The fastest way to get an AI project approved — by yourself, your business partner, or your finance team — is to put real numbers in front of the decision. Most businesses skip this step and end up either overpaying for the wrong thing or stalling because they can't justify the spend. Here's the simple math you can run in 10 minutes.
Step 1: Pick a single repetitive task
ROI math falls apart when you try to calculate it across a vague "AI transformation." Pick one specific task: answering inbound calls, qualifying leads, generating weekly reports, sending follow-up emails, processing invoices. The narrower, the easier to measure.
Step 2: Measure what it costs you today
Time × hourly rate × frequency = current cost. If a task takes a salesperson 30 minutes a day at $50/hr loaded cost, that's $25/day or about $6,500/year. If you miss 5 inbound calls a week and a converted lead is worth $500, that's $130,000/year in missed revenue. Be honest. Most teams underestimate how much repetitive work actually costs.
Step 3: Estimate what AI will save
Most AI automations don't replace 100% of a task — they replace 70-90%. Use 75% as a conservative starting point. So your $6,500/year salesperson task becomes ~$4,875 in savings; your missed-call problem becomes ~$97,500 in recovered revenue.
Step 4: Subtract the cost of the AI
Implementation + ongoing usage. A typical small AI build is $500–$5,000 one-time, plus $100–$500/month in operating costs. Even on the high end, your payback period for most use cases is under 3 months.
Step 5: Pick the highest-ROI starting point
Run this math on 2-3 candidate workflows and you'll quickly see which one to start with. The biggest mistake is spreading effort thin — pick the one with the best ROI, ship it, and use the savings to fund the next one. Want help running these numbers? Take the AI Readiness Assessment and we'll surface your highest-ROI opportunities.