Managing AI Development and Deployment Projects
The 10 Best Practices That Separate AI Projects That Ship From AI Projects That Stall Managing AI development and deployment projects requires practices fundamentally different …
•
25 min read
The 10 Best Practices That Separate AI Projects That Ship From AI Projects That Stall Managing AI development and deployment projects requires practices fundamentally different …
How to Audit AI Systems Beyond Approval and Into Real Operations Most organizations audit AI model approval thoroughly and audit AI model operations barely at all. They verify that …
Practical “You Build It, You Run It” for AI: How to Create End-to-End Ownership Without Burning Out Teams Most AI systems do not break because the first version was badly built.
How to Connect Data, Workflows, and Tools Without Creating More Complexity Than Value Most AI integration efforts fail for a frustrating reason. The AI feature works in isolation, …
From Manual Sampling to Monitoring 100% of Transactions GRC data scattered across disconnected systems. Compliance controls that depend on slow, human-driven processes never built …
Top Risk Scenarios and Controls That Actually Protect Your AI Project A risk register with 15 vaguely worded AI risks and a color-coded heat map is not a taxonomy. It is a …
Incident Types and Direct Loss Categories That Define Real Exposure for AI Projects Here is a question that reveals whether your AI risk program is mature or performative: Can you …
A Practitioner’s Guide to ISO/IEC 25059 A model with 95% accuracy that nobody can explain, nobody can maintain, and nobody trusts is not a quality AI system. It is a liability …
A Practitioner’s Field Guide Most AI threat models are incomplete. Not slightly incomplete. Fundamentally incomplete. Last year I reviewed the threat model for a financial services …
Here’s the Governance Playbook That Actually Holds Up A perfectly validated AI model starts degrading the moment you deploy it. That sentence annoys people. I get it. You want …