Recent Blogs
Clarifeye’s Ken Sanford on the AI context layer nobody names: the tribal knowledge in experts’ heads, who should write your AI skills, and who should own them.
A fresh Claude wrote a better battlecard prompt than my 2025 version. Here’s what AI GTM engineering adds once prompts come cheap, and 3 questions to ask.
Randy Bean on where the chief data officer should report, the 42 percent technology reporting line, and why every CDO from last year’s CDOIQ panel is gone.
The AI slowdown already happened in the enterprise: 56% of CEOs see no AI payoff yet. Why human in the loop keeps AI stuck, and three fixes to start.
Three people counted the same job and got three answers. A working definition of AI GTM engineering, and why the scarce skill is not the engineering.
Executive recruiter Jim Jinright on why interest rates and overhiring, not AI, stalled the tech job market, and why judgment is what companies pay for now.
Intuit Credit Karma’s Veenit Shah and Puneet Singh on data quality at scale: five pillars across 40,000 columns and the AI agent that came last.
Profisee CDO Malcolm Hawker on why AI-ready data depends entirely on the use case, and what a semantic layer can never fix about your customer records.
Claude went down mid-deadline. I switched to Codex and kept working. Why your context and workflows belong in files you own, not in a vendor’s product.
Capital One’s Amy Lenander and Christina Egea on curating data products, building for the hundredth use case, and why adoption is a listening problem.
Qlik CTO Sam Pierson on adaptable AI architecture, routing tasks to models that are 10x cheaper, and why AI token costs are decided before the model runs.
Stop counting em dashes. Unearned transitions show where AI-assisted writing fakes logic and where your draft needs real thinking.