AI Strategy for Business Leaders

Decide where AI will pay off in your organisation, prove it with a well-designed pilot, and adopt it with the right data, governance and people in place.

Beginner · AI for Business · about 9.5 hours · 4 modules · certificate on completion

Taught by Divyà Mehtà, Kwamé Asantè, AI Learning Companions.

About this course

Every organisation is being told to do something with AI. This course helps leaders do the right things, in the right order. You'll learn what machine learning and generative AI can and can't do, in plain terms; find use cases in the tasks and decisions of your own organisation and prioritise them by value, feasibility, data readiness and risk; decide whether to buy a product, build on a model through an API or build your own; and design a pilot that gives a clear answer, with an honest calculation of its return. The last module covers what makes AI adoption last: risk management and governance in proportion to the stakes, and the skills, roles and communication that bring people with you. The labs are done on paper and in a spreadsheet, with a sample textile manufacturer or your own organisation, and the course ends with an AI strategy you write for an organisation you know.

What you'll learn

  • Explain in plain terms what machine learning and generative AI can and can't do for a business
  • Find AI use cases in your organisation's tasks and decisions, and prioritise them by value, feasibility, data and risk
  • Decide whether to buy an AI product, build on a model through an API or build your own
  • Design a pilot with a baseline and success criteria, and work out its return on investment honestly
  • Set up governance that fits the risk, and lead the people side of adopting AI

Skills you'll gain

AI strategy, Use-case prioritisation, Data readiness, Build-or-buy decisions, Pilot design, ROI analysis, AI governance, Change management

Tools

Spreadsheet (Excel, Google Sheets or Calc)

Before you start

None: no technical background is needed. You need a spreadsheet program (Excel, Google Sheets or LibreOffice Calc) for two of the labs. It helps to have an organisation in mind: your own, or one you know well.

Modules

  1. What AI can and can't do for a business

    Cut through the hype: what machine learning and generative AI actually do, where they're strong, where they fail, and why an AI strategy starts with business goals rather than technology.

    • AI, machine learning and generative AI in plain terms (10 min)
    • Where AI is strong, and where it fails (12 min)
    • Strategy first: tie AI to business goals (10 min)
    • Hands-on lab: Map AI opportunities to your business goals

  2. Finding and prioritising use cases

    Find candidate uses for AI in tasks and decisions, score them for value, feasibility, data readiness and risk, and check that the data can really support the best ones.

    • Finding use cases in tasks and decisions (10 min)
    • Scoring value, feasibility and risk (12 min)
    • Is your data ready? (12 min)
    • Hands-on lab: Prioritise an AI backlog in a spreadsheet

  3. Build, buy or use an API, and proving the value

    Choose how to get an AI capability, then prove it: a pilot with a baseline and agreed success criteria, and an honest calculation of the return.

    • Build, buy or use an API (12 min)
    • Designing a pilot that gives a clear answer (12 min)
    • Measuring return on investment (12 min)
    • Hands-on lab: Work out a pilot's return

  4. Risk, governance, people and change

    Manage AI's risks with governance in proportion to the stakes, and bring people with you: skills, roles and honest communication decide whether AI is adopted or quietly ignored.

    • The risks of AI in business (12 min)
    • Governance that fits the risk (10 min)
    • People, skills and change (12 min)

Project: Write an AI strategy for an organisation

Write a short AI strategy (three to five pages, or a deck of about ten slides) for an organisation you know, or for the textile manufacturer from the labs. Connect AI to the organisation's goals; choose and justify a small portfolio of use cases; assess data readiness and decide how to get each capability; plan a pilot for the top use case with a baseline, a comparison group, success and stop criteria and an estimate of its return; and set out the risk, governance and people plan that will make adoption safe and lasting. Write for a busy executive team: plain language, clear choices and honest numbers.

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