Before the Prompt™ is the weekly destination for CDOs, VPs of Data, and senior AI strategy leaders navigating the gap between data reality and AI ambition. Built from 12+ years inside Wall Street. No hype. Just hard-won signal.
A thought leadership platform for CDOs, VPs of Data, and senior AI strategy leaders who are tired of AI hype and want the real conversation about what it takes to get data right — before the prompt gets written.
Built from 12+ years inside Goldman Sachs, Morgan Stanley, UBS, and Marsh McLennan, and a 1,000+ member executive community that won't settle for consulting-speak or recycled frameworks.
Weekly signal on AI-ready data, governance, and enterprise strategy. No fluff — just the thinking senior leaders need before their next board conversation.
Read on Substack →Deep conversations with practitioners solving real AI-readiness problems at enterprise scale. No guests paid to be polite.
Coming SoonInvite-only sessions where CDOs and AI startup founders go deep on data foundations. Three seats. Sixty minutes. Zero decks.
Explore →Long-form thinking on the frameworks, failure modes, and patterns behind enterprise AI readiness. Written for practitioners, not consultants.
Read Archive →Five proprietary frameworks and concepts — each built from enterprise practice and coined to name patterns the industry hadn't yet articulated.
Five pillars. Three layers. The enterprise AI readiness architecture spanning Build Governance, Remediate Quality, Institutionalize AI Governance, Drive Change, and Grow AQ + Empower EQ — built from 12+ years inside Wall Street and global insurance.
Explore the Framework →The organizational limbo where AI pilots succeed in demos but never reach production — because nobody fixed the data that sits beneath them. Named. Analyzed. Mapped to root causes.
Read on Substack →Seven pillars for measuring and developing the human capacity to work alongside AI — Curiosity, Critical Thinking, Unlearn-to-Relearn, Data Ethics, Collaborative Intelligence, Decision Agility, Data Storytelling. Beyond data literacy.
Read on Substack →A 4-stage semantic resolution methodology: Collect → Extract → Cluster → Resolve. The structured antidote to AI systems that don't know what "revenue" means — because your enterprise doesn't either.
Read on Substack →The hidden accumulation of undocumented, manual, and brittle processes that silently collapse when AI workloads arrive — before anyone notices. The governance gap nobody tracks until it's too late.
Read on Substack →Senior data leaders and startup founders. One topic. Sixty minutes. No slides. A small, curated series built around the real problems behind AI readiness — how people are actually solving them, what's working, and what's not.
"The conversations that change how you think about a problem don't happen in conference halls. They happen at small tables."Request a Seat →
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Contact Me →Led enterprise data governance and AI strategy across the organization, building frameworks for semantic governance, data quality measurement, and AI readiness assessment. Architected the foundational thinking that became the BRIDG·E framework.
Led data quality programs and credit risk data management, ensuring the accuracy and reliability of risk-critical data across trading and lending operations. Built quality measurement disciplines that tied remediation directly to business outcomes.
Managed product data across the enterprise and led the data incident review process — identifying root causes, driving remediation ownership, and building the operational muscle for continuous data quality improvement.
Delivered regulatory reporting and compliance data programs within one of the world's most demanding financial data ecosystems, where data accuracy carried direct regulatory and reputational consequence.