The Art of AI Use Case Prioritisation

Most organisations have more AI ideas than they can possibly pursue. The challenge isn't generating opportunities—it's deciding which ones to focus on first. Effective prioritisation is essential for maximising AI value.

Building a Systematic Approach

Start by cataloging potential use cases systematically. Don't rely on whoever shouts loudest. Establish a process for collecting and evaluating AI opportunities from across the organisation.

The Value-Feasibility Matrix

Assess both value and feasibility. High-value opportunities that can't be implemented are just as useless as low-value ones that can. Create a framework that considers both dimensions:

Quadrant	Value	Feasibility	Action
Quick Wins	Medium	High	Start here
Strategic Bets	High	Medium	Plan carefully
Low Priority	Low	Low	Defer or skip
Moonshots	High	Low	Long-term roadmap

Key Prioritisation Principles

1. Consider Learning Value

Your first AI projects will teach your organisation how to do AI well. Choose initiatives that will build capabilities you need for future, higher-value applications.

2. Build Momentum with Quick Wins

Early success creates enthusiasm and support for larger initiatives. Don't start with your most ambitious project.

3. Evaluate Dependencies Carefully

Some use cases require capabilities that don't yet exist:

• Data infrastructure
• Technical skills
• Organisational readiness
• Vendor relationships

Map these dependencies and factor them into your prioritisation.

4. Be Willing to Say No

Not every AI opportunity is worth pursuing. Sometimes the best decision is to focus resources on fewer initiatives that are more likely to succeed.

"Prioritisation isn't a one-time exercise. As you learn more and conditions change, revisit your priorities. The organisations that get AI right are those that continuously refine their focus."