
## Quick Navigation

- [Before we start — why good AI ideas are rare](#before-we-start)
- [Why most idea lists fail](#idea-lists-fail)
- [What makes a strong AI use case](#strong-use-case)
- [The task-fit test](#task-fit-test)
- [The prioritisation matrix](#prioritisation-matrix)
- [Quick wins vs strategic bets](#quick-wins)
- [Portfolio balance](#portfolio-balance)
- [From shortlist to pilot](#shortlist-to-pilot)
- [What to say no to](#what-to-say-no-to)
- [The use case funnel in one diagram](#final-mental-model)
- [Cheat sheet](#concept-reference-card)

---

<a id="before-we-start"></a>

## Before we start — why good AI ideas are rare

When people first encounter AI seriously, they tend to overestimate how many things should be turned into AI projects.

Every meeting becomes a brainstorm. Every friction point sounds like a use case. Every executive wants a quick win. Very quickly the organisation has twenty ideas, no shared criteria, and no real sense of which ones are strategically meaningful, operationally feasible, or worth the distraction.

That is normal. It is also expensive.

Strong use case discovery is not about generating more ideas. It is about filtering weak ideas early enough that the organisation can put real energy behind the few that matter.

---

<a id="idea-lists-fail"></a>

## Why most idea lists fail

Ask an organisation for AI ideas and you will usually get one of two bad outcomes.

Either you get a list of vague ambitions like "use AI in every department," which is too abstract to act on, or you get a pile of disconnected suggestions with no way to compare them. Both create noise, not direction.

Use case discovery only becomes useful when it is paired with prioritisation. Otherwise the organisation mistakes enthusiasm for a roadmap.

---

### Try it yourself — The idea list audit

Take the current AI ideas in your team and ask of each one:

| Question | Yes / No |
|---|---|
| Is the problem clearly defined? | |
| Is the task something AI is genuinely good at? | |
| Is the needed context or data accessible? | |
| Can adoption be imagined in a real workflow? | |
| Could success be measured in under 90 days? | |

Ideas with too many "No" answers are not priorities yet. They are placeholders.

---

<a id="strong-use-case"></a>

## What makes a strong AI use case

A strong use case usually has five qualities:

| Quality | Why it matters |
|---|---|
| **Clear problem** | It solves a specific pain point, not a fashionable one |
| **Task fit** | The work matches what AI is actually good at |
| **Usable data or context** | The system has something reliable to work with |
| **Adoption path** | Real people can integrate it into their workflow |
| **Measurable outcome** | Success can be defined before building |

This is the discipline of choosing problems that are operationally tractable, not just interesting.

---

<a id="task-fit-test"></a>

## The task-fit test

Before you score value and feasibility, there is a more basic question to ask: **is this even the right kind of task for AI?**

AI tends to be strong when work involves one or more of the following:

- summarising large amounts of information
- drafting or transforming content
- extracting structure from messy inputs
- classifying, routing, or prioritising items
- pattern recognition with human review
- assisting people through repeated decision preparation

It tends to be weak when the task requires:

- final authority with significant consequence
- precise guaranteed accuracy with no review
- stable deterministic logic that normal software handles better
- deep context the system cannot reliably access
- tacit judgment that is hard to formalise and hard to supervise

This matters because many bad AI projects begin life as poor task matching. No amount of model enthusiasm rescues a use case that fundamentally wants a database, a workflow rule engine, or a human conversation rather than an AI system.

---

<a id="prioritisation-matrix"></a>

## The prioritisation matrix

The simplest useful prioritisation lens is a 2x2:

- **Value**: How much benefit would success create?
- **Feasibility**: How realistic is delivery given data, workflow, skills, and risk?

Use cases with high value and high feasibility are natural starting points. Low-feasibility ideas are not always bad, but they should be treated as strategic investments rather than easy wins.

```mermaid
flowchart TD
    A["High Value / High Feasibility\nStart here"] --> D["Portfolio"]
    B["High Value / Low Feasibility\nStrategic bets"] --> D
    C["Low Value / High Feasibility\nOnly if they unlock learning"] --> D

    style A stroke:#27ae60
    style B stroke:#f39c12
    style C stroke:#2980b9
```

---

<a id="quick-wins"></a>

## Quick wins vs strategic bets

A healthy AI portfolio needs both.

