The Practical CTO

Most enterprise AI initiatives follow a familiar arc: an enthusiastic pilot, promising early results, executive buy-in — and then a quiet death somewhere between the sandbox and production. I've watched this happen at large insurance companies, global manufacturers, and mid-size financial services firms alike. The pattern is so consistent that researchers and analysts have given it a name: the PoC trap. What makes this particularly frustrating is that the early results are often genuine. The prototype really does reduce claims processing time by 60%. The document search demo really does surface the right answer in seconds. The AI really is as capable as the team hoped. And yet, twelve to eighteen months later, the project is either quietly shelved or limping along on a skeleton crew, never having reached the users it was supposed to help. In this post, I want to be direct about why this happens, what the data shows, and — more importantly — what the organizations that actually...

Why "Trust But Verify" Is Dead I remember sitting in a post-incident review in 2019, staring at a timeline that showed how an attacker had moved laterally through a corporate network for 47 days before anyone noticed. The entry point was a single compromised VPN credential. Once inside the perimeter, the attacker had free rein — database servers, file shares, internal APIs, all of it accessible with minimal friction. The perimeter model had failed, quietly and completely. That incident crystallized something I'd been seeing across enterprise environments for years: the castle-and-moat security model was not just outdated, it was actively dangerous. The assumption that anything inside the network could be trusted was a liability, not a feature. Zero Trust isn't a product you buy. It's an architectural philosophy that rewires how you think about every access decision, every connection, every user, and every device. This guide is for CTOs and security arc...

Why Most Enterprise AI Deployments Get Retrieval Wrong I've consulted on AI implementations at a dozen enterprise organizations over the past two years, and I keep seeing the same pattern: teams build a demo with an LLM that impresses the executive sponsor, get budget to scale it, and then spend six months dealing with the fallout of a system that confidently generates plausible-sounding answers sourced from nothing in particular. The model hallucinates. It misses recent updates. It can't tell you where its answer came from. And when it fails, it fails invisibly — the user doesn't know they've been given wrong information. The solution isn't a better base model. It's better retrieval architecture. Retrieval-Augmented Generation, done properly at enterprise scale, is the difference between an AI assistant that users can actually trust and one that becomes a liability. This guide covers everything you need to build a production-quality RAG pipeline — from the ...

Photo by ThisIsEngineering on Pexels Photo by fauxels on Pexels I spent a good chunk of 2023 arguing with a VP of Engineering about whether our company "needed DevOps or Platform Engineering." It was the wrong question. By the time we shipped our internal developer platform in early 2024, I understood why: DevOps is a culture , and Platform Engineering is a discipline . Conflating the two is like confusing Agile with Scrum — one is a philosophy, the other is a set of tools and practices you use to live it. In 2026, this distinction matters more than ever. Engineering organizations are larger, cloud bills are more painful, and developer cognitive load has crossed a threshold where good engineers are leaving not because the work is hard, but because the friction is intolerable. Platform Engineering is the answer to that friction. This post is everything I know about making it work. DevOps Told Us What to Value. Platform Engineering Tells Us How. Platform Engineering i...