Hook

You've got ten AI use cases on your list. Your executive team wants to know which three to fund. Your head of operations wants predictive maintenance. Your CFO wants cost optimization. Your CISO wants anomaly detection. Everyone has a different priority, and they're all convinced their use case is the most important.

Without a framework, you'll either fund them all (spreading resources too thin), pick the loudest voice (which might not be the best investment), or paralyze yourself trying to make a perfect decision.

Here's the reality: not all AI use cases are created equal. Some have high business impact but require infrastructure you don't have yet. Some are technically straightforward but solve a problem nobody actually cares about. Some are low-risk quick wins that build momentum. Some are ambitious bets that set up your organization for long-term success.

A prioritization framework lets you make defensible, transparent decisions. Your team understands the criteria. Your executives can see the trade-offs. You can communicate why you're funding one project and deferring another.

Purpose

A use case prioritization framework lets you evaluate AI projects consistently, communicate trade-offs transparently, and allocate limited resources to the initiatives that deliver the most value with acceptable risk.

Specifically, the framework should help you:

• Score each use case on multiple dimensions (business impact, technical feasibility, resource requirements, alignment with roadmap)
  - Identify winners and losers: which projects should you fund, which should you defer, which should you kill?
  - Build a portfolio, balance quick wins with strategic bets, short-term ROI with long-term capabilities
  - Explain your decisions, when you defer a project, you can point to the criteria and say "here's why this one ranks lower"
  - Track and reassess, as you build capabilities and complete projects, your scores change. Use the framework to decide when to revisit deferred projects

Why This Matters

Most organizations prioritize AI use cases based on executive preference, loudest voice, or "whoever pitched it best." This creates three problems:

First, you pick the wrong projects. A use case that looks great in a presentation might require infrastructure you don't have, data quality you can't achieve, or skills you can't hire. You don't discover these issues until you're deep into the pilot.

Second, you spread resources too thin. You fund five projects with a 2-person team. Nothing gets done well. All five projects stall. You lose credibility.

Third, you miss the portfolio approach. Great AI organizations mix quick wins (high confidence, near-term ROI), foundation builders (lower ROI but enable future projects), and ambitious bets (high payoff if successful, but risky). If you pick only high-risk, high-reward projects, you'll fail on several and run out of credibility. If you pick only safe, low-value projects, you won't move the needle on business outcomes.

A prioritization framework prevents these failures. It forces you to evaluate trade-offs systematically. It builds credibility because the decision process is transparent.

Core Concepts

Key Insight: The Prioritization Formula

A simple formula for prioritization is:

Priority = (Business Impact × Strategic Alignment) ÷ (Technical Risk × Resource Cost)

This formula captures the key trade-offs:

• Business Impact - What financial value does this deliver? How many customers does it serve? How does it reduce risk?
  - Strategic Alignment - Does this support your roadmap? Does it build capabilities you need for future projects? Does it align with company strategy?
  - Technical Risk - How novel is this? How many unproven components are there? How much technical debt will we accumulate?
  - Resource Cost - How many people do we need? For how long? What infrastructure do we need to build?

Higher impact and strategic value increase priority. Higher risk and resource cost decrease priority.

Note: this isn't a perfect formula. You still need human judgment. But it creates structure around the decision-making process.

Key Insight: How to Score Each Dimension

For each dimension, define a simple rubric:

Business Impact (1-10 scale)

• 9-10: Solves a critical problem affecting the entire organization or a major revenue driver. Expected ROI >$1M annually.
• 7-8: Solves an important problem for a major business unit. Expected ROI $300K-$1M.
• 5-6: Solves a meaningful problem for a function or team. Expected ROI $50K-$300K.
• 3-4: Solves a niche problem. Expected ROI <$50K.
• 1-2: Nice-to-have but not essential. ROI unclear.

