Startup Founder Essentials — Course 12: AI Enabled Working for Startup Founders

Lesson list

1. What would justify relying on this result?Different parts of an AI-assisted result need different evidence. Clear acceptance criteria connect each part to its intended use and make the remaining judgement visible.
2. Choosing the working arrangement and review effortA suitable arrangement needs the capabilities, evidence access and controls required by the task. Cost comparison becomes meaningful after those requirements and the necessary output quality are met.
3. Setting the decision, assumptions and evidence boundaryA decision brief separates evidence from assumptions, preferences and constraints before AI develops a recommendation. It preserves genuine support while making omissions, alternative explanations and decisive unknowns visible.
4. Checking factual research and source-based answersA citation needs to exist, support the stated claim and fit the intended decision. Close reading can justify a bounded use of research while revealing where an apparently sourced recommendation goes beyond its evidence.
5. Checking calculations and generated artefactsReproducible outputs can be tested against defined inputs and requirements. Successful tests justify reliance within their demonstrated scope, while commercial assumptions need separate evidence.
6. Judging recommendations when there is no known answerCommercial recommendations combine evidence with assumptions about causes and future behaviour. Decision quality can be judged through the reasoning, alternatives and bounded commitment even while the outcome remains unknown.
7. Independent review that can disagree for a reasonReview adds assurance when findings can be tested against original evidence and stable criteria. Valid corrections, unsupported criticism and acceptable work require different responses.
8. Controlling iteration and deciding when to stopRevision is useful when it resolves a material defect or supports a better bounded decision. Stable criteria, identifiable versions and explicit resource limits make acceptance, further evidence gathering and stopping distinguishable.
9. Guardrails for information and evidenceInformation controls govern what a workflow may receive, disclose and treat as authoritative. Their usefulness depends on the particular failure they prevent, reveal or contain.
10. Guardrails for actions, authority and recoveryDelegated actions need enforceable limits and evidence of their actual state. Recovery arrangements address what has already happened as well as what should stop next.
11. Evaluating the workflow and the people or models checking itEvaluation examines whether the whole arrangement and its assessors make the required distinctions at an acceptable cost. Missed errors, false rejections and limits of coverage inform a bounded adoption decision.
12. A complete founder working methodA coherent working method connects the intended decision to evidence, checks, permissions and stopping conditions. The business proposal and the AI workflow can warrant different decisions from the same case.