The executive checklist: 30 questions before scaling AI
A verification routine across five dimensions — data, risk, integration, people and cost — to separate enthusiasm from readiness.
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10 publications with this tag.
A verification routine across five dimensions — data, risk, integration, people and cost — to separate enthusiasm from readiness.
Baseline, metrics per use case, efficiency gain versus accumulated asset — and how to avoid endless pilots that never reach production.
A decision matrix by data sensitivity and criticality: total cost, sovereignty, open versus proprietary models, and exit strategy.
How to build an append-only, hash-chained record of AI decisions — with periodic anchoring, auditable correction, and vendor-independent export.
What to record, how long to keep it, and how to answer an auditor, a customer or a regulator without depending on your vendor's goodwill.
How to turn your AI usage policy into executable rules: risk classification, attribute-based authorization, guardrails and CI tests.
Roles, a risk-based approval matrix, an acceptable use policy and a use-case inventory — the minimum executive governance for scaling without surprises.
Legal basis, minimization, anonymization, impact assessments and model-vendor clauses — what changes when AI enters the workflow.
How to capture, version and curate prompts, responses and decisions so every AI use improves the next one — instead of vanishing at the end of the session.
The real path of a corporate prompt: retention, model training, subprocessors and data residency — and the questions that belong in the contract.
Put it to work
Everything we cover here — AI governance, legacy system integration, audit trails and preserved corporate knowledge — is available on the e.works platform at eworks.cloud.
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