Practical writing on AI systems, agents, automation, and lessons from building.
AI automation is automation where one or more steps need a model because a rule cannot do them: reading, classifying, drafting, deciding within limits. Here is the definition, where it applies, and where plain automation is still right.
The phrase AI website is used for three different things: a site generated by a model, a site with a chatbot on it, and a site whose forms and content are wired into a model behind the scenes. Only one changes the business.
AI for business means using models to do specific steps inside a workflow: reading, drafting, classifying, deciding within limits. Here is what that covers, where it pays, and where it does not.
The Model Context Protocol is a standard for connecting AI models to tools and data so that one connection works across many clients. Here is what it is, why it matters for a business that owns its systems, and what to control.
Zapier and Make both automate between apps and both call models. Zapier is simpler to start and easier to hand to anyone. Make goes deeper on branching and data handling. Here is how a lean team should choose.
Architect a customer 360 data model around five stages, Data Graphs, and identity rules to enable real-time activation, personalization, and embedded AI.
Practical steps to instrument data lineage tracking: five stages using W3C PROV and OASIS DPS, start with automation and include embedded AI patterns.
Five practical components for practitioners to design and scale human-in-the-loop automation, prevent review bottlenecks, and capture feedback for retraining.
Make Zapier AI automation production ready for teams. Map structured outputs, enforce governance, add human review, plus when to hire help.
A breakdown of which AI workflows actually pay off in a restaurant, from phone automation to demand forecasting, and where the evidence for AI in the kitchen is still thin.
