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AI Lead: roll out AI in a team or organisation

Step 7 of 7 in AI Lab

Next in your path: 1.8.4 AI Governance Lead: oversight, incidents and accountability

What you'll be able to do
  • Pick AI use cases for a team by value, risk and effort, and say no to the wrong ones.
  • Draft a short, plain-language AI use policy: what data, which tools, who reviews, when to disclose.
  • Build a risk register and match the amount of oversight to the risk.
  • Check an AI tool and plan before the team uses it, with a simple vendor checklist.
  • Teach colleagues with a short training plan and a few champions.
  • Run a small pilot with a baseline, success measures and a go or no-go decision, then report the result honestly.

Before this course: AI Workflows: repeatable systems for yourself or a team (course 1.1.3). You already turned a repeated task into a written workflow with a six-part card, a review checklist, a time-and-quality log, a runbook and a change log. Earlier you learned the basics (course 1.1), research, long documents and the five-point quality gate (course 1.1.2). Those courses made you good at using AI and building one workflow. This course is about leading: helping a whole team or organisation use AI on purpose, safely and with proof that it helps. This is education, not legal or compliance advice. Your organisation's lawyers, privacy officer and security staff decide the rules.

Words in this course 56
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Open the Vocabulary Hall
01

The AI lead role and choosing use cases

In simple words

A coach does not play every position. The coach decides who plays where, what the rules are, and how the team will know it is winning. An AI lead does the same with AI at work. First you decide which jobs are worth giving to AI, and which are not.

Key facts
  • An AI lead here means the person who helps a team adopt AI: choosing use cases, setting rules, training people, and reporting results. It can be a manager, a team member or a volunteer. You do not need to be a programmer.
  • The Government of Canada's guide on generative AI says institutions should assess and mitigate risks before use, start with low-risk uses, and use AI to help employees, not replace them. It also lists "Relevant" among its principles: use AI only where it meets real needs, because it is not the best choice in every case.
  • A use case is a specific job, such as "draft the weekly customer-email summary". "Use AI in marketing" is not a use case.
  • Score each candidate use case from 1 (low) to 3 (high) on four questions. This scoring sheet is a practical suggestion, not an official method:
QuestionScore 3 means
Value: how much time or quality could it gain?Frequent task, takes a long time today
Checkability: can a person easily check the output?Output can be checked against notes, numbers or a list
Data safety: how sensitive is the input?No private or confidential data needed
Effort: how easy is it to start?A good prompt and a short review is enough
  • Start with use cases that score high on all four. Leave out cases with sensitive data and costly, uncheckable mistakes until you have rules, training and results.
  • Ask the people who do the work. They know which tasks are repeated, painful or risky. Involving them also builds trust.
  • Name what AI is not for in your team yet (for example final decisions about a person's job, pay or health).
  • A good AI lead is honest about limits. Saying "we tried it and it did not help" is a useful result.
Step by step
  1. List 10 tasks your team does weekly. Ask at least three colleagues to add theirs.
  2. Write each as one specific job ("turn meeting notes into an action list").
  3. Score each job from 1 to 3 on value, checkability, data safety and effort.
  4. Add the four scores. Circle the top three and the jobs scoring 1 on data safety.
  5. Write one line for each circled job: who does it today, how long it takes, and who would check the AI output.
  6. Write a short "not yet" list for jobs that involve sensitive data or high-stakes decisions.
  7. Check: someone outside the team could read your list and tell why these three jobs come first and why the others wait.
Watch on YouTube
Lesson 2B: The 4D FrameworkAnthropic · YouTube · youtube.com

Videos by other creators on YouTube, for reference. They are not made by or affiliated with The Future Market.

