Department / AI + Tech

AI and Technology

Practical governance, adoption, data, architecture, and value questions behind modern technology programs.

What business capability changes if the technology works?

Start with the mechanism

Choose an AI Workflow Before Choosing a Model

Start with a bounded job, its inputs, decisions, exceptions, and controls before comparing model capabilities.

Decision: Document the human workflow and mark the step where assistance could change time or quality.
Watch: A model demo becomes the project while the surrounding work is never defined.

Mechanism index

AI value

Connect adoption to cycle time, error, conversion, risk, or capacity rather than prompts, licenses, and usage alone.

9 min read

human review

Design oversight around impact, reversibility, uncertainty, and affected people instead of reviewing every output equally.

9 min read

data readiness

Clarify ownership, definitions, access, quality, and permitted use before promising an intelligent feature.

9 min read

automation exceptions

Count edge cases, recovery work, and escalation paths before calling a process automated.

9 min read

AI pilot

Use bounded data, users, permissions, outcomes, and exit criteria so experimentation does not become accidental infrastructure.

9 min read

technology lock-in

Expose data portability, workflow dependency, model substitution, and switching effort while negotiating from strength.

9 min read