AI Business Case Template for Manufacturing Ops
A no-fluff AI business case template for manufacturing ops, built by an ex-VP of AI. Fill in the numbers, get a one-page case finance will actually approve.
Most AI projects at mid-market manufacturers die in the approval meeting, not the build. The deck has a model, a demo, and a vibe. It has no number. An AI business case template that survives a CFO's questions does one thing the demo never did: it puts a defensible dollar figure on a specific workflow and shows when the money comes back. I shipped agents into real operations at a $250M furniture manufacturer, and the cases that got funded all looked the same. Here's the template, the way I'd hand it to a plant controller.
What a real AI business case has to answer
Finance isn't skeptical of AI. They're skeptical of vague. A business case that gets approved answers four questions in plain numbers:
- What workflow, exactly? Not "AI for procurement." "Supplier-document lookups for the buying team, ~40 a day across 6 buyers."
- What does it cost us today? Loaded labor hours, error/rework dollars, or both.
- What does the agent cost to build and run? Build, integration, and 12-month run cost.
- When do we break even, and what's the 3-year picture? Payback in months, then NPV.
If you can't fill those in, you don't have a business case. You have an experiment, and you should fund it as one — small, time-boxed, with a kill date.
The one-page AI business case template
Copy this. One workflow per page. Resist the urge to bundle five agents into one case; you'll lose the line-by-line credibility that gets a yes.
| Section | What goes here | Example (supplier-doc agent) |
|---|---|---|
| Workflow | The specific, repeated task | Buyers searching POs, specs, certs, datasheets |
| Frequency | Volume per day/week | ~40 lookups/day, 6 buyers |
| Time per instance | Today, manually | ~12 min avg (email + dig through SharePoint) |
| Current annual cost | Hours × loaded rate, plus error cost | 2,000 hrs/yr × $65 = $130K |
| Agent build cost | One-time, fully loaded | $45K (build + ERP/doc integration) |
| Annual run cost | Inference, hosting, maintenance | $18K/yr |
| Time saved | Realistic %, not 100% | 60% of lookup time |
| Annual benefit | Conservative, post-adoption | $78K/yr |
| Payback | Build ÷ monthly net benefit | ~9 months |
| 3-yr net | Benefit − run − build | ~$135K |
| Owner | A named human | VP Procurement |
| Success metric | One number, tracked weekly | Avg lookup time ↓ from 12 to 5 min |
The example numbers are illustrative — swap in yours. The structure is the point.
How to fill in the numbers without lying to yourself
Current cost: count hours and rework, separately
Two cost buckets, and most cases only count the first. Labor is hours × loaded rate (use fully-loaded, not base wage — usually 1.3–1.4x). Rework and error is the quieter one and often the bigger one: a wrong config that reaches the floor, a quoting error eaten on margin, a misread spec that scraps a run. On the order-hygiene workflow, the error cost dwarfed the labor cost. Pull six months of actual rework tickets and put a dollar figure on the ones an agent would have caught.
Benefit: haircut it twice
Two haircuts keep you honest. First, the agent won't save 100% of the time — a buyer still reviews the answer, still handles edge cases. Use 50–70%. Second, adoption isn't instant — model a ramp, not a step function. I assume ~60% of full benefit in year one. If the case still clears payback after both haircuts, it's real.
Costs: don't forget integration and the second year
The build number people quote is the model work. The number that bites is integration — wiring the agent to your ERP, your document store, your ticketing system. Budget integration at 40–60% of total build for a manufacturer with older systems. And put a real maintenance line in: data drifts, prompts need tuning, someone owns it. A case with $0 run cost is a fiction finance will catch.
Build cost vs. benefit: where the first five agents land
Not every workflow makes a clean case. The ones that do share a profile: high frequency, document-heavy, low ambiguity. Here's a rough sort.
| Workflow | Build effort | Annual benefit | Payback |
|---|---|---|---|
| Supplier-doc lookup | Medium | High | 6–12 mo |
| Order/quote hygiene | Medium | High (error cost) | 4–9 mo |
| Ops/QBR prep | Low–Med | Medium | 6–10 mo |
| Order-status triage | Medium | Medium | 8–14 mo |
| Demand/inventory Q&A | Medium–High | Medium | 10–18 mo |
Start where build effort is low and benefit is high. Order hygiene usually wins on payback because it attacks error cost, which is large and ignored. The grand "AI platform" case — the one with no single workflow and a 24-month horizon — is the one that never gets funded. Don't write it.
The mistakes that sink the case
- No named owner. A business case without a human accountable for the metric reads as a science project. Put a VP's name on it.
- One number with no source. "Saves $500K" with no math behind it gets cut faster than a conservative $80K with a clean trail.
- Ignoring change cost. Training, the adoption ramp, the inevitable "the old way was fine" pushback. Budget a few weeks of friction.
- Bundling. Five agents in one case means five chances to find a weak number. One page, one workflow, one yes.
Closing
A good AI business case template isn't a sales tool — it's a filter. It tells you which agent to build first and gives finance a number they can defend. If you want the math run on your actual workflow, send me one task your team wishes ran itself and I'll build a working agent on it and screen-record the result — a free First 5 Agents teardown, so you see real output before you write the case. Book a call and bring one workflow.
Let's see what's worth building first.
A 15-minute call: tell me where your AI or planning is stuck, and I'll tell you the one thing worth building first — and whether it's worth doing at all.