Insights & Perspectives
Operator-grade perspectives on AI-era institution design. Written for the people running it, not the people writing about it. Grouped by theme so you can find the argument you need.
“When you take AI and accelerate a workflow that shouldn't exist, you don't create transformation. You create faster waste.”
The case for People Operations as the load-bearing function of the AI era. The triptych argument (RADAR / FGKD / CoG) and the essays that anchor it.
The Last Moat: Find. Grow. Keep. Deploy. Rewritten for the AI Era
If the institution is the next great moat, the question is who builds the institution. People Operations is now the load-bearing function, and four verbs that govern the talent lifecycle now mean something different. The anchor essay for the FGKD Diagnostic.
Read article →Where Is Your Gravity?
Every organization has a center of gravity: what the work actually pulls toward, regardless of what the strategy says. The gap between stated strategy and observed gravity is where senior talent reads the company first. The anchor essay for the CoG Diagnostic.
Read article →The CHRO's AI Strategy: What Every Chief People Officer Needs to Know in 2026
HR leaders face a dual mandate: transform the function with AI while managing the workforce disruption AI creates. Here is how to think about both.
Read article →Why Your Team Is Resisting AI (And It's Not What HR Thinks)
Resistance is rarely about the tool. It is your organization's immune system rejecting a perceived threat to identity. Here is how to measure that immune system before the rollout dies.
Read article →Each Function Metabolizes a Different Emotion: The Hidden Substrate of Enterprise AI
Every transformation looks rational on the surface and runs on emotion underneath. Why Legal stalls on closure, HR on identity, Finance on trust, IT on agency, and Operations on belonging, and how to use the map.
Read article →What slows enterprise AI down isn't capability. It's organizational metabolism. Pathologies, archetypes, and the signals that name where the transformation is stuck.
Why Smart Models Drift
A good model did the wrong thing on a job it had done right forty times. Nothing broke. The cause was a blank in the instruction, filled by what looked plausible. Why a smarter model makes this worse, why telling it to be careful does nothing, and the one-line fix: decide the high-stakes choices yourself instead of leaving them to the model. An AI reliability lesson that is also a management one.
The ABCs We Must Learn: AI, BI, and CI
Everyone bought the A. Almost nobody got the value. The reason is the two letters the AI conversation skipped. AI is the intelligence you rent, BI is the structured truth you already built, and Context Intelligence is the new layer that teaches a machine how your business actually works. Why transformation needs all three, why they multiply instead of add, and where to start.
Read article →We Cannot Afford Cognitive Atrophy
Cognitive surrender is the moment. Cognitive atrophy is the slope. As AI handles more of our thinking, the real risk is not one wrong answer but the slow loss of the ability to catch it. What aviation, auditing, and radiology already know about keeping judgment alive under automation.
Read article →What Is Organizational Metabolism?
The binding constraint on AI ROI isn't capability. It's absorption speed. Here's why the fastest metabolizers win, and what the slow ones get wrong.
Read article →Pilot Purgatory: Why 75% of Enterprise AI Pilots Never Reach Production
The gap between proof-of-concept and production is where AI ambitions go to die. Understanding the graduation problem is the first step to solving it.
Read article →AI Transformation Is Not a Technology Problem
Organizations with the best AI infrastructure often have the worst absorption rates. The bottleneck is almost never technical. It's organizational.
Read article →The Four Archetypes of AI Metabolism
Cosmetic, Assisted, Transitional, Adaptive. Where does your organization fall? Each archetype has a different binding constraint and a different critical action.
Read article →How to Measure Enterprise AI Readiness (Without the Usual BS)
Most AI readiness assessments measure capability. The real question is absorption speed. Here is how to measure what actually predicts AI ROI.
Read article →Reimagine vs Automate: Why Most AI Initiatives Pave the Cowpath
Most AI projects accelerate workflows that should not exist. Five signals every transformation must transmit, and the named pathology you fall into when each one is missing.
Read article →Can, or Should: The Second Axis of AI Deployment
Most automation asks only whether a machine can do a task. A framework Google released into the public domain adds a second axis, empathy, to answer whether it should. Why an IP veteran gave it away instead of patenting it.
