Series / Learning path
Enterprise AX
A field-driven series on moving beyond AI adoption to redesign enterprise work and operating models.
- 01
The Point of AX Is Transformation, Not AI
Why enterprise AX stalls, what practitioners actually need, and why organizational context matters more than another model.
- 02
Why Enterprise AI Transformation Should Start Narrow
General-purpose AI is a Swiss Army knife; enterprise AX is a purpose-built blade. A Market Intelligence case shows why transformation means redesigning work, not automating it.
- 03
The Future of AX: When Domain Experts Become Software Owners
From a Dentist’s AI Stack to the Reinvention of Enterprises and Private Equity
If AI changes the economics of software and technical debt, how might domain expertise, organizational design, mid-market firms, and private-equity value creation change with it?
- 03
Who Disappears First: The AI Team or the Business Teams?
As agents take on more work, which team changes first? A task-level view of how enterprise AI may reshape both builders and business practitioners.
- 03
Do Agents Have to Be Graphs? Why I Am Removing LangGraph from My Default Stack
From prescribing every route to designing the environment and boundaries around an agent
After building a multi-agent research system with LangGraph, I am rethinking where explicit graphs belong as models choose more of their own execution paths.
- 04
Why Our Agent Platform Got Off to a Rocky Start
From a convincing demo to a predictable production system
Our agent platform worked in demos but stumbled in production. A retrospective on predictability, trust debt, and earning abstractions through deployment.
- 05
From Retrieval Engineering to Task Engineering
What changes when retrieval becomes part of an end-to-end task?
AI engineering is moving from optimizing retrieval pipelines to specifying, constraining, and evaluating end-to-end enterprise tasks.