AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 4 of 4)

[Views are my own] Part 4 – Implementation and The Business Case In this series, we’ve covered the "Triad" (Part 1), the "Golden Set" (Part 2), and the "Federated Framework" (Part 3). Now, we answer the hardest question: How do we implement this without

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 3 of 4)

[Views are my own] Part 3 – The Three Pillars of AI Governance Most enterprises are now past the “one AI pilot” phase. You have dozens of teams, dozens of models, and far more prompts than anyone can count. Each team has its own agent, its own prompt file, its own

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 2 of 4)

[Views are my own] Part 2 – Why We Must Evaluate Our Instructions Before We Evaluate AI In Part 1 of this series, we diagnosed the core problem with AI evals: we often evaluate the model's answers before validating our own instructions. We also introduced Step 1: The Governance

AI Governance & Risk Management

Instruction Governance: The Missing Layer of Enterprise AI (Part 1 of 4)

[Views are my own] PART 1 - Stop Blaming the Model – Start Evaluating Your Instructions The Debate: Engineering Rigor vs. Enterprise Reality This article was born from a recent discussion with peers, fellow VPs and Product leaders, on how to best approach AI evaluation. The prevailing view was that we should

Knowledge Management & Curation

The Judgment Economy (Part 4/4): Connections vs. Collections

[Views are my own] In the first three parts of this series, we built the case for a new strategic mandate. We moved from filtering Signal vs. Noise, to creating Information vs. Insight, to building an enterprise-grade framework for Credibility vs. Plausibility. In this final part, we move to

AI Governance & Risk Management

The Judgment Economy (Part 3/4): Credibility vs. Plausibility

This is the central tension for every enterprise. Generic AI is built for plausibility (it sounds correct). Enterprise AI must be built for credibility (it is correct, auditable, and grounded in your business data). This requires a new, essential human function: Level 3: The Trust Broker.

Data-Driven Decision Making

The Judgment Economy (Part 2/4): Information vs. Insight

[Views are my own] In Part 1 of this series, we explored the foundational challenge of our time: separating Signal vs. Noise. We defined the problem of "Polished Emptiness" and introduced the Taste-Maker as the first level of curation – the disciplined judgment required to filter the field.

Human-AI Collaboration

Vibe-Coding Is a Filter, Not a Threat: How to Use It Without Wasting Your Team’s Time.

AI is changing the world fast. Stop focusing on who is using what tool. Focus on why, and double down on the human skills that AI can't replicate: your judgment, your creativity, and your scars. That is the only moat that lasts.

Data-Driven Decision Making

The Judgment Economy (Part 1/4): Signal vs. Noise

We are drowning in information. Generative AI accelerates this, creating a flood of "Polished Emptiness" – plausible-sounding content with no substance. As AI commoditizes generation, the last true scarcity is trust. Value is shifting from creation to curation and judgment.

Human-AI Collaboration

We’ve Reached the Point Where Good Writing Looks Suspicious

A Personal Perspective on AI, "Polished Emptiness," and the Primacy of Thought.

Innovation & Ideation Methods

Your Career Is a Story: Lessons From a 'Syntax Error'

I had the incredible opportunity to speak to a 9th-grade class for their Career Day. Instead of presenting a job title, I chose to share the journey. It was a wonderful exercise in reflection, reminding me that our careers are not ladders, but mosaics we build from curiosity, challenges,

Data-Driven Decision Making

From Data-Informed to Knowledge-Driven

Data-driven is not enough. Data-informed restores judgment but keeps you in the present. Knowledge-driven turns decisions into reusable assets. A simple template and review loop help teams learn faster, cut rework, and scale better choices.

Human-AI Collaboration

The ChatGPT Report and The Enterprise Blind Spot

OpenAI’s ChatGPT usage report is consumer-only, so enterprise signal is skewed. Still clear: AI is a copilot. Writing dominates, most of it editing. Younger users drive volume; older pros drive work. B2B: prioritize decision support, co-creation, embedded copilots, governance.

Data-Driven Decision Making

The Foresight Gap: Why Your Last Success Is Building Your Next Crisis

Success hides fragility. Each growth stage plants the next crisis. The risk isn't debt but unseen debt across tech, culture, innovation, and governance. Make trade-offs visible, manage and pay them down, and build antifragile systems. Fast is fine. Blind is fatal. Track debt, hire for adaptability.

