Practical perspectives on making AI operational
Topics below are examples of our specialties: operating models, governance, platform decisions, adoption, and measurement.
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We'll often share content, white papers and guides to help you evolve your AI strategies and practices
What “AI governance” should look like in practice
How to design controls that protect the business without slowing delivery.
Controlling tool sprawl: platform strategy for GenAI
A decision rubric for selecting tools that fit architecture, cost, and compliance.
Measuring AI: KPIs tied to business outcomes
How to avoid vanity metrics and connect AI activity to performance.
Adoption is the work: enablement for AI initiatives
Training, workflow design, and change management that makes AI stick.
The AI operating cadence: a rhythm teams can sustain
Intake, prioritization, review, measurement — and how to keep it lightweight.
Responsible AI without paralysis
How to set guardrails that accelerate adoption instead of blocking it.
Data readiness for AI: what matters (and what doesn’t)
Pragmatic guidance on data quality, access controls, and operational metadata.
Evaluating AI vendors: questions executives should insist on
Security, privacy, model behavior, IP, and exit strategy — in plain language.
Designing the AI operating model: roles, RACI, and governance
A structure that helps teams ship responsibly — without bureaucracy.
Want a specific topic?
Tell us what you’re wrestling with — governance, platform selection, adoption, measurement — and we’ll suggest a starting point.