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.
Tag
7 publications with this tag.
A verification routine across five dimensions — data, risk, integration, people and cost — to separate enthusiasm from readiness.
Wave sequencing, exit criteria per phase, what to centralize and what to federate — and the mistakes that cost a year.
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.
Roles, a risk-based approval matrix, an acceptable use policy and a use-case inventory — the minimum executive governance for scaling without surprises.
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.
Individual licenses create personal gain and organizational loss. What a company forfeits when the knowledge produced with AI dies inside the chat window.
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.
e.works infrastructure on AWS
A managed environment protected by e.works on AWS, with encryption, per-company isolation, backup and high availability.
On-premises, in your environment
The same platform running in your company's data center or private cloud, when data sovereignty requires that nothing leaves your perimeter.
In either model your data stays yours — with access control, audit logging, configurable retention and guaranteed availability.
Newsletter
Analysis on automation, industrial data and technology adoption. No spam.