Step-by-step method for auditing, piloting and supervising one workflow at a time.
A key security concern when using generative AI is sensitive data exposure, meaning confidential information slips out of your hands the moment someone pastes it into an AI tool. Most of the time it happens quietly. A sales rep wants a messy customer list reformatted, so it goes into a chatbot. A developer can’t figure […]
For most production AI agents, yes. An agent that acts on its own should have an identity you can name, scope, audit and revoke. That doesn’t mean handing it broad access. The setups that hold up best tie each agent’s permissions to a single task, and to the intent of one person who answers for […]
Knowledge work automation means using software to take over, or speed up, work that runs on information. That includes drafting, summarizing, searching, classifying, routing, and analyzing. AI has pushed the idea into almost every office. The research on results is messier than the sales pitch. Some controlled experiments show large gains. Studies of whole workforces […]
An AI agent is software you hand a goal to. It works out a next step, uses a tool to carry it out, looks at what came back, and decides what to do next. Anthropic’s guide to building effective agents puts it more bluntly: language models using tools in a loop, steered by feedback from […]
Every task you hand to AI ends up in one of three places. Software does the task, AI helps a person do it, or a person keeps doing it alone. Each option comes with its own costs, risks, and review work, and the right one can change from one step to the next inside a […]
Automation follows rules you write in advance. AI handles the parts of a task where those rules run out: interpreting language, judging context, predicting an outcome. Most real workflows need both. The useful question is which steps belong to each one, and where a human should stay in control. This guide covers the technical difference, […]
