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LEARNING MATERIALS / LEARNING PATH

Lessons and how to use them

Start with lesson 1 and practice the work involved in an enterprise AI project, or choose a lesson for a problem you face now. The focus is working with customers and other teams; production coding needs separate training.

The full course plan allows 72 hours: 3 hours of discussion and 3 hours of practice per lesson. For self-study, read and practice at your own pace.

How to work through the exercises

On your own: Read the explanations and worked examples, follow the exercise steps, then open the self-check answers and review the checklist. Keep the lesson records in one notebook to use in the next lesson. When starting midway, use the lesson’s examples to draft missing records and mark anything unconfirmed.

With a group: Take the roles of a business researcher, an engineer, the customer’s business owner and someone who will use the system. Share each role’s needs and concerns, agree on a next step, then exchange feedback and revisit a step that was unclear or incomplete.

The organizations, people, events and data in the cases were created for practice; they are not real customer records.

The five stages of the course

Follow this sequence to learn and practice. Revisit earlier judgments when findings, risks or permissions change.Swipe to view the full diagram.

PHASE 01

Understand roles and responsibilities

What an FDE does

What does an FDE need to do beyond building the system?

A responsibility note, a delivery plan and a next-step decision with an owner and a condition for reviewing it.

PHASE 02

Understand the work and define the problem

Start working with the customer

What should you agree on at the first meeting?

A one-page agreement covering goals, access, responsibilities and communication.03

Understand the work and the people involved

How do you find out how the work really happens?

A research plan, three sourced observations, an actual workflow and a map of the people involved.04

Define the problem and agree on success

Which problem is worth solving, and when should you stop?

A problem brief, a non-AI alternative, and success and stop criteria.

PHASE 03

Design the solution and manage risk

Design the human–AI workflow

Which steps should AI handle, and how can people review or take over?

A human–AI workflow with handling for success, failure and unknown outcomes, plus a staffed takeover plan.06

Compare options and test key assumptions

What should you test first when time is limited?

An assumption list, a test plan and a note explaining how results determine the next step.07

Manage data, risk and responsibility

Who can decide to pause or restore the system?

A data ownership map, three risk scenarios and conditions for continued operation.

PHASE 04

Evaluate and run a pilot

Evaluate the system and decide whether to release

How do you decide whether the system is ready for users?

A failure list and results by group, including reviewer disagreements, the five areas of evaluation and a release recommendation.09

Help a pilot become part of everyday work

Why do people still avoid the system after launch?

A task-based adoption funnel, workload changes by role and a support plan.10

Handle disagreements and share bad news

How do you explain a problem without making promises you cannot support?

A disagreement note, an incident update and a concrete repair plan.

PHASE 05

Assess value and hand over

Assess value and recommend the next investment

Does saving time justify a larger investment?

A sourced value case with full costs and an investment recommendation tested against changes in key assumptions.12

Hand over and expand the deployment

Can the receiving team run the system after you leave?

A handover rehearsal record, product feedback and a recommendation to expand or exit.