing EBITDA and creating risk that any buyer would price into the valuation.
Owner dependency
The founder personally managed all major client relationships and approved every quote over $5,000. No structured handover or delegation framework existed.
Manual scheduling
Field staff were scheduled via spreadsheets and phone calls. Last-minute changes caused cascading inefficiencies — overtime costs were running at 14% of labour spend.
Revenue leakage
Ad-hoc quoting and inconsistent billing meant approximately $800K in annual work was either underquoted, unbilled, or written off.
Poor visibility
Financial reporting was monthly, backward-looking, and lacked granularity by client, service line, or site. The business could not demonstrate earnings quality to a buyer.
Unstandardised processes
Each site manager ran their operation differently. Quality varied, complaints were handled reactively, and there was no documented standard operating procedure.
What We Implemented
Over a 14-month engagement, we deployed five targeted AI and automation interventions, each selected for its direct impact on EBITDA.
AI-driven workforce scheduling
Replaced spreadsheet-based rostering with an automated scheduling engine that matched staff to jobs based on location, skill set, and availability. Overtime dropped from 14% to 4% of labour spend within six months.
Automated quoting and billing
Implemented an AI-assisted quoting system that generated quotes from job specifications and historical data. Integrated with billing to eliminate the gap between work completed and invoiced. Revenue capture improved by $620K annually.
Real-time operational dashboards
Built automated reporting across client profitability, site-level margins, labour utilisation, and cash flow. Gave the management team — and prospective buyers — clear visibility into earnings quality.
Customer communication automation
Deployed AI-powered triage and response management for client requests. Response times dropped from 4+ hours to under 30 minutes. Complaint resolution became proactive rather than reactive.
Process standardisation and documentation
Used AI to document existing workflows, identify inconsistencies, and generate standardised SOPs across all sites. Reduced the business's reliance on institutional knowledge held by the founder and key managers.
The Outcome
After 14 months of implementation, the business presented to market with materially improved financials, reduced operational risk, and a clear, auditable performance record.
| Metric | Before | After | Change |
|---|---|---|---|
| Revenue | $22.0M | $23.4M | +6.4% |
| EBITDA | $2.6M | $3.6M | +38% |
| EBITDA Margin | 11.8% | 15.4% | +3.6pp |
| Valuation Multiple | 3.8x (est.) | 5.2x (achieved) | +1.4x |
| Enterprise Value | $9.9M (est.) | $18.7M (achieved) | +89% |
| Overtime as % of Labour | 14% | 4% | -10pp |
| Revenue Leakage | ~$800K/yr | ~$180K/yr | -78% |
In this scenario, the business sold at a 5.2x EBITDA multiple, achieving an enterprise value of $18.7M. The strength of operational systems, earnings visibility, and reduced owner dependency were key factors in the buyer's pricing.
This is an illustrative scenario based on a composite of common operational patterns in facilities management. It does not represent a specific client engagement, and the figures shown are not guaranteed outcomes. Every business is different.
Is your business facing similar challenges?
We begin with a confidential discussion to assess where AI can have the greatest impact on your EBITDA and exit value.