Generative AI Use Case Discovery: How to Size and Prioritize Opportunities

You’ve probably seen the headlines. Generative AI is everywhere. Your competitors are piloting chatbots, your board wants a strategy deck, and every vendor claims their model will revolutionize your workflow. But here’s the uncomfortable truth: most companies are stuck in "Pilot Purgatory." They have dozens of cool demos but almost no measurable return on investment. Why? Because they started with the technology instead of the problem.

Use case discovery for generative AI isn’t about finding the flashiest application; it’s about systematically identifying where large language models can solve specific, high-value business problems. It requires shifting from "what can AI do?" to "where does our business bleed money or time that AI can actually fix?" If you skip this step, you’re just buying expensive toys.

The Trap of Pilot Purgatory

Let’s look at the data. Stanford’s 2025 AI Index reports that while 78% of organizations used AI in 2024, only a fraction saw real financial impact. McKinsey found that only 39% of respondents attributed any enterprise-level EBIT (earnings before interest and taxes) impact to AI, and for most, that impact was less than 5%. BCG goes further, noting that only 26% of companies have developed the capabilities to move beyond proofs of concept into tangible value.

This gap exists because many leaders treat generative AI as a universal hammer looking for nails. They launch broad initiatives like "improve productivity" without defining what that means. A vague goal like "make support faster" is useless for budgeting. You need to know exactly how many tickets you handle, how long each takes, and what percentage of that time an AI assistant can realistically reduce. Without this granularity, you can’t size the opportunity, and you certainly can’t prioritize it against other strategic bets.

Step One: Map Workflows, Not Just Tech

Start with your end-to-end workflows. Don’t ask your IT team what models they want to test. Ask your operations leads where the bottlenecks are. Look for processes that are:

• High-volume: Repeated thousands of times a month.
• Language-intensive: Involving reading, writing, summarizing, or classifying text.
• Measurable: You already track time, error rates, or conversion metrics.
• Verifiable: A human can quickly check if the AI output is correct.

For example, customer support is a classic starting point. The NBER study "Generative AI at Work" analyzed 5,179 support agents and found that using a conversational assistant increased issues resolved per hour by 14% on average. Crucially, the gain was 34% for novice workers, while experienced agents saw minimal impact. This tells us something vital for sizing: don’t apply a blanket productivity boost across your entire workforce. Segment your users. An AI tool might be transformative for junior staff but marginal for seniors.

Sizing the Opportunity: Beyond Hype

How do you put a number on this potential? Avoid single-point estimates. Instead, build a model with conservative, base, and upside scenarios. The basic formula for labor-saving use cases looks like this:

Annual Value = (Total Transactions × Time Saved per Transaction × Loaded Labor Cost) × Adoption Rate × Realization Factor

Notice the last two variables. Adoption rate accounts for user resistance-will people actually use the tool? Realization factor accounts for the fact that saving time doesn’t always equal cash savings. If an agent saves 20 minutes a day but still has the same workload, you haven’t saved payroll dollars; you’ve released capacity. That capacity is valuable only if you plan to grow volume or cut headcount. Be honest about which one you’re doing.

Also, subtract the costs. Model inference isn’t free. Neither is integration, monitoring, security reviews, or the human time spent verifying outputs. A Harvard Business School study involving 758 BCG consultants showed that while AI helped them complete 12% more work 25% faster, there were limits. On tasks outside the model’s capability frontier, accuracy dropped significantly. If your verification burden is high, your net value shrinks fast.

Prioritization: The Multi-Dimensional Score

Once you have a list of candidates and rough sizes, you need to rank them. A simple "Impact vs. Effort" matrix often fails because it ignores risk and readiness. AWS and Deloitte both recommend a weighted scoring approach. Here’s a practical framework you can adapt:

1. Strategic Alignment (Weight: High): Does this support our top 3 company goals?
2. Net Annual Value (Weight: High): What is the realistic dollar impact after costs?
3. Data Readiness (Weight: Medium-High): Do we have clean, accessible data? Can we legally use it?
4. Technical Feasibility (Weight: Medium): Is the tech mature enough? Do we have the skills?
5. Risk Profile (Weight: Variable): What happens if the AI hallucinates? Is it a minor annoyance or a legal liability?
6. Time to Value (Weight: Medium): Can we pilot and measure results in under 12 weeks?

Don’t forget reusability. A use case that builds a robust retrieval system, evaluation pipeline, or identity management layer for AI adds value to future projects. Even if its direct ROI is modest, its infrastructure value might justify early investment.

Designing a Proof of Concept That Actually Proves Something

Many pilots fail because they’re designed to succeed, not to learn. A good proof of concept (PoC) must have explicit success criteria defined before you start coding. "It works well" is not a criterion. "Reduces average handling time by 10% with a hallucination rate below 2%" is.

Structure your PoC to measure three things:

• Business Impact: Did the metric improve? Use A/B tests or matched cohorts. Self-reported enthusiasm is unreliable.
• Technical Performance: Latency, uptime, cost per transaction, and accuracy against a ground-truth dataset.
• User Experience: Adoption rates, task completion time, and qualitative feedback on trust.

If the pilot doesn’t hit the threshold, kill it or redesign it. Don’t let sunk costs drive scaling decisions. Remember, the goal is to find durable value, not to defend a bad idea.

Governance and Risk: The Silent Killer

NIST released the Generative AI Profile (NIST AI 600-1) in July 2024, highlighting risks like fabrication, bias, privacy leakage, and prompt injection. For high-stakes use cases-like drafting legal contracts or advising customers-you need stronger controls than for low-stakes ones-like brainstorming marketing emails.

Score risk as Probability × Severity. A rare error in a marketing draft is low severity. A frequent error in a medical summary is high severity. Mitigate these risks with retrieval-grounded generation (RAG), human-in-the-loop review steps, and strict access controls. If the cost of a mistake exceeds the benefit of automation, keep the human firmly in charge.

Building a Portfolio, Not Just a Project

Treat your AI initiatives as a portfolio. You need quick wins to build momentum and fund larger transformations. Start with high-frequency, low-risk workflows like internal knowledge search or meeting summaries. These are easy to deploy and measure.

Simultaneously, invest in foundational capabilities: data pipelines, evaluation frameworks, and governance policies. These enable more complex use cases later. As you scale, shift focus from individual apps to platform capabilities. The most successful organizations aren’t those with the best single model; they’re those with the best integration of proprietary data, workflow redesign, and continuous learning loops.

Finally, stay skeptical of vendor hype. The market is moving fast, but the fundamentals of business value haven’t changed. Measure everything. Discount theoretical benefits heavily. And remember: AI is a tool for augmenting human judgment, not replacing accountability.

• Nov, 7 2026
• Collin Pace
• 0
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• Tags:
• generative AI use cases
• opportunity sizing
• AI prioritization
• business value assessment
• AI portfolio management

Written by Collin Pace

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