AI Use Case Selection
AI use case selection is the discipline of choosing which problems in your business actually deserve AI investment — and which are vanity projects masquerading as innovation. The right framework scores candidate use cases on two axes: business value (revenue lift, cost reduction, risk reduction) and technical feasibility (data availability, model maturity, integration complexity). McKinsey found that 70% of enterprise AI projects fail to deliver value, and the #1 cause is selecting use cases with weak ROI math. The winning portfolio mixes 60-70% near-term efficiency plays (where AI augments existing workflows), 20-30% revenue-generating use cases, and 10% exploratory bets. If you cannot articulate the dollar value of a use case in one sentence, do not fund it.
The Trap
The trap is starting with 'we should use AI' instead of 'we have a problem that AI happens to solve.' Founders read a Bloomberg headline about Klarna replacing 700 agents and immediately commission a chatbot — without checking whether their support volume even justifies the build cost. The second trap is selecting flashy use cases (image generation, autonomous agents) over boring high-value ones (invoice extraction, lead scoring, churn prediction). The boring ones win because the data is clean, the value is measurable, and incumbents already have proven playbooks. Finally, teams confuse 'AI-feasible' with 'AI-valuable' — just because GPT-4 CAN draft your emails does not mean drafting emails is your bottleneck.
What to Do
Run a structured intake every quarter: (1) Have each function submit 3-5 candidate use cases with a 1-page brief — problem, current cost, expected lift, data sources. (2) Score each on Value (1-10) and Feasibility (1-10) using a fixed rubric. (3) Plot on a 2x2 matrix. Fund the top-right quadrant aggressively, pilot the top-left (high value, harder), kill the bottom half. (4) Set a $50K-$150K budget cap on every pilot with a 90-day kill date. (5) Require every funded use case to declare its baseline metric and target lift BEFORE the build starts.
Formula
In Practice
JPMorgan's COIN (Contract Intelligence) platform is the textbook example of disciplined use case selection. Instead of chasing AI moonshots, they targeted commercial loan agreement review — a process that consumed 360,000 lawyer-hours per year. The use case scored high on value (clear $/hour cost), high on feasibility (structured documents, repeatable patterns, abundant labeled examples), and had executive sponsorship. After deployment, COIN reviewed in seconds what previously took 360,000 hours annually. They picked a boring, high-value use case while competitors were still building chatbots.
Pro Tips
- 01
Apply the 'Cost-Per-Decision' lens: count how many times a decision is made per month and the cost of each one. AI is most valuable in high-frequency, medium-stakes decisions (loan approvals, fraud flags, content moderation) — not in low-frequency, high-stakes decisions (M&A, hiring an exec) where humans should stay in the loop.
- 02
Avoid the 'AI tax' on greenfield use cases. If your team has never shipped an ML system, your first project should NOT be a custom RAG pipeline. Start with a vendor tool (Glean, Cresta, Harvey) on a contained workflow, then graduate to custom builds once you've earned the operational chops.
- 03
The best use cases sit on top of a process you've already mapped. If you cannot draw the current workflow on a whiteboard in 5 minutes, AI will not magically fix it — you have a process problem, not an AI problem.
Myth vs Reality
Myth
“If a use case is technically feasible with current models, we should pursue it”
Reality
Feasibility is necessary but not sufficient. The real filter is whether the value of automation exceeds the total cost of ownership — including model inference, data prep, integration, change management, and ongoing maintenance. A feasible use case with a 6-year payback is a worse investment than a boring SaaS subscription.
Myth
“AI use cases should generate new revenue, not cut costs”
Reality
Cost-reduction use cases dominate the actual ROI ledger of enterprise AI. Bain's 2024 survey found that 67% of measured AI value comes from efficiency gains in existing workflows. Revenue-generation use cases are sexier in board decks but have longer payback and higher failure rates. Stack your portfolio toward boring efficiency wins.
Try it
Run the numbers.
Pressure-test the concept against your own knowledge — answer the challenge or try the live scenario.