Strategic Alignment (1-10 scale)

• 9-10: Directly enables roadmap Phase 3 projects or builds critical infrastructure for multiple future projects. Aligns with company strategy.
• 7-8: Enables Phase 2 projects or builds capability that supports multiple use cases.
• 5-6: Supports current roadmap but isn't a core enabler.
• 3-4: Aligned with strategy but not critical to roadmap.
• 1-2: Tangential to strategy or roadmap.

Technical Risk (1-10 scale, where 10 is high risk)

• 1-2: Straightforward. We've solved similar problems before. Standard tools and patterns. Low innovation risk.
• 3-4: Some novel components but mostly proven patterns. Requires some research but not experimental.
• 5-6: Multiple novel components. Requires significant research or new tools. Moderate risk.
• 7-8: High technical novelty. Unproven approaches. Significant implementation risk.
• 9-10: Cutting-edge technology. Very little precedent. High experimental risk.

Resource Cost (1-10 scale, where 10 is very expensive)

• 1-2: Can use existing tools and infrastructure. <2 FTE for 3 months.
• 3-4: Requires some new infrastructure but mostly existing tools. 2-3 FTE for 3-6 months.
• 5-6: Requires significant infrastructure build or new tools. 3-4 FTE for 6+ months.
• 7-8: Requires major infrastructure build and multiple teams. 4-6 FTE for 6-9 months.
• 9-10: Requires significant new infrastructure, multiple teams, and hiring. 6+ FTE for 9+ months.

Key Insight: Portfolio Balancing

Don't fund all high-priority projects. Build a portfolio:

• 20% of resources on quick wins: High impact, low risk, fast payoff. These build credibility and fund the organization's belief in AI.
  - 40% of resources on strategic initiatives: Medium-to-high impact, moderate risk, alignment with roadmap. These move the needle on business outcomes.
  - 20% of resources on foundation building: Lower near-term ROI but enable multiple future projects. Data infrastructure, governance, team capability.
  - 20% of resources on ambitious bets: High potential payoff, higher risk. These set up future competitive advantage.

This distribution prevents two failure modes: (1) only doing safe projects, which limits impact; (2) only doing risky projects, which leads to multiple failures and loss of credibility.

Key Insight: The Portfolio Matrix

A useful tool is a 2x2 matrix: Business Impact (vertical axis) vs. Technical Feasibility (horizontal axis).

|
High Impact| Strategic Bets | Quick Wins
| (medium-high risk) | (low risk)
|_____________________|___________________
| |
Low Impact | Long Tail | Avoid
| (niche projects) |
|_____________________|___________________
Low Feasibility High Feasibility

• Quick Wins (high impact, high feasibility), Fund these first. They build momentum and credibility.
  - Strategic Bets (high impact, lower feasibility), Fund these second, once you have proven capability.
  - Long Tail (lower impact, lower feasibility), Fund these only if you have excess capacity.
  - Avoid (lower impact, higher feasibility), Only fund if they're stepping stones to bigger projects.

Key Insight: Re-Prioritize as Your Capabilities Grow

Your prioritization today isn't your prioritization in six months. As you build capabilities, infrastructure, and team, projects that seemed risky become feasible. Projects that seemed low-impact become valuable as you understand your business better.

Schedule quarterly reviews of your use case portfolio. Update scores based on:

• Capabilities you've built (infrastructure, team, governance)
  - Learnings from completed projects
  - Changes in business strategy or priorities
  - New opportunities that have emerged

Practical Use Cases

Use Case 1: Prioritizing in a Manufacturing Company

A mid-sized manufacturer has these AI use case ideas:

• Predictive Maintenance, Predict equipment failures before they happen
  - Quality Control (Computer Vision), Detect defects in products using cameras
  - Energy Optimization, Predict and optimize energy consumption across plants
  - Supply Chain Forecasting, Predict demand and optimize inventory
  - Safety Anomaly Detection, Detect unusual safety incidents before they become injuries

Let's score each:

