How to Calculate AI ROI Before You Build

The AI ROI trap

The most dangerous number in AI is a vendor's ROI projection. It is almost always calculated backwards — starting from the desired outcome and working back to assumptions that make the numbers work. The AI ROI trap is believing that because AI can automate a task, it will save the full loaded cost of the people currently performing it. In reality, AI changes how people spend their time; it rarely eliminates roles entirely. The lawyer whose document review is accelerated by 60% does not become 60% cheaper — they spend that freed capacity on higher-value work that was previously deferred. The ROI is real, but it shows up as increased throughput, faster time-to-revenue, and reduced risk — not as a simple headcount reduction line item. Measuring AI ROI honestly requires a framework that captures both direct cost savings and second-order value creation.

The five-question framework

1. What is the current fully-loaded cost of the process? — Include not just salaries but overhead, management time, rework, and the opportunity cost of delays. A document review that costs $150,000 in associate time may cost $250,000 when you add partner supervision, write-offs, and the deals that did not close because due diligence was slow.

2. What is the AI's expected impact on throughput, not just cost? — If AI accelerates a process by 50%, does that mean you can handle 50% more volume with the same team, or that the team can redirect 50% of its time to higher-value work? The answer changes the ROI calculation significantly.

3. What are the hard costs of the AI system? — Compute, software licenses, integration, training, and ongoing maintenance. Be honest about ongoing costs; a model that requires monthly fine-tuning on new data has a different cost profile than a static deployment.

4. What is the risk of not doing it? — If competitors deploy AI and reduce their turnaround time from weeks to days, what is the cost of lost business? This is the hardest number to quantify but often the most important.

5. What is the implementation timeline and break-even point? — When does cumulative benefit exceed cumulative cost? For most well-scoped AI projects, this should happen within 6-12 months.

Sample calculation — document review automation

Consider a mid-sized law firm spending 3,000 hours per year on first-pass document review for due diligence, at a blended rate of $500/hour — an annual cost of $1,500,000. ALTE deploys a document review AI system at a one-time build cost of $180,000 and annual hosting, maintenance, and fine-tuning costs of $45,000. The system reduces first-pass review time by 75%, from 3,000 hours to 750 hours. The remaining 750 hours still require human review at $500/hour ($375,000). Annual savings: $1,500,000 − ($375,000 + $45,000) = $1,080,000. Year-1 ROI: ($1,080,000 − $180,000) / $180,000 = 500%. However, a more conservative calculation assumes six months to full productivity: year-1 savings of $540,000 against costs of $180,000 + $22,500 (half-year maintenance) = $202,500, for a year-1 net benefit of $337,500 and an ROI of 167%. Break-even occurs in month 3 of full operation. Even the conservative case justifies the investment decisively.

The pre-build ROI audit

ALTE offers a two-week pre-build ROI audit for organisations considering AI investment. We model the current-state process cost, define the target-state AI workflow, calculate the hard costs of deployment, and produce a three-year net-present-value projection with sensitivity analysis on key assumptions (adoption rate, accuracy, maintenance cost escalation). The output is a board-ready business case that can withstand CFO scrutiny. More importantly, it identifies the use cases where AI will and will not generate a positive return — saving organisations from investing in AI for AI's sake and directing capital to the opportunities where it will actually move the needle.