AI Strategy Practice for Consultants
Run AI discovery with clients and give advice that survives scrutiny and changing tools
A taste of a lesson
A client wants a list of the best AI tools for their business. Should I just give them one?
A tool list is the deliverable most likely to be out of date within months, and it skips the hard question: tools for which tasks? Offer a short discovery stage instead. Find the two or three tasks with the most cost or pain, check data and constraints, then shortlist and test options against those tasks with the client's own examples. You can still name candidates, framed as things to trial. Practise now: write the one sentence you would use to redirect that client without sounding evasive.
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
- Run a structured AI discovery conversation with a client
- Translate client processes and data into a short list of realistic options
- Write recommendations with evidence, cost ranges, risks and prerequisites
- Scope staged engagements instead of open ended transformations
- Handle confidentiality, disclosure and vendor independence properly
Lesson plan
- 1 What clients really ask for Decode common client requests into underlying business problems. Start
- 2 Running discovery Practise a discovery conversation through role play with a demanding client. Start
- 3 From findings to options Turn discovery notes into a prioritised set of options. Start
- 4 Deliverables that age well Structure a deliverable around decisions and methods rather than tools. Start
- 5 Scoping the engagement Design staged engagements with clear outputs at each stage. Start
- 6 Ethics and independence Handle client data, your own AI use and vendor relationships honestly. Start
Try asking
About this tutor
For independent consultants and advisory staff who are asked by clients to help with AI. Clients often want a strategy, a list of tools or a quick win, and the risk is selling generic advice that ages badly. You will practise running a discovery conversation, mapping a client's processes and data to realistic opportunities, framing recommendations as options with evidence and risks, scoping pilot engagements, and being honest about what you do not know. We role play client meetings, critique sample deliverables and discuss ethics: confidentiality of client data in AI tools, disclosure of your own AI use, and staying independent of vendors. You leave with a discovery guide and a deliverable structure you can reuse.
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About the teacher
Helps leaders choose AI work that pays for itself and drop the projects that do not
9 tutors 353 lessons taught Sample
I teach managers and executives how to make sound decisions about AI without needing to become engineers. My background is in strategy and operations work inside companies, where I spent a lot of time turning vague ambitions into projects with a budget, an owner and a way to tell if they worked. I teach with real decisions: a use case...
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