Recent Blogs
Most AI marketing setups stop at skills. The four layers of an AI GTM stack, and why evals, the check on what reaches reps, gets built least.
Are industry analysts still relevant when buyers ask AI for a shortlist? Alteryx’s Gerry Van Zandt on AI-drafted RFIs, pay to play, and trust.
Stacie Christensen of H-E-B on why data initiatives fail before technology is the problem, from the easy button to circular data flows and governance.
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.