New Roles and Responsibilities Around AI
Decide who owns what when AI enters your organisation, from sponsor to reviewer
A taste of a lesson
Our CEO wants to hire a head of AI and make them responsible for everything AI related. Is that a good idea?
A dedicated leader can help, but responsible for everything usually means a bottleneck and an excuse for others to disengage. Department heads still need to own the value and risk of AI in their own processes, and data owners still decide what data may be used. A better brief for the role: set standards, support teams and keep the overall picture. Try this: list three AI uses in your company and write who should be accountable for each one.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- List the responsibilities that AI use creates and who should hold each
- Build a RACI chart with exactly one accountable person per activity
- Compare central, embedded and hub and spoke structures for your context
- Describe how individual roles shift as review and specification work grows
- Avoid bottlenecks, self approval and titles without authority
Lesson plan
- 1 The Jobs Around AI List the responsibilities AI use creates, independent of who holds them today. Start
- 2 One Accountable Person Use a RACI chart to make ownership explicit and spot gaps and overlaps. Start
- 3 Choosing a Structure Compare central, embedded and hub and spoke arrangements for your organisation. Start
- 4 When One Person Wears Many Hats Assign responsibilities safely in a small organisation with few people. Start
- 5 How Everyday Roles Shift Anticipate changes in job content as AI takes on drafting and routine tasks. Start
- 6 Writing It Down and Reviewing It Publish a responsibility map and review it as AI use grows and changes. Start
Try asking
About this tutor
For leaders, managers and people teams working out how responsibilities should change as AI spreads through the organisation. The lessons cover the jobs that need doing around AI (sponsorship, use case ownership, data ownership, risk review, security, evaluation, support and training), central versus embedded team structures, using a simple responsibility chart, how individual roles shift when review work grows, avoiding bottlenecks and title inflation, and writing it all down. It suits small companies where one person wears several hats as well as larger ones setting up a dedicated group. You leave with a draft responsibility map for your own organisation.
Reviews
Students can review a tutor after a paid lesson. Nobody has yet.
About the teacher
The people side of AI at work: change, training, culture and fair practice
9 tutors 407 lessons taught Sample
I teach managers and people teams how to bring AI into a workplace without losing trust. My work background is in people operations and internal communications, so most of my time has gone into the human questions: who feels threatened, who gets credit, what good work looks like now and how to train people who are busy and sceptical. I...
See Saoirse's profile and tutorsMore like this
Other tutors on the same or nearby topics.