Free playbooks and tools first — then the books, articles, and published pieces.
Practical playbooks on putting AI to work in go-to-market — free to read, download, and install. More are on the way.
AI already audits your product marketing — every buyer question an engine answers is a graded test of your positioning, evidence, and comparison content. This is the method for auditing it yourself first: 19 evidence-gated criteria across five layers, CMMI-style maturity scores, named fix owners, an attribution gate on every outcome claim, and a board-ready budget case. With a free audit skill.
The CMO's practical guide to AI search: 73% of B2B buyers research with AI, and ranking no longer transfers — only ~2.1% of Google top-10 pages appear in ChatGPT's citations. Semantic alignment, answer units, journey query mapping, the technical checklist, and the new measurement stack — with a free 26-check AI-Readiness Scorecard and audit skill.
You already know how to design AI loops — it's called management. The method for non-technical teams: testable goals, written Done-Tests (evals), workers, iteration, and escalation. With a free one-page Loop Design Canvas and a free skill that designs loops with you.
Four free, working Claude Skills for product marketers: impact-first case studies with the claims audit built in, audience-correct release notes, buyer criteria extracted from raw call transcripts, and deal-specific positioning tables. Installable in about a minute; MIT-licensed; distilled from practice with real results behind it.
Sales asks for battle cards, then does not use them. This paper lays out the method I built to replace them: an AI-consumable competitive knowledge base, account-specific positioning generated from call transcripts, and a system that customizes the buying experience for every enterprise deal.
Algorithms Rewiring Democracy — a non-technical book on AI's impact on society and democracy, written through an end-to-end AI writing pipeline I built from my own ideas. I didn't just write a book about AI; I built the machine that helped write it.
The Digital Reformation is a book about what happens to institutions, power structures, and democratic norms when artificial intelligence becomes the infrastructure of information. It is written for a general audience — no technical prerequisites, no assumed familiarity with how the systems work at a code level.
The production process is part of the story. Rather than using AI tools as a writing aid, I designed and built an end-to-end pipeline: a structured system for generating and refining drafts, maintaining argument consistency across chapters, catching contradictions, and managing the revision cycle at a pace that would not have been possible without the infrastructure. The pipeline runs on the same agent architecture as the production systems I built at ActivTrak — applied to a different problem.
Writing a book about AI while building the machine that helped write it is the thesis in practice, not just in argument.
Token rationing, credentialing theater, and the enablement-vs-cost misdiagnosis. Published on LinkedIn with a 2-part audio edition.
Most organizations that claim to be "rolling out AI" are rationing it — restricting model access, limiting tool permissions, or gating usage behind training requirements that have more to do with liability than learning. The AI Cost Rationing series examines what that rationing actually costs, and why the enablement-vs-cost framing is the wrong frame entirely.
The series covers three threads: token rationing (what happens when you restrict the compute budget for AI work), credentialing theater (training programs designed to check a box rather than build capability), and the misdiagnosis of cost as the central variable when adoption is the actual problem.
The 2-part audio edition was produced as a companion to the written series — a format test as much as a publishing decision.
Ground-up AI strategy in a real organization — documented as it happened.
The AI Adoption Case Study series is a first-person account of building an AI program inside a mid-market SaaS company, from the first agent deployed to a ten-agent production network, a 14-module company-wide course, and 50+ discrete AI deliverables. It is not a retrospective; it was documented in real time, which means the uncertainty, the dead ends, and the decisions that looked wrong before they looked right are all in the record.
The series is structured around decision points: what to automate first, how to build the knowledge base, how to get a skeptical sales team to actually use the tools, how to evaluate whether an agent is working. Each installment is self-contained — readable without the prior entries — but the arc builds toward a complete picture of what a ground-up AI strategy looks like when the person building it is also the person responsible for the GTM outcomes it supports.
A byline in People Managing People on redesigning entry-level roles for an AI-shifted workforce.
Published externally in People Managing People, a publication for HR and people-operations leaders. It sits squarely in my domain, workforce analytics and the future of work, and extends the AI-and-org thinking that runs through my writing.
The Product Marketing Alliance recognized my work with its 2024 AI Marketing Innovation Award.
The award recognized a body of production AI built and run by a marketing team in a real organization — roughly ten agents in daily use, custom MCP servers, and RAG knowledge bases doing real revenue work, not a product feature or a proof of concept. The second-brain knowledge system (Obsidian, Claude, and a custom MCP server) is one of those systems, listed in the AI Systems case study.