Use Case
Impact
Strategic
Tech Risk
Resource
Score

Predictive Maintenance
9
8
5
6
(9×8)/(5×6) = 2.4

Quality Control (Vision)
8
6
7
8
(8×6)/(7×8) = 0.86

Energy Optimization
7
7
4
5
(7×7)/(4×5) = 2.45

Supply Chain Forecasting
8
9
5
6
(8×9)/(5×6) = 2.4

Safety Anomaly Detection
6
8
3
3
(6×8)/(3×3) = 5.3

Ranking: Safety (5.3) > Energy (2.45) > Predictive Maintenance (2.4) ≈ Supply Chain (2.4) > Vision (0.86)

But wait, the ranking doesn't feel right. Let me add context:

• Safety Anomaly Detection is a quick win. It uses existing monitoring data, requires minimal new infrastructure, and has strong executive support. It's worth doing as a proof of concept.
  - Predictive Maintenance is strategically important but requires significant data collection and cleaning. It's a better Phase 2 project after you've proven capability.
  - Quality Control (Vision) requires computer vision expertise (which you don't have) and significant infrastructure build. Lower priority for now.
  - Energy Optimization and Supply Chain Forecasting both have good scores. You should do both, but stagger them so you don't spread resources too thin.

Decision: Fund Safety Anomaly Detection (3 months, build credibility) and Energy Optimization (6 months, strategic value). Defer Predictive Maintenance to Phase 2, defer Quality Control until you have CV expertise, and defer Supply Chain to Phase 3 after you've optimized energy.

This is a portfolio approach: one quick win + one strategic initiative.

Use Case 2: Re-Prioritizing After a Successful Pilot

An enterprise IT organization deployed IT Anomaly Detection as a quick win in Phase 1. It worked well. They now need to decide what to fund in Phase 2.

Original use cases under consideration:

• Predictive Capacity Planning, Predict infrastructure capacity needs 6 months ahead
  - Security Threat Detection, Detect anomalous security events
  - Cloud Cost Optimization, Recommend cloud resources to shut down or resize
  - Application Performance Prediction, Predict performance degradation before it affects users
  - Chatbot for IT Support, Build an LLM-based chatbot for IT support tickets

Scores (first pass, before Phase 1 learnings):

Use Case
Impact
Strategic
Tech Risk
Resource
Score

Capacity Planning
8
9
5
6
2.4

Security Detection
9
8
6
7
1.7

Cloud Cost Optimization
7
8
4
4
3.5

App Performance
7
7
5
5
1.96

IT Support Chatbot
5
6
6
6
0.83

Ranking: Cloud Cost (3.5) > Capacity Planning (2.4) > App Performance (1.96) > Security (1.7) > Chatbot (0.83)

But now you apply learnings from Phase 1:

• You built excellent anomaly detection infrastructure. Security Detection can leverage that, reducing technical risk from 6 to 3.
• You learned a lot about data quality. Capacity Planning and Cloud Cost both require better data, but you now know how to get it.
• You have one experienced ML engineer. Security Detection requires two. That's a constraint.

Updated scores:

Use Case
Impact
Strategic
Tech Risk
Resource
Score

Capacity Planning
8
9
4
5
3.6

Security Detection
9
8
3
5
4.8

Cloud Cost Optimization
7
8
4
4
3.5

App Performance
7
7
4
4
3.06

IT Support Chatbot
5
6
5
5
1.2

New ranking: Security (4.8) > Capacity Planning (3.6) > Cloud Cost (3.5) > App Performance (3.06) > Chatbot (1.2)

Decision: Fund Security Detection (primary Phase 2 initiative, builds on Phase 1 learning) and Cloud Cost Optimization (quick parallel win). Defer Capacity Planning to Phase 3 after you've improved data quality on Phase 2 projects.

Use Case 3: Evaluating an Ambitious Bet

Your organization has successfully delivered several Phase 2 projects. Your CEO now wants to fund a high-risk, high-reward initiative: "AI-driven autonomous incident response, our infrastructure detects and fixes problems without human intervention."

Scoring:

Dimension
Score
Reasoning

Business Impact
10
If successful, reduces MTTR by 80%, prevents millions in revenue loss. Game-changing.

