Start Lesson
Your CEO asks: "If we invest in AI for customer support, what do we get back?" You could say "significant efficiency gains" -- and get ignored. Or you could say "$22,000 per month in recovered labor costs at a 12x return, breaking even in 3 days." One of those answers gets budget approval. This lesson gives you the calculator to produce the second answer.
By the end, you will have a filled-in ROI projection for your top AI opportunity -- the one you scored in Lesson 1 and chose a Prompt/Buy/Build path for in Lesson 2. Real dollars. Conservative estimates. A number you can put in front of a CFO.
A completed AI ROI Calculator with monthly and annual projections, including hidden costs most people forget, for your highest-priority AI opportunity.
(Hours Saved x Fully Loaded Hourly Cost) - Total AI Cost = Monthly ROI
Three variables. No consultants required.
| Variable | How to Calculate | Common Mistake | |----------|-----------------|----------------| | Hours Saved | Current hours on task minus projected hours with AI. Use conservative estimates. If you think AI cuts 10 hours to 2, estimate 10 to 4 until you have real data. | Assuming 90% time savings on day one. Start with 50-60% and adjust after 30 days. | | Fully Loaded Hourly Cost | (Annual salary + benefits + overhead) divided by 2,080 work hours. A $75K/year employee is roughly $50/hour. A $120K/year employee is roughly $80/hour. | Using base salary only. Benefits and overhead add 30-50%. | | Total AI Cost | Subscription fees + API costs + setup labor + ongoing maintenance hours valued at hourly rate. | Forgetting setup time and maintenance. |
Situation: A 4-person marketing team spends 20 hours/week collectively on social media posts, blog drafts, and email newsletters.
AI intervention: Claude Pro at $20/user/month plus a shared prompt library (4 hours to build).
| Line Item | Calculation | Monthly Value | |-----------|-------------|---------------| | Current time on task | 20 hrs/week x 4.3 weeks | 86 hours/month | | Time with AI (conservative 60% reduction) | 86 x 0.40 | 34 hours/month | | Hours saved | 86 - 34 | 52 hours/month | | Fully loaded hourly cost | $75K salary = ~$50/hr | $50/hr | | Gross monthly value | 52 x $50 | $2,600 | | AI subscription cost | 4 users x $20 | -$80 | | Prompt library maintenance | 2 hrs/month x $50 | -$100 | | Error correction overhead (15%) | 52 x 0.15 x $50 | -$390 | | Net Monthly ROI | | $2,030 | | Annual ROI | $2,030 x 12 | $24,360 | | Setup cost | 4 hrs prompt library + 4 hrs training x $50 | $400 (one-time) | | Payback period | $400 / $2,030 | < 1 week |
Return on AI spend: 25x. And this is the conservative estimate -- it does not count the additional content volume the team can now produce with freed-up hours.
Situation: A SaaS company handles 500 support tickets/day. Average ticket: 8 minutes of agent time. 12 support agents at $60K/year ($40/hour fully loaded).
AI intervention: Enterprise AI triage tool at $2,000/month. Auto-resolves 40% of tickets, pre-drafts responses for 30%.
| Line Item | Calculation | Monthly Value | |-----------|-------------|---------------| | Auto-resolved tickets | 200/day x 8 min = 1,600 min/day | 587 hrs/month | | Pre-drafted responses (save 4 min each) | 150/day x 4 min = 600 min/day | 220 hrs/month | | Total hours saved | 587 + 220 | 807 hours/month | | Fully loaded hourly cost | | $40/hr | | Gross monthly value | 807 x $40 | $32,280 | | AI tool subscription | | -$2,000 | | Integration setup (amortized) | $8,000 over 12 months | -$667 | | Monitoring and maintenance | 10 hrs/month x $80 (dev rate) | -$800 | | Error correction (10% of auto-resolved need re-review) | 20/day x 8 min x 22 days x $40/hr | -$940 | | Net Monthly ROI | | $27,873 | | Annual ROI | $27,873 x 12 | $334,476 | | Setup cost | Integration: $8,000 + training: $2,000 | $10,000 (one-time) | | Payback period | $10,000 / $27,873 | 11 days |
At this scale, the AI investment pays for itself before the first month ends.
The formula above is clean, but real deployments have friction. Budget for all of these:
| Hidden Cost | Typical Range | When It Applies | |-------------|---------------|-----------------| | Prompt engineering (upfront) | 20-40 hours | All paths | | Prompt maintenance (ongoing) | 2-5 hours/month | Prompt and Build paths | | Error correction overhead | 10-20% of "hours saved" | All paths | | Team training | 4-8 hours/person (initial) + 1 hr/month | All paths | | Integration development | $5,000-$20,000 (one-time) | Buy and Build paths | | Integration maintenance | 5-10 hours/month | Buy and Build paths | | API costs at scale | $0.01-$0.10 per request | Build path |
The honest formula:
(Hours Saved x Hourly Cost) - AI Subscription - Setup Cost (amortized) - Ongoing Overhead = Real Monthly ROI
Not every AI project pays off. Run the numbers before committing, and watch for these patterns:
Review time exceeds savings. If every AI output needs 15 minutes of editing, and the task only took 20 minutes manually, you saved 5 minutes and added complexity. Net loss.
Error costs exceed time savings. If a single AI mistake costs $10,000 (wrong invoice, bad legal clause, incorrect medical info), and the expected error rate is 5%, your expected monthly error cost is $10,000 x 0.05 x monthly volume. Run that number.
Volume is too low. AI shines on tasks that happen hundreds of times. If the task happens 5 times a month, setup costs rarely justify savings.
The task changes faster than you can tune. If the underlying process changes weekly, you spend more time updating AI workflows than you save.
The honest conclusion: Sometimes the right answer is "do not invest in AI for this." That finding saves you real money.
Two projects with identical annual ROI can be very different investments:
| | Project A | Project B | |---|---|---| | Monthly ROI | $5,000 | $1,000 | | Setup time | 6 months to build | 1 week | | Setup cost | $40,000 | $500 | | First-year return | ($5,000 x 6) - $40,000 = -$10,000 | ($1,000 x 12) - $500 = $11,500 |
Project B wins year one despite lower monthly ROI because it started generating value immediately. Always ask: when does this start paying for itself?
Take your #1 AI opportunity (from Lesson 1) with its Prompt/Buy/Build recommendation (from Lesson 2). Fill in this calculator:
Opportunity: _________________________ Path: Prompt / Buy / Build
| Line Item | Your Numbers | |-----------|-------------| | Current hours/month on this task | | | Estimated hours/month with AI (be conservative) | | | Hours saved/month | | | Fully loaded hourly cost of person(s) doing task | $ | | Gross monthly value of time saved | $ | | AI tool/subscription cost per month | -$ | | Setup cost (amortized monthly over 12 months) | -$ | | Ongoing maintenance hours x hourly rate | -$ | | Error correction overhead (15% of gross value) | -$ | | Net Monthly ROI | $ | | Annual ROI | $ | | Payback period (total setup cost / net monthly ROI) | |
Your output should have: A single dollar figure for monthly ROI, annual ROI, and payback period. If the payback period is longer than 6 months, reconsider the path -- a simpler approach (Prompt instead of Build) might deliver faster returns.
You have the what (Lesson 1), the how (Lesson 2), and the how much (this lesson). Now you need the when: a phased rollout plan that sequences your AI adoption so each phase builds organizational knowledge for the next. Lesson 4 gives you the roadmap template. Bring your ROI calculator -- you will use the payback period to decide which phase each opportunity belongs in.
