Writing
Essays & Ideas
Thinking out loud about enterprise AI, integration strategy, customer success, and the quiet work of turning complexity into clarity.
AI Will Be Diffused Through Software — Not Vibe-Coded Apps
The next phase of enterprise AI isn't standalone AI apps. It's AI embedded into the software enterprises already run. Context is the delivery mechanism.
Doing vs. Learning: Why You Need to Build Something Real with AI
Watching demos, reading papers, and taking courses is not the same as building something real with AI. The learning gap in enterprise AI is a doing gap.
2026 Prediction: Context Is All You Need
Every AI capability gap I see in enterprise today comes down to one missing ingredient: trusted, real-time context. The organizations that solve context will win the AI decade.
MuleSoft and Relevance in the Agentic Enterprise
As enterprises move toward agentic AI, the question of platform relevance becomes urgent. CARGO — Composability, Actionability, Reusability, Governance, Orchestration — is how I evaluate it.
AI: The Great Equalizer or the Ultimate Amplifier?
The democratization narrative says AI levels the playing field. The amplification argument says it accelerates the already-capable. Both are right — which outcome you get depends on choices you make now.
Ideas Are Cheap. Theses Are Expensive.
Everyone has ideas. Almost no one has done the work to turn an idea into a thesis — a belief held with conviction because you've stress-tested it against reality.
Builders, Operators, and Consumers: The Three Constituents of Agentic AI
Every agentic AI deployment involves three distinct roles — builders who create agents, operators who deploy them, and consumers who use them. Confusing these roles is one of the most common sources of failed AI programs.
7 Learnings From Building a Team in the Post-ChatGPT Era
Two years of building a global team in an environment where AI is changing the skills that matter, the work that gets done, and what good looks like. Here's what I've learned.
The Chef vs. Doctor Paradigm in Customer Advisory
A chef asks what you want and makes it for you. A doctor tells you what you need based on their diagnosis. The best customer-facing professionals know which mode to be in — and when to switch.
People Leadership in the Age of AI
The fundamentals of great people leadership haven't changed. But the context has — and some of what that requires from managers is genuinely new.
Skill Divergence and AI: The Widening Gap
AI is creating a skill divergence unlike anything we've seen in previous technology waves. The gap between those who learn to work with AI and those who don't is compounding faster than most people realize.
Accelerating TTC: Time to Credibility in Every Customer Interaction
In an era where customers have infinite access to information and zero patience for generic interactions, how quickly you earn credibility isn't a nice-to-have. It's the game.
Context and Action: The Two Gaps Slowing Every Enterprise AI Deployment
Enterprise AI deployments stall at two predictable points: they can't get the right context to the AI, and they can't get the AI's output to trigger real actions. Both are integration problems.
Poets and Quants: The Ideal Skillset for the Agentic Era
The professionals who will thrive in the agentic AI era aren't pure technologists or pure humanists. They're the ones who can move fluently between both worlds.
High-Touch Customer Success as We Know It Is Dead
The traditional high-touch customer success model — QBRs, onboarding programs, executive sponsorship — was designed for a different era. It's not sustainable, and AI is making that visible.
ICP vs. ICB: Why Ideal Customer Behavior Drives Expansion
Every GTM team obsesses over ICP — Ideal Customer Profile. Almost none systematically measure ICB — Ideal Customer Behavior. That's where the expansion gap lives.
The Three Things That Actually Drive Enterprise Expansion
After years of studying enterprise expansion patterns, the drivers are more specific than most people realize — and most customer success programs are optimizing for the wrong things.
Confusing Consumption for Adoption: The Biggest Cause of Churn
Adoption and consumption are not the same thing. Conflating them is one of the most common — and expensive — mistakes in enterprise software customer success.
The Operating Model Is the AI Strategy
Most enterprise AI strategies spend 80% of their attention on the technology and 20% on the operating model. That ratio needs to flip. The operating model is where AI programs succeed or fail.
Year-End Reflections: A Framework for Looking Back
I use a simple 4L framework for year-end reflection: Loved, Learned, Longed For, Loathed. It's the most useful annual ritual I've found.
AI: Terminator or Iron Man?
The two dominant narratives about AI in public discourse are both wrong in ways that matter for how enterprises should think about and invest in AI. The real story is somewhere more interesting.
4 Learnings From 4 Years at MuleSoft
Four years into one of the most formative chapters of my career. What I've actually learned — about enterprise platforms, about customer success, and about myself.