LESSON 01The AI lead role and choosing use cases
LESSON 01 / ANIMATED LESSON / AI VOICE00:00 / 01:00
Read the video script
[Host] "An AI lead does not use AI for everything. An AI lead picks the right jobs."
[Screen: ten tasks with scores for value, checkability, data safety and effort] "I list the tasks, ask the team, and score each one."
[Screen: top three circled, a 'not yet' list beside them] "Three jobs go first. Risky ones wait until we have rules and results."
[Text on screen: Start small, start safe] "Start with low-risk jobs a person can check."
Try it now

Run the scoring sheet on 10 real tasks with at least three colleagues. Choose three use cases and write a "not yet" list.

ClaudeStuck? Copy a ready-made prompt for Claude

Copy this, open Claude, paste it, and Claude will walk you through the lesson step by step.

For a grown-up: Claude is for adults (18+). Copy this, paste it into Claude, and use the answer to explain the lesson to your child.

I'm learning "AI Lead: roll out AI in a team or organisation" on The Future Market. Lesson 1: "The AI lead role and choosing use cases".

What the lesson teaches:
- An AI lead here means the person who helps a team adopt AI: choosing use cases, setting rules, training people, and reporting results. It can be a manager, a team member or a volunteer. You do not need to be a programmer.
- The Government of Canada's guide on generative AI says institutions should assess and mitigate risks before use, start with low-risk uses, and use AI to help employees, not replace them. It also lists "Relevant" among its principles: use AI only where it meets real needs, because it is not the best choice in every case.
- A use case is a specific job, such as "draft the weekly customer-email summary". "Use AI in marketing" is not a use case.
- Score each candidate use case from 1 (low) to 3 (high) on four questions. This scoring sheet is a practical suggestion, not an official method:
- Question - Score 3 means
- Value: how much time or quality could it gain? - Frequent task, takes a long time today
- Checkability: can a person easily check the output? - Output can be checked against notes, numbers or a list

The exercise I'm working on:
Run the scoring sheet on 10 real tasks with at least three colleagues. Choose three use cases and write a "not yet" list.

I'm stuck. Please:
1. Explain the main idea in plain, simple words with one everyday example.
2. Walk me through the exercise one small step at a time. Ask me what I've done so far before giving the next step.
3. Don't just give me the final answer. Help me do it myself, and check my work when I share it.
I'm a parent helping my child (about 8 years old) with a lesson called "The AI lead role and choosing use cases" from the course "AI Lead: roll out AI in a team or organisation".

Here's what the lesson says:
A coach does not play every position. The coach decides who plays where, what the rules are, and how the team will know it is winning. An AI lead does the same with AI at work. First you decide which jobs are worth giving to AI, and which are not.

The activity:
Run the scoring sheet on 10 real tasks with at least three colleagues. Choose three use cases and write a "not yet" list.

Please explain this so an 8-year-old understands: short sentences, a fun everyday example, and no hard words.
Then give me 3 simple questions I can ask my child to check they understood, and a way to do the activity together, one tiny step at a time.
Open Claude
Sources
My notes
02

Write a short AI use policy

This lesson is part of full access: every course and every level for one payment.

03

Risks and a risk register

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04

Check the tool and the plan before the team uses it

This lesson is part of full access: every course and every level for one payment.

05

Train the team and teach others

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06

Run a pilot

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07

Review, scale and keep leading

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++

Final project

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??

Quiz

  1. What does government guidance on AI at work advise about first uses?

  2. Name the six FASTER principles.

  3. Why should every rule in a team AI policy have a reason?

  4. Who should check a team AI policy before it is published?

  5. What are the two scores multiplied in a simple risk register?

  6. What is indirect prompt injection?

  7. What principle limits the damage of a successful injection?

  8. By default, does Anthropic use Team, Enterprise and API inputs and outputs to train models?

  9. What are the four AI Fluency skills in Anthropic's course?

  10. When must the success line for a pilot be written?

  11. What three decisions can follow a pilot review?

  12. What does Anthropic's Usage Policy require for high-risk recommendations?

EX

Final exam

This lesson is part of full access: every course and every level for one payment.

CE

Certificate

This lesson is part of full access: every course and every level for one payment.