Measuring AI return on investment

Start with today's baseline

Measure how the process runs now. How many items a month, how many minutes each, how often something goes wrong and what each mistake costs. Take an accounts team that enters 2,000 supplier invoices a month at about four minutes each. That is roughly 130 hours of typing, before anyone corrects a wrong GSTIN.

Without a baseline like this, any result can be claimed and none can be proven.

Estimate realistic automation

Very few processes are fully automated on day one. Estimate the share of items the system will handle on its own, and the review time for the rest. If 60 percent of those invoices go straight through and the other 40 percent need a two minute check, the saving is real but well short of 130 hours. Write down the smaller number. A modest estimate that comes true does more for your next budget request than a bold one that does not.

Count all the costs

• Building the pilot and taking it to production.
• Running costs: model usage, hosting and monitoring.
• The time people spend reviewing output.
• Training, and the quieter cost of people changing how they work.

Value that counts

• Hours freed, and what those hours will be used for. If the answer is "nothing in particular", the saving is weaker than it looks.
• Errors avoided and what they cost, such as rejected input tax credit or duplicate payments.
• Shorter cycle times, such as faster customer onboarding or collections.
• Revenue protected, such as calls answered after hours that used to go to voicemail.

Prove it after launch

Report against the same measures as the baseline, every month. If the numbers fall short, narrow the scope or stop. Stopping a weak use case early is a good result. It frees money and attention for a better one.

For a first rough estimate, try our AI ROI Calculator.