Practical thinking on AI in production, custom software and keeping what you already have alive.
You think the big bill is building it. Then you learn that launch is the start, not the finish, and that keeping it alive has its own cost.
No-code tools are great until they aren't. Knowing where that line sits saves you an expensive rebuild down the road.
You ask for a quote on an app and get huge ranges or dodging. They're not trying to trick you. The question, as asked, barely has an answer.
We've all fought with a bot that understood nothing. If you're putting one on your business, it's worth knowing why they fail so often.
Every company with software has one person who knows it all. The day that person actually leaves is when you find out how expensive that comfort was.
You've got a task eating hours and two roads: pay someone to do it or pay to automate it. The right answer depends on things almost nobody works out.
You're about to hire someone to build something important and you can't judge if they're any good. These questions separate the ones who know from the ones selling smoke.
Everyone wants AI. Almost nobody wants to look at the real state of their data, which is exactly what decides whether the AI will work.
Late software is almost a law of physics. Some of it is the builder's fault, and a big chunk is on the client. Here's the uncomfortable truth.
Your developers talk about 'technical debt' and you nod along. Here's what it really means and why it deserves your attention even if you never touch code.
Forget the chatbot on your homepage. The AI automations that really pay are inside, on boring tasks nobody wants to do.
That spreadsheet that started as a helper and now runs half the company. How to tell when it's finally time to replace it with something solid.
