The costliest AI failure I encounter in my practice is never a defective algorithm. It is a mathematically perfect model deployed to solve a business problem that simply is not a …
Organizations have zero or one AI policy. They need six. A single “AI policy” that tries to cover governance, acceptable use, content provenance, employee-owned AI tools, safety …
AI policies read like aspirational mission statements. “We commit to transparency.” “We value fairness”. “We believe in responsible AI”. These statements sound responsible. They …
What many organizations call an AI strategy is really just a pile of unrelated AI ideas competing for budget. One team wants a chatbot. Another wants threat detection. Another …
Practical Controls That Keep Predictive Models Reliable After Deployment A credit risk model validated in 2025 during historically low interest rates began producing increasingly …
Most people still think the path into AI is linear. Study the right degree. Get good grades. Read enough papers. Apply to the big companies. Hope for a break. That path still …
The AI Project Management Playbook Most AI projects fail to deliver real value, and the reason is almost never bad algorithms or insufficient data. The reason is that most teams …
AI vendor contracts are still written as if AI were just another SaaS product. That is the core problem. AI vendor contracts raise issues that traditional software terms were never …
Data science project failure and success is largely a function of how effectively and how closely AI strategy, people, processes, and projects are integrated and aligned with the …
How to Get From Sandbox to Production Without Falling at the Final Hurdle Most analytics teams don’t fail because they chose the wrong algorithm. They fail because they built a …