Strategic Alignment
9
This is a Phase 4 transformation goal. Does it align with roadmap? Yes.

Technical Risk
8
Very high. Autonomous remediation is complex. High stakes (need to avoid breaking things).

Resource Cost
9
Requires multiple teams, new infrastructure, extensive testing. 6+ months.

Priority Score
(10×9)/(8×9) = 1.25
Low score due to high risk and cost.

Recommendation: Don't fund this as your next project, even though the impact is huge. Here's why:

• Too risky for your current capabilities. You haven't proven the ability to maintain complex models in production. Autonomous systems are higher stakes.
  - Long payoff timeline. This project will take 6-9 months with high uncertainty. You'll burn resources and face multiple failures before success.
  - Credibility risk. If this ambitious bet fails, you lose the credibility you've built with Phase 1 and 2 projects.

Instead: Do this in Phase 4, not Phase 2. In the meantime, do Phase 3 projects that build toward this goal:

• Deploy more sophisticated anomaly detection (detects complex problems)
• Build automated remediation for simple issues (low-risk, high-frequency problems)
• Expand the team and capability to handle higher-risk systems

Each Phase 3 project moves you closer to autonomous incident response. By the time you attempt the full system, you'll have the capability to execute it.

Examples

Example 1: Use Case Prioritization Matrix

A healthcare organization lists these AI initiatives:

HIGH IMPACT
|
Strategic Bets |
- Medical Imaging | - Patient Risk Prediction
(low feasibility, | (high feasibility,
high impact) | high impact)
|
| QUICK WINS
_____________________|_________________________
|
Long Tail | Avoid
- Staff Scheduling | - Duplicate Detection
(low feasibility, | (high feasibility,
low impact) | low impact)
|
LOW IMPACT

LOW FEASIBILITY --- HIGH FEASIBILITY

Quick Wins (Fund First):

• Patient Risk Prediction (high impact, feasible with existing data)
• Appointment No-Show Prediction (high impact, low technical risk)

Strategic Bets (Fund After Quick Wins):

• Medical Imaging Analysis (high impact, requires CV expertise you're hiring)
• Cohort Analysis for Clinical Trials (high impact, some new infrastructure needed)

Long Tail (Fund If Resources Available):

• Staff Scheduling (moderate interest, moderate difficulty)

Avoid:

• Duplicate Detection (solvable but low value; focus on higher-impact projects first)

Example 2: Scoring Rubric for AI Use Cases

Business Impact Scoring (1-10)

• Revenue Impact: Will this generate revenue or enable revenue? ($1M+ = 9-10, $100K-$1M = 6-8, <$100K = 1-5)
  - Cost Reduction: Will this reduce operating costs? (>20% reduction = 9-10, 5-20% = 6-8, <5% = 1-5)
  - Risk Mitigation: Will this reduce organizational risk? (Critical risk mitigated = 9-10, Important risk reduced = 6-8, Nice-to-have = 1-5)
  - Strategic Value: Does this support long-term strategy? (Critical = 9-10, Important = 6-8, Tangential = 1-5)

Average these four scores to get Business Impact.

Technical Feasibility Scoring (1-10, where 10 = very feasible)

• Data Availability: Do you have the data needed? (Complete, high quality = 9-10, Partial, needs cleaning = 5-8, Missing or poor quality = 1-4)
  - Technical Complexity: How hard is it to build? (Standard patterns = 9-10, Some novelty = 5-8, Very novel = 1-4)
  - Infrastructure Requirements: Do you have the infrastructure? (Existing infrastructure = 9-10, Need some upgrades = 5-8, Need major build = 1-4)
  - Team Capability: Do you have the skills? (Strong in-house = 9-10, Need to hire = 5-8, Need external partners = 1-4)

Average these four scores to get Technical Feasibility.

Priority = Business Impact × Technical Feasibility

This simpler formula avoids the division and gives equal weight to impact and feasibility. It's easier for non-technical stakeholders to understand.

