A CFO in Brisbane sat across the table from me last month with a familiar look — equal parts scepticism and exhaustion.
“Every vendor tells me AI will transform the business,” she said. “None of them can tell me what it’ll actually save us.”
She wasn’t wrong to be frustrated. According to McKinsey’s 2025 Global AI Survey, 74% of executives say they struggle to quantify AI’s business value before committing to investment. In Australia, the Decidr AI Readiness Index found that 57% of SMEs treat AI as a cost to manage rather than a growth lever — largely because nobody’s shown them the maths.
Here’s the framework we use to fix that.
The 4-Step ROI Framework
Before you sign a contract or approve a pilot, answer these four questions:
Step 1: What’s the problem costing you today?
Don’t start with AI. Start with the workflow.
Pick a process your team complains about regularly — invoice processing, customer query triage, report generation. Then quantify:
• How many hours per week does this consume?
• How many people are involved?
• What’s the loaded cost per hour (salary + overheads)?
Example: A 3-person finance team spends 15 hours/week on manual invoice matching. At $85/hour loaded cost, that’s $66,300/year on one task.
Step 2: What’s the realistic improvement?
Be conservative. Vendors love to promise 80% efficiency gains. Reality is usually 30–50% for well-scoped automation.
For the invoice example: if AI reduces manual effort by 40%, you recover 6 hours/week — saving $26,520/year.
Step 3: What’s the total cost of implementation?
Include everything:
• Software licenses (annual)
• Integration and setup costs
• Training time (hours × people × hourly rate)
• Ongoing maintenance or support fees
Example: Copilot at $50/user/month for 3 users = $1,800/year. Setup and training = $5,000. Total Year 1 cost = $6,800.
Step 4: Calculate the payback
Simple formula:
Net Benefit = Annual Savings – Annual Cost
ROI = (Net Benefit / Total Investment) × 100
Using our invoice example:
• Year 1 savings: $26,520
• Year 1 cost: $6,800
• Net benefit: $19,720
• ROI: 290%
That’s a business case a CFO can approve in one meeting.
The Traps to Avoid
When we review AI business cases that failed to get funded, we see the same mistakes:
• Vague benefits: “Improved productivity” doesn’t mean anything. Quantify in hours or dollars.
• Ignoring adoption costs: Training is never free. Neither is the productivity dip during the learning curve.
• Overpromising: A 70% automation rate sounds great until you miss it by half and lose credibility for the next project.
A Quick Sanity Check
Before you pitch any AI investment, run it through this filter:
• Can I explain the current cost in one sentence?
• Is the improvement assumption defensible (not just vendor claims)?
• Have I included all implementation costs — not just licenses?
• Does the ROI hold even if results are 30% below expectation?
If yes to all four, you’ve got a case worth making.
The Bottom Line
AI isn’t magic. It’s maths. And the organisations getting real value from it aren’t the ones chasing hype — they’re the ones who can show the numbers before they spend.
As Gartner noted in their 2025 AI Value Realisation Report, companies that establish clear ROI frameworks before implementation are 2.4x more likely to scale AI beyond pilot.
Start with the problem. Quantify the pain. Model the fix. Then decide.
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Sources: McKinsey Global AI Survey 2025; Decidr National AI Readiness Index 2025; Gartner AI Value Realisation Report 2025.
