Finding AI Use Cases — The TRIPS Framework

Finding AI Use Cases

TL;DR: Organizations claim “lack of use cases” while drowning in opportunities. The TRIPS framework (Time, Repetitiveness, Importance, Pain, Sufficient Data) systematically scores tasks to identify where AI delivers real value — usually in unglamorous optimization, not flashy innovation.

The Use Case Desert Paradox

Surveys cite “lack of use cases” as a top barrier to AI adoption. But the reality is opposite — there are so many use cases that organizations can’t tackle more than a fraction.

The problem isn’t finding use cases. It’s recognizing them.

Two Blocks to Overcome

The Sexy Block

Most valuable AI applications optimize existing work rather than innovate. Organizations fixate on flashy demonstrations while missing substantial value in unglamorous tasks.

What Gets Attention	What Delivers Value
AI-generated video ads	Automated report generation
Chatbots with personality	Data entry automation
Creative content generation	Document processing
Novel product features	Performance metric analysis

Reality: Optimization (doing current tasks faster/cheaper) beats innovation (doing entirely new things) for most organizations starting with AI.

The ROI Block

Traditional ROI calculations fail for rapidly evolving AI. The formula gets stale before implementation completes.

Better approach: Measure change instead.

Change = (new - old) / old

Apply this to quantifiable metrics:

• Time per task
• Leads generated
• Customer satisfaction (NPS)
• Error rates
• Processing volume

Don’t calculate financial returns — calculate capability improvements.

The TRIPS Framework

Score each task across five dimensions (1-5 scale):

Dimension	Question	High Score Means
Time	How long does this take?	Hours/days per instance
Repetitiveness	How often and consistently?	Daily/weekly, predictable pattern
Importance	What’s the economic value?	Directly impacts revenue/costs
Pain	How difficult or unpleasant?	High cognitive load, tedious
Sufficient Data	Do we have examples?	Training data, documented processes

Higher TRIPS average = stronger AI candidate

The Process

Step 1: Decompose Jobs into Tasks

Break job descriptions into 25-100 discrete tasks using agentic AI tools.

Example decomposition (F&B Director):

• Analyze revenue and cost performance metrics
• Create weekly variance reports
• Update menu pricing based on cost changes
• Review supplier contracts
• Train staff on new procedures
• Handle customer complaints
• …

Step 2: Apply TRIPS Scoring

Score each task systematically:

Task	T	R	I	P	S	Avg
Analyze performance metrics	4	5	5	3	4	4.2
Create variance reports	5	5	4	4	4	4.4
Update menu pricing	3	3	5	2	4	3.4
Train staff	4	2	4	3	3	3.2

Step 3: Calculate AI Likelihood

Rate each task 0-10 for current AI capability:

• Can today’s models handle this?
• Is the task well-defined enough?
• Are inputs/outputs structured?

Step 4: Create Weighted Rankings

Final Score = TRIPS Average × AI Likelihood

Example:

• Performance metrics: 4.2 × 8 = 33.6 ✓ Top candidate
• Variance reports: 4.4 × 7 = 30.8 ✓ Strong candidate
• Staff training: 3.2 × 4 = 12.8 — Lower priority

Step 5: Develop Implementation Plans

For top 5 candidates, detail:

• Strategy (why): Business case, expected change
• Tactics (what): Specific AI approach, tools needed
• Execution (how): Integration points, human checkpoints

Real-World Example

Role: Restaurant F&B Director

Top-Ranked Use Case: “Analyze revenue and cost performance metrics”

Criterion	Score	Why
Time	4	Hours weekly
Repetitiveness	5	Every week, same format
Importance	5	Directly impacts profitability
Pain	3	Tedious but manageable
Sufficient Data	4	POS data available
AI Likelihood	8	Well-structured, data-rich

Implementation:

1. API integration with POS system
2. Automated weekly variance analysis
3. Human review checkpoint before action
4. Gradual transition from advisory → autonomous

Critical Success Factors

• Use agentic systems for complex decomposition (Claude Code, not basic chat)
• Format outputs as YAML rather than tables (cheaper, cleaner)
• Start advisory, move autonomous — AI suggests, then executes
• Focus on time liberation — free humans for strategic work

Budget-Conscious Tools

You don’t need expensive infrastructure:

• Minimax M2.7: ~$200/year
• Claude with Projects: $20/month
• Open-source alternatives available

The approach works regardless of specific platform.

Key Takeaways

• “Lack of use cases” is a recognition problem, not a scarcity problem
• Optimization beats innovation for most AI starting points
• TRIPS framework: Time, Repetitiveness, Importance, Pain, Sufficient Data
• Score tasks, weight by AI likelihood, prioritize top 5
• Measure change, not traditional ROI

Related

• automation/ai-enablement-levels — Where you are on the AI maturity curve
• automation/ai-agent-organization — Organizing agents for reliability
• glossary/llm-evals — Measuring whether AI is actually working

Sources

• Terraforming the AI Use Case Desert — Almost Timely / Christopher S. Penn (March 2026)