Anti-Patterns

Anti-Pattern 1: Not Using a Framework, Just Picking Favorites

Your CEO's favorite project gets funded. Your CFO's favorite project gets funded. Everyone else's projects get deferred. This creates politics, damages morale, and often picks the wrong projects.

Instead: Use an explicit framework. Publish the scores. Explain the rationale. When you defer a project, people can see why.

Anti-Pattern 2: Ignoring Portfolio Balance

You fund five high-risk, high-reward projects. All five struggle. You run out of credibility and budget. No projects complete.

Instead: Balance quick wins, strategic initiatives, and ambitious bets. Quick wins build momentum. Strategic initiatives move the needle on outcomes. Ambitious bets set up future advantage.

Anti-Pattern 3: Not Re-Prioritizing as Capabilities Change

You prioritize projects in January based on your current capabilities. By June, you've built new infrastructure and hired new people. You're still doing the projects you prioritized in January, which are no longer the highest-value projects.

Instead: Re-prioritize quarterly. Update scores based on new capabilities. Adjust your portfolio based on learnings.

Anti-Pattern 4: Picking Only Low-Risk Projects

You only do projects with high feasibility and moderate impact. You deliver them, but your organization is no longer making any progress toward transformation. You become a "keep the lights on" function.

Instead: Include some higher-risk projects in your portfolio. They have higher payoff and set up future competitive advantage. Just don't bet everything on them.

Anti-Pattern 5: Not Defining What "Success" Means for Each Project

You pick a project, execute it, and when it's done, you're not sure if it succeeded or not. Did we achieve the expected ROI? Are stakeholders actually using the model?

Instead: For each project, define success metrics before you start. This helps with prioritization (projects with clearer success metrics are higher priority) and helps you know when you're done.

Human Judgment Checkpoints

Checkpoint 1: Does the Prioritization Surprise Anyone?

If your prioritization exactly matches what people expected, you might not be adding value. Look for cases where the framework recommends prioritizing something different than what people assumed. Are there good reasons to override the framework, or is the framework revealing something important?

Checkpoint 2: Are You Being Realistic About Resource Constraints?

You can't do everything. Your prioritization should reflect realistic resource availability. If you're prioritizing three projects but only have capacity for one, you're setting yourself up for failure.

Checkpoint 3: Does the Portfolio Feel Balanced?

Review your prioritization across your entire portfolio. Do you have quick wins, strategic initiatives, and ambitious bets? Or are you skewed toward one category?

Checkpoint 4: Can You Defend Each Prioritization Decision?

For your top three projects, write down why they're prioritized above the alternatives. If you can't articulate a good reason, the prioritization might be wrong.

Checkpoint 5: Are You Revisiting This Quarterly?

Prioritization isn't a one-time exercise. Schedule quarterly reviews where you re-score your use cases and adjust your portfolio based on new information.

Key Takeaways

Use a consistent framework to score use cases on multiple dimensions. Business impact, strategic alignment, technical risk, and resource requirements should all factor into prioritization. Don't rely on executive preference or politics.

Build a balanced portfolio: quick wins, strategic initiatives, foundation building, and ambitious bets. Quick wins build credibility. Strategic initiatives move outcomes. Foundation work enables future projects. Ambitious bets set up competitive advantage. You need all four types.

Use a decision matrix or portfolio quadrant to visualize trade-offs. High impact + high feasibility = quick wins (fund first). High impact + lower feasibility = strategic bets (fund after proving capability). Make trade-offs visible.

Re-prioritize quarterly as your capabilities and priorities change. What's a risky project today becomes feasible after you build infrastructure or hire talent. What's a high-priority project today becomes less important if business strategy shifts.

Define success metrics for each project before you start. This improves your ability to prioritize (projects with clearer success metrics should rank higher) and helps you know when you're done.

Communicate your prioritization transparently. Publish the framework, the scores, and the rationale. When people understand why you're funding one project and deferring another, they're more likely to accept the decision.