Lead AI work that you do not build yourself. You make the decisions that Larkfield's leaders really faced, from evidence that the technical courses recorded, and you finish with an AI product and team plan for your own field.
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By the end of this course, you will be able to:
Larkfield’s support team has an AI assistant that drafts replies to customers. Omar, the product manager, wants a cheaper version. The engineers say the new version is “better on average”. A judge model, a strong AI model that grades the replies, preferred its replies 126 times to 48. But the evaluation found that the new version sent no answer at all on two safety tickets. Nadia, the new head of customer operations, must decide, and she cannot read the code. What does she ask for, and how does she decide?
In this course, you sit in the leader’s chair. Every decision is one that Larkfield really faced in the technical courses, with the evidence that those courses recorded. You decide which use cases are worth doing, and whether to build, buy or partner. You plan work when nobody knows the result in advance, and you choose where a person must check the AI’s work. You review an experiment that you could not run yourself, and you launch without hurting customers.
You read tables, short reports and cost estimates, never code. Every number has a label: recorded in an earlier course, a published price, a stated assumption with a range, or a constructed example. You fill in the same templates that the course uses. At the end, you write an AI product and team plan for a use case in your own field.
None. You must be able to read a short report, a table of results and a simple cost estimate, with percentages. You do not write code, and you need no account and no money. You need a browser and a document tool (a word processor or a text editor) to fill in the course's templates.
People who lead AI work but do not build it. Product managers, product owners, founders and business leaders who must choose and launch AI products; engineering managers and tech leads who run AI teams; and domain experts in finance, health, retail, law or any other field who will lead AI work in their own area.
Experienced AI practitioners designed this course. AI helped us draft the content; people chose the teaching approach and the examples, and reviewed every lesson. The outputs that you see are the actual results of running the code. How we make our courses
Decide where AI helps. Start from a problem and the cost of a mistake, sort what AI does well from what it does badly, and estimate the value of a use case as a low, base and high range, with review time counted as a cost.
Decide how to get it. Compare building, buying and partnering, ask vendors the questions that matter, and run a fair pilot with the criteria and the decision date written before it starts.
Plan under uncertainty. Plan AI work as time-limited experiments and stage gates, each with the evidence to pass and the result that stops the work, and define "done" with thresholds on an agreed evaluation set.
Design for people. Choose where a person reviews or approves the AI's work and what that review costs, earn the right level of user trust, and plan adoption that you measure by outcomes, not only by usage.
Build and run an AI team. Map the roles at each stage, hire from the work, review evidence that you cannot produce yourself, and write the team's rules for AI-assisted work.
Lead risk, governance and a responsible launch. Keep a risk register with owners, set approvals in proportion to the risk, and launch in stages with the conditions that pause the feature written in advance.
Three decision case studies with full evidence packs. Write a go/no-go memo for a support reply assistant, recommend whether to build, buy or partner for invoice extraction, and write a 90-day plan to recover a stalled AI project. Complete at least one for the certificate; each one is a portfolio piece.
Complete at least one case study for the certificate. The others are optional. What each one covers
Use every module in one practice project. You collect evidence, and check it against the course outcomes.
Realistic projects from start to finish, on real data, with every output explained. Each one makes a portfolio piece. Complete at least one for the certificate; the others are optional.
Go/no-go memo for a support reply assistant2 hr
Review a proposed AI feature with its pilot evidence (evaluation results, error examples, review time and cost), and write a go, no-go or go-with-conditions memo.
Build, buy or partner for invoice extraction2 hr
Lead the decision as a finance domain expert: compare an in-house build, two vendor products and a partner on recorded trial results, cost ranges and data terms, and write the recommendation.
Recover a stalled AI project2 hr
Diagnose a project that has run for six months without a launch (no definition of done, no evaluation set, no owner for adoption), and write a 90-day recovery plan with stage gates.
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