Read article →What Is Decay Maturity? The Seventh Dimension of Organizational Metabolism
Decay Maturity measures how well your enterprise resists AI reliability erosion over time. The seventh dimension of OMI Enhanced, the four-stage model (Blind, Aware, Instrumented, Self-Healing), and why it is the question most boards have not yet asked.
Read article →The Eighth Conversation: Why Your Enterprise Needs a Chief Reliability Officer for AI
Of the seven C-suite conversations that shape enterprise AI, none owns whether the AI you shipped six months ago still works. The Decay Tax is paid because the eighth conversation has no host. A case for the role and where it should sit.
Read article →The trillion-dollar AI capex cycle, the margin squeeze it forces, and the invisible productivity gap underneath every modern workforce.
Discovery Is the New SEO
There is a file on your website written for an audience you have never met: AI agents. For twenty years it kept crawlers out. Now it tells shopping agents how to buy from you, and Shopify ships it on every store by default. The audience for being found just doubled, and almost everyone is still writing only for the humans. A front-row view of the discovery layer being rebuilt for machines.
The Human Margin: Where White-Collar Jobs Actually Live
The optimists and the doomers share one mistake: both think AI and jobs is a question about tasks. It is a question about two margins. A job lives in the band between the work worth funding and the work a machine can do for less, and that band is moving from both sides. The matched pair to the Margin Thesis.
Read article →The Margin Thesis: Trillions in AI Infrastructure Need Returns
Those returns must come from displacing white-collar labor. Organizations restructuring proactively will thrive. Those waiting will be restructured by the market.
Read article →How to Measure Whether Your Company Is Exposed to the AI Margin Squeeze
The squeeze is not a question. The question is whether you absorb it or get absorbed. Five firm-level factors decide which firms compound through the cycle and which ones get restructured.
Read article →The Human Ether: Why Your Workforce Is 6.3x Less Productive Than You Think
The gap between theoretical capacity and actual output is staggering. Most organizations assume 80% productivity. Reality is closer to 16%.
Read article →How services, agents, and product surfaces are reorganized when AI is the default substrate. The architectural moves the org chart cannot show.
Silent Suffering: Why Your Service Operation Looks Healthy and Isn't
Most internal service operations run on a satisfaction score that only the satisfied answer. The dissatisfaction never reaches the dashboard. It leaks into skepticism, memes, alias rants, and workarounds instead. The TARP iceberg, why a rising score on a falling response rate is the most dangerous reading on the board, and the four-layer telemetry that makes silent suffering visible before it becomes a reorg.
The Operating Model Every Enterprise Services Org Is Missing
Strategy, Transformation, Automation, Content, and Knowledge are not five teams. They are one system. AI agents collapse the tolerance for running them as silos.
Read article →The Agentification Playbook: How to Prioritize 50+ AI Agent Opportunities
When every team wants AI agents, how do you decide what to build first? A scoring framework tested on 83 real agent opportunities at a 200,000-person org.
Read article →The Orchestrator Is a Delivery Manager
An orchestrator in an enterprise agent platform does a delivery manager's job: match the right talent to the opportunity. Agent Cards are resumes, the registry is the bench, evals are reference checks. The whole apparatus ports over. Then two things invert, and the inversion is the thesis.
Read article →Why Your Product Roadmap Looks Healthy but Users Keep Churning
Your launch calendar is busy and retention is sliding. The problem is not pace; it is the mix. Treat features as five medicines and the pattern becomes obvious.
Read article →First-principles notes on building wealth: the math of long-term outcomes, the levers that actually move them, and the difference between the inputs you control and the ones you only hope for.
The Compounding Equation Has Three Variables. You Control Two.
Portfolio growth runs on three engines: the return you earn, the time you stay invested, and the money you keep adding. Most investors obsess over the one they control least. Worked scenarios on which lever actually moves the final number, and when.
Read article →The Formula for Compounding Anything
The math that grows a portfolio grows a life. Where you start matters least. Time sits in the exponent, and it is the one lever you actually control. The short, punchy companion to the worked essay.
Read article →See where your institution stands.
The People Operations triptych: three diagnostics, two essays, one institution. Start with the hub.
Open the People Ops hub