Insights
Practical notes from real AI deployments. What works, what stalls, and what to do first. No hype.
Adding a human reviewer to an AI workflow feels like governance. In practice, it often becomes a rubber stamp. Here is how to design review steps that actually catch errors.
Choosing the wrong first AI use case is the most expensive mistake mid-market organizations make. Here's how to pick one that actually reaches production.
Off-the-shelf AI promises rarely survive contact with real organizational workflows. Here's what mid-market leaders need to know before signing that contract.
Before you choose a model or write a prompt, you need to know exactly how work moves through your organization today. Most AI projects skip this step and pay for it later.
Most public-sector AI initiatives don't fail in deployment — they fail in procurement. Here's what I've seen on the ground and what agencies can do differently.
A polished AI strategy deck is not an AI strategy. Here's what mid-market leaders get wrong when they confuse planning with execution—and how to fix it in 90 days.
Before you buy another AI tool, you need to understand what your people actually do all day. Here's how mid-market organizations can stop chasing models and start fixing workflows.
A cautionary lesson from a real mid-market engagement: moving fast on AI without mapping your regulatory exposure first can cost you more than the pilot ever saved. Here's what we saw, and what to do instead.
Most organizations stall on AI not because they lack the right model, but because their existing data is siloed, unlabeled, or ungoverned. Here's how to diagnose the real bottleneck before you buy another tool.
Most mid-market organizations think they need a bigger budget or a better model. They don't. They need to look at the data they're already generating every day. Here's how to start there.
Agentic AI systems look impressive in controlled environments. Here's what actually breaks them when real workflows, real data, and real users get involved — and how to fix it before you deploy.
Most mid-market AI deployments stall not because the technology fails, but because organizations skip the workflow design step entirely. Here's what that costs you—and how to fix it.
Most AI pilots succeed in the demo and die in deployment. Here's why the gap exists and the specific disciplines that close it.
The same failure mode I had to design around building a CV parser shows up, at much higher stakes, in how electronic health records move between systems. What silent data loss actually looks like, and the nothing-dropped rule that fixes it.
Building software for places where the internet is a maybe changes how you design everything, not just the sync button. Patterns from real field work, no client names, just what actually holds up.
A year of building with AI doing most of the typing. What it speeds up, what it quietly costs you, and the METR study that explains why it can slow down your best people on your hardest codebases.
Working AI in 30 days and governed production in 90 is a real promise, not a marketing number. Here's exactly what lives inside each window, and what I cut when a client tries to rush it.
NIST AI RMF and ISO 42001 have real, published timelines, and none of them fit in 30 days. Here's what the certification bodies and NIST itself actually say, so you can call out the vendors who are lying to you.
New 2026 CPT codes removed the thresholds that made RPM impractical for small practices. What changed, what it pays, and how to run a program without burning out your staff.
The verified failure numbers behind enterprise AI pilots are brutal, and the reasons are mostly boring. What actually gets working software into governed production.
