AI investment & economics
AI business case and ROI
Current decision
Fund
Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first
What changed from the pinned baseline
Baseline $1.91M → current $1.91M. NPV change: $0k.
Upfront investment
$0k
Year 1 discounted flow
$0k
Year 2 discounted flow
$0k
Year 3 discounted flow
$0k
The four contributions sum exactly to the NPV change before display rounding. Each scenario uses its own discount rate. Sensitivity below varies one input at a time; it is not a probability interval.
Cash flows and exact baseline bridge
| Year | Baseline cash flow | Current cash flow | Current discounted flow | NPV contribution change |
|---|---|---|---|---|
| 0 | -$600,000 | -$600,000 | -$600,000 | $0 |
| 1 | $753,333 | $753,333 | $672,619 | $0 |
| 2 | $1,220,000 | $1,220,000 | $972,577 | $0 |
| 3 | $1,220,000 | $1,220,000 | $868,372 | $0 |
@ 12% discount
Break-even discount rate
Undiscounted
Tornado · NPV sensitivity (±30%)
Dashed line = base NPV $1.91M. Widest bar = the driver that most moves the case. Select a row to focus its assumption.
Steering pre read · business case
FundAI initiative, 3-year business case
NPV (range)
$1.03M to $2.80M
IRR
143%
Payback
0.8 yr
Recommendation: Fund. The case stays NPV-positive across the full ±30% sensitivity band. Largest lever: annual value.
If you act on this · the call → expected lift → how you'd measure it
The call
Fund, fund with conditions, or defer based on range, sensitivity, and payback.
Expected lift · illustrative
Improves funding discipline by surfacing the driver that can make or break the case.
How you'd measure it
NPV, IRR, payback, sensitivity driver, adoption progress, realized value vs modeled value.
Present the range, not the point
Steering committee takeaway: Present the range, not only the point. Points get challenged. Ranges with clear assumptions get governed.
How this is built
Cash flows: year 0 = −investment; year t = annual value × average adoption (linear ramp) − run cost, over 3 years. NPV discounts at the chosen rate; IRR solved by bisection; payback interpolated on undiscounted cumulative flow.
Tornado varies each driver ±30% and re computes NPV; bars are sorted by swing and centered on the base NPV. Stack: Next.js (static) + shared design system; client side.
Limitations: this is a portfolio business case artifact. Real funding decisions would require finance validation, benefits ownership, implementation estimates, risk adjustments, and post launch value tracking.
Method, evidence and limitations
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
AI business cases are often fragile because value, adoption, run cost, and implementation effort are uncertain. Funding decisions need to see the range, the payback, and the driver that can break the case.
Approach
The builder calculates NPV, IRR, payback, run cost impact, adoption ramp, and sensitivity. A tornado view shows which assumption creates the largest swing in value.
Why this way
This connects AI funding to financial discipline, value realization, adoption risk, run cost, and executive approval.
The metric
NPV and payback; the widest tornado bar (the driver the case hinges on).
The trade-off
Optimistic value versus conservative adoption and run cost assumptions.
Outcome
A fund/defer decision with the fragility named, not hidden in a point estimate.