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PRACTICAL AI FOR MANAGERS

Practical AI for Future Managers: From Use Cases to Implementation

For people who will be accountable for AI work rather than building it: eight modules that follow one arc from spotting a use case to running it in production. Assumes the ground covered in AI for Non-Technical Teams — this course starts where that one stops.

  • 8 modules
  • 24 lessons
  • 9 quizzes
  • 105 questions
  • About 12 hours

Free — no enrolment needed

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Course syllabus

Eight modules follow a single arc: the manager's operating picture, finding use cases, qualifying them, the business case, build or buy, implementation, governance and risk, and running it. Each module ends with a quiz, and a final assessment covers all eight. Every lesson and every quiz is free.

  1. 1. The Manager's Operating Picture

    What a manager is actually accountable for when AI is involved, how to use these tools competently in your own work, and the four patterns through which AI produces value — plus the one that produces the most and gets funded the least.

  2. 2. Finding Use Cases

    A repeatable method for surfacing AI candidates from the people who do the work, followed by a worked catalogue across every major business function — customer-facing first, then the back-office functions where the returns are usually better and the attention is usually worse.

  3. 3. Qualifying and Prioritising

    Turning a long candidate list into a short ordered one: a qualification screen that kills bad candidates cheaply, a scoring method that survives challenge, and a sequencing logic that builds capability instead of collecting disconnected pilots.

  4. 4. The Business Case

    Modelling the benefit so it survives a finance director, modelling the cost including the parts vendors do not quote and the running economics that are new to AI, and writing a case that can be defended and later checked against reality.

  5. 5. Build, Buy or Assemble

    Three sourcing routes and how to choose between them, a vendor evaluation that survives a good demo, and the contract terms that decide whether you can leave — including who owns the data your usage generates.

  6. 6. Implementation

    Designing a pilot that produces a decision rather than a demonstration, confronting the integration work that is where projects actually die, and earning the adoption that determines whether any of it was worth doing.

  7. 7. Governance and Risk

    An AI policy short enough that people follow it, what the emerging regulation actually requires of an organisation that deploys AI rather than builds it, and the three operational controls that make oversight real: a model inventory, a human-oversight design, and an incident route.

  8. 8. Running It

    What to measure once a system is live and how often to look, how to catch silent degradation and control costs that scale with success, and how to decide between scaling, sustaining and shutting something down.

Source and attribution

Independently written by Srileo Technologies, and vendor-neutral. The cases and figures it cites — including the MD Anderson and IBM Watson Oncology audit, the MIT NANDA GenAI Divide report, and the EU AI Act's compliance dates — are attributed in the lessons to their published sources, with the contested ones flagged as contested so you can judge them yourself.