Most enterprise AI problems are operations problems wearing an AI costume.
An agent harness is the infrastructure that surrounds an AI agent: tools, permissions, memory, guardrails, evaluation, and recovery.
Run an AI readiness assessment in minutes: score six dimensions against the three-stage maturity model (AI Literate, AI Enabled, AI First) and get the specific move that advances you a stage.
Shadow AI is unsanctioned employee AI use, and it maps exactly where official tools fail.
AI governance in five decisions: data boundaries, access, evaluation, spend, and accountability.
Who should own AI internally: a Chief AI Officer, a center of excellence, or federated champions? A decision framework by company stage, plus who owns builds after deployment and how to run AI alongside day jobs.
How to evaluate AI agents in production: golden sets, sampled review, tripwires, and the 8-Minute Check for reviewing eight hours of agent work in eight minutes.
How to control AI and LLM token spend without limiting employee productivity: the Token P&L, per-workflow attribution, runaway session prevention, and when spending limits help versus hurt.
How to make AI work over fragmented data without a massive re-architecture: the retrieval layer pattern, prioritizing data investments by workflow, and building a company brain that captures tacit knowledge.
Hire an AI team or work with an external partner? The honest cost comparison, the three-phase pattern that outperforms both extremes, and the questions that expose a weak AI consultancy in one call.
How to make serious AI commitments while staying portable across model vendors: the swap test, what to standardize versus abstract, multi-model routing, and partnership posture for a moving frontier.