Three waves of enterprise change — client/server, the web, the cloud — have taught me what actually converts a new technology into business value, and what just looks like it does. The tools have shifted. The discipline of earning returns from them has not.
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Independent and co-published pieces. Mostly on AI and enterprise reinvention; occasionally on other things I can’t stop thinking about.
Each week I publish a short brief on what actually moved — major news, notable research, model and feature releases, and a short observation on what it means for enterprises. Download any week as a PDF to share.
A running record of talks and appearances. For speaking inquiries, please contact fred.brown@pwc.com.
Advising on AI reinvention requires operating at the frontier, not describing it from a distance. I have built a personal AI infrastructure to run my own work — content intelligence, editorial review, foresight, and knowledge management — as a live testing environment for the principles I apply with clients.
A compounding knowledge base built on semantic search, not keyword matching. Every article, research paper, consulting framework, and point of view I’ve read lives here, retrievable by meaning. The longer it runs, the more useful it becomes — each new piece connects to what came before rather than sitting in isolation.
A panel of 13 specialist AI personas that deliberate on a draft before it publishes. Each has a fixed mandate: Strategic Skeptic, Contrarian, Consulting Cliché Detector, AI Writing Detector, C-Suite Reader, and eight others. They review independently, surface tensions, then cross-examine each other before a final synthesis. Nothing I write goes out without running it.
Sends the same prompt simultaneously to eight frontier models and returns responses side-by-side. A built-in Judge anonymizes and ranks them, a Synthesizer builds a composite answer, and a Summarizer runs four independent meta-analyses: consensus, synthesis, devil’s advocate, and executive summary. Useful for evaluating model judgment, not just output.
A weekly foresight pipeline that scans over 80 frontier sources — leading researchers and practitioners on X, Substack newsletters, YouTube channels, arXiv preprints, GitHub trending repositories, HackerNews, and my own consulting document library. All voices actively shaping where AI is going, not reporting where it has been. It applies five reasoning moves to surface patterns 3–6 months before they reach mainstream conversation.
A briefing generated fresh each day from a curated and evolving set of topics shaped by my focus areas and how I engage with it over time. Starred cards carry forward into the next day’s generation. Any card can be saved directly to Open Brain or expanded with a live web search before saving.
A persistent, searchable record of every significant AI conversation I’ve had, organized by topic and project rather than buried in a flat chat log. It pulls structure from Open Brain, pushes promoted entries back into it, and cross-notifies when something relevant surfaces — so work compounds instead of disappearing at the end of a session.
Across thirty-plus years advising large organizations, I have led business and technology reinventions across the TMT sector and beyond.
That foundation is what I bring to AI — not as a technology experiment, but as a business transformation discipline.
Three waves of enterprise change — client/server, the web, the cloud — have taught me what actually converts a new technology into business value, and what just looks like it does. The tools have shifted. The discipline of earning returns from them has not.