Stop Funding AI Ideas by Gut Feel
Most organizations now have more AI ideas than they can fund. Everyone has a favorite. But enthusiasm does not tell you which idea is actually worth the money. If you cannot explain your choice to finance, risk, and the board, it will not survive the next hard question.
 
The evidence backs this up. A 2025 MIT study found that only 5% of custom AI pilots ever reached production with a measurable financial return. A separate 2025 McKinsey survey of nearly 2,000 executives found that just 6% of companies counted as AI "high performers." Adoption is easy. Proving value is rare. This gap is not even new — the book traces it back to the same "productivity paradox" economists first named when computers hit the workplace in the 1980s, and shows what it finally took to close it then.
 
A System You Can Defend
AI Use-Case Economics: The Printable Value–Risk Portfolio Builder gives you one repeatable question to ask about every AI idea, the same way, every time: given what this is worth, how hard it is to build, how much resistance it will meet, and what could go wrong, does it deserve money now, later, or never?
 
This is not a generic AI guide. A full chapter is devoted to the specific, documented reasons AI portfolios fail, from automating a broken process to counting time saved as cash before it actually is. This 83-page printable workbook from Articsledge turns the lessons from that evidence into a complete, working system. You will:
Estimate real financial value, and tell the difference between time saved and cash actually saved
Calculate total cost of ownership, including the hidden costs most teams forget
Score feasibility, adoption friction, and strategic fit on clear, defined scales
Score how risky a project is before and after your safeguards, and know exactly when that leftover risk should block it
Combine every score into one transparent Priority Score, with every formula shown in full and every weight adjustable
Sort each idea into one of nine portfolio categories, from Quick Win to Reject
Design a pilot with real success, failure, and stopping rules, set before launch
Track realized benefits after launch, and decide to scale, redesign, pause, or stop
 
The full system includes seven scoring dimensions (value, cost, feasibility, adoption friction, risk, strategic fit, and evidence confidence), six mandatory decision gates that stop an attractive financial number from overriding an unacceptable risk, and nine portfolio categories with clear entry rules and next actions. Eleven original diagrams walk you through it visually, including a residual-risk lookup grid and a full Value–Risk portfolio matrix. Part VI hands you 21 printable worksheets covering the entire path from your first intake form to your quarterly portfolio review, including a Weighted Priority-Scoring Sheet, a Pilot Charter, a Benefits-Realization Tracker, and an Executive Decision Summary built for a one-page leadership briefing. The closing chapters also set a governance rhythm: a fixed cycle for pilot reviews, full quarterly portfolio reviews, and an annual check on the scoring weights themselves, so the system stays accurate as your evidence improves. A full glossary, source notes, and reference list round it out, so every figure in the book traces back to a named, dated source.
 
Real Evidence, Not Just Theory
Part IV applies the whole method to three real, named organizations: Klarna's customer-service AI rollout, including its widely reported course-correction a year later; DBS Bank's multi-year, enterprise-wide AI program in Singapore; and a Unilever supply-chain pilot run with Walmart Mexico. A fourth section covers a peer-reviewed academic study of generative AI's effect on more than 5,000 customer-support agents. Every case keeps its original limitations intact. Self-reported figures are labeled as such, and nothing here is presented as a guarantee.
 
The book's own terms let you print and copy the worksheets for use inside your organization, and every table is simple enough to rebuild in a spreadsheet if you prefer working digitally. Bring it to your next AI review meeting and start scoring your first batch of ideas the same day.
 
Built for the People Who Decide
This workbook is built for business and finance leaders, risk and governance teams, AI program owners, review committees, and operations or IT leads who all need the same defensible answer to the same question: which AI idea deserves funding next, which one needs more preparation first, and which one should simply be turned down.
 
You already have the ideas. AI Use-Case Economics gives you the system to tell the strong ones from the risky ones, backed by named sources and formulas you control, not hype. Get your copy from Articsledge and score your first use case today.