Knowledge Check
Your CTO wants to invest in three AI initiatives: (A) GenAI sales-call coach scoring 200 daily reps, (B) computer-vision quality inspection on a $400/unit defect, replacing 4 inspectors at $80K each, (C) a custom LLM that drafts internal memos for execs. Which should you fund FIRST?
Industry benchmarks
Is your number good?
Calibrate against real-world tiers. Use these ranges as targets — not absolutes.
Enterprise AI Use Case Outcomes (by value tier at funding time)
Synthesis of McKinsey, Bain, and BCG AI surveys 2023-2024Hard $ baseline + sponsor
~70% deliver measured value
Soft baseline (CSAT, NPS)
~30% deliver measured value
No baseline, strategic narrative only
<10% deliver measured value
Source: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
Real-world cases
Companies that lived this.
Verified narratives with the numbers that prove (or break) the concept.
JPMorgan Chase (COIN)
2017-present
JPMorgan deployed COIN (Contract Intelligence) to extract data from commercial credit agreements. The team rejected sexier use cases (algorithmic trading, GenAI advisors) in favor of a high-volume, structured-document problem with a clean labor baseline. Result: a process that consumed 360,000 lawyer-hours annually now runs in seconds, with higher accuracy than human reviewers on the targeted clauses.
Annual Hours Eliminated
360,000
Document Processing Time
Hours → seconds
Use Case Profile
High-volume, structured, labor-bound
Initial Scope
One contract type, then expand
The best AI use cases are often the most boring ones: high-frequency, well-defined inputs, clear cost baselines. JPMorgan won by being disciplined about what NOT to build.
Hypothetical: Mid-market Insurance Carrier
2024
Hypothetical: A 1,200-person insurance carrier funded six AI projects in parallel after a board AI mandate, with no baseline metrics required. 18 months later, four projects had been quietly shut down (chatbot, claims summarizer, agent recommender, marketing copy generator), one was stuck in pilot purgatory, and one (fraud anomaly detection on a 15-year-old rules engine) delivered $3.2M in prevented payouts. The CIO's lesson: the four killed projects shared one trait — no measurable baseline at funding time.
Projects Funded
6
Projects Killed
4
Successful Project Baseline
$/fraudulent claim, audited
Killed Projects Baseline
None at funding
Portfolio approaches fail when there is no scoring discipline. One use case with a hard baseline outperformed five 'strategic' bets combined.
Decision scenario
The AI Portfolio Allocation
You are CIO of a $400M consumer goods company. The CEO has approved a $1.2M AI budget and wants three projects funded. Your team has surfaced eight candidate use cases ranging from $80K invoice extraction to a $700K demand-forecasting overhaul.
AI Budget
$1.2M
Candidate Use Cases
8
Funded Projects Slots
3
Operational AI Maturity
Low (no production ML yet)
Decision 1
Your top two scored use cases are: (1) Demand forecasting overhaul — $700K, projected $2.4M annual margin lift, but requires a new data pipeline and your team has never shipped ML to production. (2) Invoice extraction with a vendor tool — $90K, projected $400K annual savings, vendor has reference customers. With one slot remaining after these two, do you fund the third based on dollar value alone, or balance the portfolio?
Fund the demand forecasting + invoice extraction + the highest-dollar remaining use case (a $300K marketing personalization project). Maximize upside.Reveal
Fund invoice extraction + a second vendor-tool win (lead scoring, $120K) + the demand forecasting at half-scope (one product line only, $350K). Build operational muscle before betting big.✓ OptimalReveal
Related concepts
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The concepts that orbit this one — each one sharpens the others.
Beyond the concept
Turn AI Use Case Selection into a live operating decision.
Use this concept as the framing layer, then move into a diagnostic if it maps directly to a current bottleneck.
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Turn AI Use Case Selection into a live operating decision.
Use AI Use Case Selection as the framing layer, then move into diagnostics or advisory if this maps directly to a current business bottleneck.