Human-AI Collaboration

PAIR: A Simple Model for AI-Accelerated Apprenticeship

AI should speed work, not erase the training ground. A Seniors-only + AI model boosts short-term output but drains your pipeline and increases risk. Try Senior + Junior + AI: juniors draft with AI, seniors inspect thinking. Treat mentorship as first-class work and measure it. Build great teams. Now.

AI Governance & Risk Management

The Parrot and the Library: Why AI Won't Kill Search (It Will Crown It)

AI will not kill search; it crowns whoever controls a fresh index. Synthesis engines give fast answers but depend on live, trusted crawling for freshness, coverage, and provenance. The future is hybrid: shelves and summaries, with winners owning both discovery and explanation. Clicks fund freshness.

Human-AI Collaboration

Beyond the Dashboard | Principle 11: Turn AI into a Judgment Multiplier

AI is not your strategist; it multiplies your judgment. Automate discovery, keep humans in the decision loop, and treat judgment as the API. Clean hypotheses and consequence paths in, clarity out. Use AI to amplify decisions, not outsource them. Automate discovery, own decisions. Judgment is moat.

Data-Driven Decision Making

Beyond the Dashboard | Principle 10: Build Thinking Systems, Not Reporting Systems

Most teams have great dashboards that report the past, not systems that drive decisions. A thinking system starts with a clear question, ties metrics to actions, supports diagnosis, and treats AI as a partner for speed, not a substitute for judgment. Goal: better decisions, not prettier charts.

Data-Driven Decision Making

Beyond the Dashboard | Principle 9: Reconcile Metric Definitions Before Analysis

Teams don’t argue about numbers; they argue about definitions. Inconsistent metrics like MAU erode trust, stall decisions, and mislead AI at scale. Fix it upstream: build a Metric Dictionary with clear names, sources, formulas, and owners. One name, one definition.

Human-AI Collaboration

From “Content Is King” to “Judgment Is the Crown”: Rethinking Authority in the AI Era

Content isn't king anymore. AI makes endless "good" content; trust and judgment are scarce. Your lived experience, context, and perspective are the differentiator. Use AI as leverage, not a crutch. Earn attention by solving sharp problems for real people and building credibility. Own your voice. Go.

Product Analytics & User Behavior

Beyond the Dashboard | Principle 8: Manage Multi-Product Portfolios Separately

Blended portfolio metrics become Franken-metrics: pretty roll-ups that mask product realities. Treat each product as its own system with distinct health and leading signals. Synthesize across products instead of averaging. AI clarifies only when signals stay separate. Or it polishes the camouflage.

Product Analytics & User Behavior

Beyond the Dashboard | Principle 7: Build Layered Dashboards to Scale Thinking

One-size dashboards are a myth. Build three layers: Outcome for execs (telescope), Driver for teams (levers), Deep Dive for analysts (microscope). AI enriches layers, it doesn’t flatten them. The aim isn’t more charts; it’s scaling clear decisions at the right altitude. Make layers reduce noise. Go!

Product Analytics & User Behavior

Beyond the Dashboard | Principle 6: Know Your Tool Stack’s Boundaries

Your stack holds partial truths. Stop chasing one dashboard. Declare sources of authority, document blind spots, and build smart bridges. AI helps only when boundaries are clear. Orchestrate specialists into a federated system so teams decide faster and argue less. Set escalation for conflicts now.

Data-Driven Decision Making

Beyond the Dashboard | Principle 5: Focus on Adoption, Not Just Delivery

Shipping is overhead; adoption is the asset. In B2B, features stick and removal is costly, so prevent bloat. In B2C, unused features fuel silent churn. AI can surface adoption signals, not define value. Lead by asking: what delivers outcomes, not what shipped. Treat non-adoption as debt. Measure it.

Data-Driven Decision Making

Beyond the Dashboard | Principle 4: Use Frameworks as Filters, not Blueprints

Frameworks focus attention but don’t decide. Used well, they clarify; used poorly, they paralyze. AI multiplies the noise with context-free models. Leadership must choose one lens per decision, declare boundaries, and decide. Tools assist. Judgment creates clarity. Choose focus over complexity now.