AI Use Case Scoring Framework

Overview #

Not all AI opportunities are created equal. This framework helps you systematically evaluate and prioritize AI use cases based on four key factors that predict success and ROI.

The core insight: AI works best when applied to workflows that are frequent, time-consuming, important, and supported by quality data. The more of these factors present, the higher the potential return.

The Four Scoring Factors #

1. Frequency #

Question: How often does this task or workflow occur?

Why it matters: High-frequency tasks offer more opportunities to realize time savings. A 10-minute savings on a daily task equals ~40 hours annually.

2. Effort #

Question: How much time and cognitive load does this task consume?

Why it matters: High-effort tasks create the most friction and often get delayed or done poorly due to time pressure.

3. Importance #

Question: How much does output quality matter? Would a second opinion help?

Why it matters: AI excels at providing "second opinions" and catching errors. High-importance tasks benefit most from this capability.

4. Data Availability #

Question: Do you have quality reference materials to guide the AI?

Why it matters: AI is only as good as the context you provide. "Garbage in, garbage out" still applies.

The Scoring Matrix #

How to Use #

1. List candidate workflows or tasks
2. Score each on the four factors (0-3 scale)
3. Calculate total score (max 12)
4. Prioritize highest-scoring opportunities

Sample Scoring #

Interpretation Guide #

Data Quality Deep Dive #

The data availability factor often determines success or failure. Here's how to assess it:

What "Good Data" Looks Like #

For Content Generation:

• Brand voice guidelines
• Past examples of successful outputs
• Style guides and templates
• Audience personas

For Analysis Tasks:

• Structured data in consistent formats
• Historical data for comparison
• Clear definitions of key metrics
• Documentation of business rules

For Process Automation:

• Documented workflows
• Clear decision criteria
• Exception handling rules
• Integration points identified

Improving Data Quality #

If a high-potential use case scores low on data:

1. Document existing knowledge - Interview experts, capture tribal knowledge
2. Create templates - Standardize formats for common outputs
3. Gather examples - Collect "gold standard" samples
4. Define criteria - Establish clear quality benchmarks

Building the Business Case #

Once you've identified high-scoring opportunities, build a business case:

Time Savings Calculation #

Annual Hours Saved =
  (Current Time per Task) × (Frequency per Year) × (Efficiency Gain %)

Example:

• Current time: 30 minutes per meeting summary
• Frequency: 10 meetings per week × 50 weeks = 500/year
• Efficiency gain: 70%

500 × 0.5 hours × 70% = 175 hours saved annually

Cost Calculation #

Annual Value = Hours Saved × Blended Hourly Rate

Example:

• 175 hours × $150/hour = $26,250 annual value

Quality Factors #

Beyond time savings, consider:

• Error reduction - Fewer mistakes requiring rework
• Consistency - More uniform outputs across team
• Speed to market - Faster turnaround on deliverables
• Employee satisfaction - Less tedious work

Common Use Case Categories #

Content & Communication #

• Meeting notes and summaries
• Email drafting and responses
• Proposal and pitch creation
• Social media content
• Internal communications

Analysis & Research #

• Competitive analysis
• Market research synthesis
• Data analysis and visualization
• Report generation

Operations & Workflow #

• Status reporting
• Time tracking and invoicing
• Project documentation
• Process automation

Customer-Facing #

• Customer support responses
• Personalized outreach
• Onboarding materials
• FAQ and help content

Implementation Tips #

Start with Quick Wins #

Choose first implementations that:

• Score 8+ on the framework
• Have clear success metrics
• Involve enthusiastic stakeholders
• Can be demonstrated to others

Build Incrementally #

1. Pilot with one team or workflow
2. Measure actual time savings and quality
3. Refine based on feedback
4. Scale to additional teams/workflows

Document Everything #

Create playbooks that include:

• Prompt templates that work
• Common pitfalls and solutions
• Quality checklist for outputs
• Escalation paths for edge cases

Key Takeaways #

1. Score systematically - Don't rely on gut feel alone
2. Data quality is critical - Invest in context materials
3. Frequency drives ROI - Daily tasks compound savings
4. Start high, learn fast - Begin with best candidates

Related Resources #

• AI Governance Framework
• 3-Step AI Integration Framework

Get Help #

Need assistance scoring and prioritizing AI opportunities for your organization? Schedule a consultation with our team.