How to Prioritize AI Use Cases: A Scoring Framework

Score and rank AI use cases by business impact, technical feasibility, data readiness, and time to value. Includes a worked example with 5 real use cases.

Every enterprise I work with has more AI use case ideas than they can execute. The typical list has 15-25 ideas after the first brainstorming session. The mistake most teams make is picking the most exciting one. Or the one the CEO mentioned. Or the one the vendor is pushing. None of these are good selection criteria.

You need a scoring framework. Something that forces objectivity and lets you compare apples to apples. I have used this framework with over 30 enterprise clients. It works because it is simple, it surfaces the right trade-offs, and it prevents teams from chasing shiny objects.

The four dimensions

Score each use case on four dimensions, each rated 1 to 5. Total score is out of 20. Higher is better. Here is what each dimension means and how to score it.

1. Business impact (1-5)

This is the most important dimension. How much revenue will this generate or how much cost will it save annually? Be specific. Not "it will improve efficiency." How many dollars?

• →Score 1: Less than $100K annual impact
• →Score 2: $100K-$500K annual impact
• →Score 3: $500K-$1M annual impact
• →Score 4: $1M-$5M annual impact
• →Score 5: More than $5M annual impact

To calculate this, find out the current cost of the process (people, time, error rates, opportunity cost) and estimate how much AI can reduce that cost. Be conservative. If the vendor says 80% automation, plan for 50%. If the research says 200-400% ROI, use the low end.

2. Technical feasibility (1-5)

Can we actually build this with today's AI capabilities? This covers three things: whether the AI technology exists to solve the problem, how complex the integration with existing systems is, and whether the team has the skills to build and maintain it.

• →Score 1: Requires AI capabilities that don't exist yet or are unreliable
• →Score 2: Technically possible but requires significant research and custom development
• →Score 3: Proven AI approaches exist but integration is complex (5+ system integrations)
• →Score 4: Proven approaches, moderate integration (2-4 systems), team has relevant experience
• →Score 5: Well-understood problem, simple integration, team has built similar systems before

3. Data readiness (1-5)

This is the dimension that kills the most projects. You can have a great use case and proven technology, but if the data is a mess, you will spend 60-70% of your budget on data preparation instead of building the AI system.

• →Score 1: Data does not exist or is inaccessible
• →Score 2: Data exists but is scattered across systems, inconsistent formats, poor quality
• →Score 3: Data exists in 2-3 systems, moderate quality, needs cleaning and normalization
• →Score 4: Data is centralized, good quality, accessible via API, minor cleanup needed
• →Score 5: Clean, labeled, centralized data already available in a usable format

A common trap: the team assumes the data is fine because it exists in a database. Then during the project they discover that 30% of records are incomplete, dates are stored in 7 formats, and the field they need is actually a free-text notes column. Audit the data before you score this dimension.

4. Time to value (1-5)

How fast can this deliver measurable results? Faster is better because it builds organizational confidence, generates data for improvement, and unlocks budget for the next project.

• →Score 1: More than 12 months to measurable results
• →Score 2: 9-12 months to measurable results
• →Score 3: 6-9 months to measurable results
• →Score 4: 3-6 months to measurable results
• →Score 5: Less than 3 months to measurable results

I recommend targeting use cases that can show measurable results within 90 days. That does not mean the project is done in 90 days. It means you can demonstrate progress with real numbers: "We processed 2,000 invoices this month with 93% accuracy and saved 120 hours of manual work." That kind of result builds momentum.

Example: scoring 5 use cases

Let me walk through a real example. This is based on a mid-market insurance company ($2B revenue, 4,000 employees) that had identified these five AI use cases.

Use case A: Invoice processing automation

Business impact: 4 (processing 50,000 invoices/year manually costs $1.8M in labor). Technical feasibility: 5 (document extraction is a solved problem with current AI). Data readiness: 4 (invoices are digital, stored in one system, consistent formats from top 20 vendors). Time to value: 5 (can process first batch within 6 weeks). Total: 18/20.

Use case B: Customer service voice AI

Business impact: 5 (200,000 calls/year at $12/call, voice AI at $5/call = $1.4M savings on 60% automation). Technical feasibility: 4 (proven technology, but requires telephony integration). Data readiness: 3 (call recordings exist but are not transcribed or categorized). Time to value: 4 (first use case live in 8-10 weeks). Total: 16/20.

Use case C: Claims document summarization

Business impact: 4 (adjusters spend 35% of time reading documents, 200 adjusters at $80K average = $5.6M in reading time). Technical feasibility: 4 (RAG-based summarization works well for this). Data readiness: 2 (documents in 4 different systems, mixed formats, no standard taxonomy). Time to value: 3 (6-8 months including data normalization). Total: 13/20.

Use case D: Predictive underwriting

Business impact: 5 (improving loss ratio by 2 points = $40M impact). Technical feasibility: 2 (requires custom ML models, extensive feature engineering, regulatory approval). Data readiness: 2 (10 years of claims data across 3 legacy systems, inconsistent coding). Time to value: 1 (12-18 months minimum). Total: 10/20.

Use case E: Internal knowledge base chatbot

Business impact: 2 (hard to quantify, estimated $200K in saved search time). Technical feasibility: 5 (standard RAG application). Data readiness: 3 (documentation exists but is spread across SharePoint, Confluence, and shared drives). Time to value: 5 (can launch MVP in 4 weeks). Total: 15/20.

Reading the results

The ranking is clear. Invoice processing (18) first, then customer service voice AI (16), then internal knowledge base (15), then claims summarization (13), then predictive underwriting (10) last.

Notice that the most exciting use case (predictive underwriting, $40M potential impact) scores lowest. The business impact is enormous but the technical feasibility, data readiness, and time to value are all poor. Starting with this project would mean 12-18 months of work before you can show any results. Meanwhile the invoice processing project could be delivering value in 6 weeks.

That is the whole point of the framework. It prevents you from chasing the biggest number while ignoring the difficulty of getting there.

Common mistakes when prioritizing

I see the same mistakes in almost every prioritization exercise.

Picking the CEO's pet project regardless of the score. This happens a lot. The framework only works if leadership commits to following it. If the CEO overrides the ranking, the exercise was pointless.

Overestimating data readiness. Every team thinks their data is better than it is. Before you finalize scores, have someone actually look at the data. Query the database. Open the files. Check for completeness, consistency, and accessibility. Two days of data auditing saves months of painful surprises.

Ignoring time to value. A project that takes 12 months to show results is a project that can get canceled 6 times. Budget cycles change. Executives leave. Priorities shift. The faster you can show measurable results, the safer your project is.

Not re-scoring quarterly. The scores change as your capabilities mature. After you deploy your first AI system, your technical feasibility scores go up across the board. Your data readiness may improve too as you build shared infrastructure. Re-run the prioritization every quarter with updated scores.

How to run the exercise

Get the right people in the room. You need business owners who understand the process cost, technical leads who can assess feasibility, and data engineers who know the actual state of the data. Not what the data dictionary says. What the data actually looks like.

Score each use case independently. Do not let the discussion of one use case bias the scoring of another. I usually have each person score silently first, then we discuss the scores and converge on a consensus number.

The whole exercise takes 2-3 hours for 10-15 use cases. At the end you have a ranked list with clear rationale for each score. That is your AI roadmap for the next 6-12 months.

If you want help running this exercise for your organization, that is one of the first things we do in every Dyyota engagement. We bring the framework, facilitate the session, and help you build the business case for the top-ranked use cases.