Insights
Practical thinking from the people doing the work.
Field notes on AI adoption, cloud migration, automation, and modernization — written for leaders who have to make these decisions in production environments, not whiteboard ones.
The Automation Nobody Owns
Automations rarely fail on launch day. They fail eighteen months later, when an upstream screen changes and nobody is quite sure whose problem it is.
Read article →Your AI Needs a Test Set, Not a Demo
A demo tells you an AI system can get the right answer. It can't tell you how often it does, or whether last Thursday's prompt change quietly made it worse.
Read article →Replace It, Rebuild It, or Leave It Alone
The most expensive answer to a legacy system is usually the one that feels most decisive. The second most expensive is doing nothing while calling it a strategy.
Read article →What Breaks After the Migration
Cutover weekend usually goes fine. The trouble arrives around week six, when something fails in a way the runbook doesn't describe and nobody is sure whose pager should have gone off.
Read article →The Process You Think You're Automating
The diagram in your operations manual describes a process nobody actually runs. Automate that one, and the exceptions your team has quietly absorbed for years become the whole project.
Read article →The Data Work Nobody Budgets For
When an AI program stalls, the postmortem almost never blames the model. It blames the data — which everyone knew was messy, and nobody had a line item to fix.
Read article →What AI Actually Returns — and How to Measure It
Most AI ROI numbers are produced after the fact to justify a decision someone already made. The useful ones are set up before the first model call — and they survive a CFO's questions.
Read article →When to Replace RPA With AI Agents — and When Not To
Every vendor with an agent platform is telling you to rip out your bots. Some are right. Most of the time, the answer is more boring — and more profitable.
Read article →Why Most Enterprise AI Pilots Never Reach Production
The honest reasons AI pilots stall — and the operating model that gets them across the line. It's rarely the model that's the problem.
Read article →The Cloud Migration Mistake That Multiplies Your Bill
Lift-and-shift looks cheap until the third invoice arrives. A practical framework for when to rehost, replatform, or refactor — and how FinOps fits in from day one.
Read article →Application Modernization Without Destabilizing the Business
The strangler-fig pattern, phased cutovers, and zero-downtime targets — translated from architecture-blog language into the decisions executives actually have to make.
Read article →How to Choose Your First Five AI Use Cases
A prioritization framework that scores impact, feasibility, and data readiness — and stops you from picking the use case that looks easiest but won't move the business.
Read article →Why Boutique Consulting Wins for AI Transformation
Big firms are optimized for big-firm work. For AI transformation in 2026, the structural advantages have flipped. Here's what that means for how you pick a partner.
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