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Finding & Ranking AI Use Cases · AI Transformation Strategist · Route to AI

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Lessons and outlineLesson 2 of 10

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AI Transformation Strategist

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Lessons

AI Strategy in Plain Words
Finding & Ranking AI Use Cases
The Business Case: Costs, Benefits & Payback
Risk, Rules & Governance
Change Management & Adoption
Pilots, Scaling & the Operating Model
AI Technology Literacy for Leaders
Data Readiness & Company Knowledge
Measuring Value & Reporting to Leaders
Strategy Interviews & Case Practice

On this lesson

In plain English
Try it: Do now, plan, or skip?
Collect ideas from the work
Map the process
Score each idea
Mix quick wins and bigger bets
Check the basics before you commit
Say no with care
Key takeaways
Hands-on exercise
Check yourself

Course0 / 10 done

AI Transformation Strategist

Overall completion 0%

Lessons

AI Strategy in Plain Words
Finding & Ranking AI Use Cases
The Business Case: Costs, Benefits & Payback
Risk, Rules & Governance
Change Management & Adoption
Pilots, Scaling & the Operating Model
AI Technology Literacy for Leaders
Data Readiness & Company Knowledge
Measuring Value & Reporting to Leaders
Strategy Interviews & Case Practice

On this lesson

In plain English
Try it: Do now, plan, or skip?
Collect ideas from the work
Map the process
Score each idea
Mix quick wins and bigger bets
Check the basics before you commit
Say no with care
Key takeaways
Hands-on exercise
Check yourself

AI Transformation StrategistLesson 2

18 min

Finding & Ranking AI Use Cases

Collect ideas from real work, score them fairly and pick a balanced shortlist.

In plain English

Find AI ideas in real work: repeated tasks, long waits and common mistakes. Map the process and apply AI to the one or two steps where it helps. Score each idea on value, feasibility, data readiness and risk. Pick a balanced list of quick wins, core bets and at most one moonshot.

Use case: One specific job you want AI to help with.
Quick win: A low-risk idea that shows results in weeks.
Data readiness: Whether the data is available, clean and allowed to be used.

Picture it

From idea to shortlist

1

Collect ideas from real work

Repeated, slow, error-prone

0 / 10 finished0% · AI Transformation Strategist

↓→

2

Map the process

Find the one or two steps

↓→

3

Score each idea

Value, feasibility, data, risk

↓→

4

Pick a balanced list

Quick wins and core bets

Try it yourself

Do now, plan, or skip?

Sort these ideas by how you would start.

Draft first replies to common support emails.
Let AI approve loans with no person involved.
Summarize long meeting notes.
Predict demand from messy data across five systems.
Send auto-written legal contracts with no review.
Search the staff handbook, with sources.

0 of 6 right

Every organization has more AI ideas than it can fund. The strategist's job is to find the good ones and rank them with clear rules. This lesson shows how to collect ideas, score them fairly and choose a short list that mixes quick wins with bigger bets.

Collect ideas from the work

Do not brainstorm in the dark. Look at real work. Ask each team: what do you do every week that is repetitive? What takes too long? Where do mistakes happen? Where do customers wait?

Run short interviews with team leads and the people who do the work.
Look at ticket logs, email folders and shared drives for repeated tasks.
Ask for the most hated task. It often hides a good use case.
Write each idea as one sentence: who does what, today, and what could change.

Map the process

For the best ideas, draw the process in simple steps. Mark where time is spent, where waits happen and where errors appear. AI is rarely worth applying to the whole process. It usually helps with one or two steps, such as reading a document or drafting a reply.

Score each idea

Use the same score card for every idea. Four questions are enough: how valuable, how feasible, how risky, and how ready the data is.

use-case-scorecard.md

USE CASE: {{one sentence}}
TEAM / OWNER: {{...}}

VALUE (1-5)         How much time, money, quality or risk does this change?   [ ]
FEASIBILITY (1-5)   Can today's AI do this well enough? Is a similar thing done?  [ ]
DATA READINESS (1-5) Is the data available, clean and allowed to be used?     [ ]
RISK (1-5, 5=low)   What if it is wrong? Is a person checking? Any legal limits? [ ]

PRIORITY SCORE = Value + Feasibility + Data readiness + Risk (low risk scores high)

NOTES
- Who is the owner and sponsor?
- What is the one number that would show success?
- Quick win (weeks) or bigger bet (months)?

The scores are for discussion, not for maths. Two people may score differently. The talk that follows is the useful part.

Mix quick wins and bigger bets

Quick wins: low risk, ready data, visible in weeks. They build trust and skill.
Core bets: valuable workflow changes, built over a few months with careful testing.
Moonshots: a few ideas that could change the business. Keep them small until you learn more.

A good short list has two or three quick wins, one or two core bets and at most one moonshot. Too many projects at once spreads people thin.

Check the basics before you commit

Is there an owner who wants this and will use it?
Is the data allowed to be used? Does privacy law or a contract limit it?
Can a person check the result at a reasonable cost?
Is there a number that shows success, and is the baseline known?
Is a simpler fix, such as a better form or a rule, enough?

⚠Beware the idea that sounds exciting but has no owner. A project without an owner usually dies after the demo.

Say no with care

Most ideas will not make the list. Tell people why. Say, this scored low on data readiness, and here is what would change that. People accept a clear no more easily than silence. Keep a parked list, and review it every quarter, because tools and data change.

Key takeaways

✓Find use cases in real work: repeated tasks, long waits and common mistakes. Ask for the most hated task.
✓Map the process and apply AI to the one or two steps where it helps, not the whole process.
✓Score every idea on value, feasibility, data readiness and risk. Use the score to start a discussion.
✓Pick a balanced list of quick wins, core bets and at most one moonshot. Every project needs an owner.

Hands-on exercise

Score five ideas

Collect five AI ideas for a team you know. Write each as one sentence.
Score each idea with the score card, from 1 to 5 on each of the four questions.
Pick two quick wins and one core bet. Write the reason for each choice.
For one idea you rejected, write a kind, clear explanation and what would change your mind.

Check yourself

Q1Why map the process before choosing AI?show answer

AI rarely fits a whole process. Mapping shows the one or two steps, such as reading a document, where it adds the most value.

Q2What four things does the score card measure?show answer

Value, feasibility, data readiness and risk. A low risk scores high.

Q3What does a balanced shortlist look like?show answer

Two or three quick wins, one or two core bets and at most one moonshot, so people are not spread too thin.

Up next

Lesson 3: The Business Case: Costs, Benefits & Payback

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