Quick wins build confidence, teach teams how to work with AI, and create visible momentum. Strategic bets build long-term advantage, but they usually need more groundwork in data, workflows, or governance.

The mistake is choosing only one. Quick wins alone create shallow transformation. Big bets alone create long delays and fragile executive patience.

---

<a id="portfolio-balance"></a>

## Portfolio balance

Good prioritisation is not only about ranking individual ideas. It is also about balancing the portfolio.

You usually want a mix of:

- productivity use cases
- customer or user experience use cases
- operational efficiency use cases
- capability-building use cases that improve future readiness

That balance matters because some use cases create immediate value, while others create the platform and habits that make larger gains possible later.

---

One practical way to think about portfolio balance is to avoid filling it entirely with the same kind of promise.

If every use case is framed as cost reduction, you may underinvest in capability-building and user experience improvements that compound later. If every use case is framed as transformation, you may wait too long for tangible wins. If every use case is low-risk productivity support, you may never build the organisational muscle for bigger system redesign.

Healthy portfolios mix:

- visible quick wins that build confidence
- medium-complexity workflow improvements that build capability
- a smaller number of strategic bets that may reshape how the organisation operates

That sequencing matters. Momentum is easier to sustain when learning and value arrive together.

---

<a id="shortlist-to-pilot"></a>

## From shortlist to pilot

Once a use case is prioritised, the next question is not "build the full thing." It is "what is the smallest credible pilot that tests value, fit, and adoption?"

Pilots should be designed to answer:

- does the use case solve a real problem?
- does the workflow actually work?
- do people trust and use the output?
- can we measure the result credibly?

That is how prioritisation turns into disciplined learning rather than random experimentation.

---

<a id="what-to-say-no-to"></a>

## What to say no to

Prioritisation is mostly the art of refusing attractive distractions.

There are several idea types that should usually be rejected or paused:

- ideas that solve no clearly owned business problem
- ideas where no one will change their behaviour even if the system works
- ideas that depend on data or context the organisation does not control
- ideas that create material governance risk without commensurate value
- ideas whose success criteria are vague enough to make failure impossible to recognise

Saying no here is not a lack of ambition. It is strategic hygiene.

Most AI portfolios improve more from better rejection than from better brainstorming.

---

<a id="final-mental-model"></a>

## The use case funnel in one diagram

```mermaid
flowchart LR
    A["Idea Pool"] --> B["Use Case Definition"]
    B --> C["Value / Feasibility Scoring"]
    C --> D["Prioritised Portfolio"]
    D --> E["Pilot"]
    E --> F["Scale / Stop / Rework"]

    style C stroke:#f39c12
    style D stroke:#27ae60
```

AI strategy becomes practical the moment you move from interesting ideas to a governable portfolio of chosen bets.

---

<a id="concept-reference-card"></a>

## Cheat sheet

| Question | Good default |
|---|---|
| **What should I prioritise first?** | High-value, high-feasibility use cases |
| **What makes a use case strong?** | Problem clarity, task fit, usable context, adoption path, measurable outcome |
| **Do I need only quick wins?** | No, balance quick wins with strategic bets |
| **What should a pilot prove?** | Value, workflow fit, adoption, and measurable outcome |


The hardest AI question is often not how to build, but what to build first. Learn how to find real use cases, assess fit, sequence experiments, and avoid expensive distractions.

AI Use Case Discovery & Prioritization

AI success is not just about tools. It depends on how teams are organised, who owns what, how standards are set, and how delivery, governance, and platform capabilities work together.

AI Operating Model & Team Design

Building an AI strategy that drives real business transformation. Vision, operating models, value frameworks, maturity assessment, and portfolio management. Turn AI potential into measurable outcomes.

AI Use Case Discovery & Prioritization

Related Trainings