Should AI live in its own chat window, or quietly show up inside the tools and workflows you already use every day?

This explores where AI should sit inside a product so it doesn't become an island: a chatbot panel people have to remember to visit, cut off from the rest of their work. The corpus gives a fairly consistent answer. Put AI where the work and the context already are, not in a new destination. Friedman calls this being "AI-second." The best AI features look like browser autofill. They fit into habits people already have, the way AI shows up inside Slack threads, Teams meetings, or GitHub pull requests. Dedicated AI panels and text boxes that appear on every screen tend to sit unused, because they ask users to break their flow and start a new kind of conversation Should AI features integrate into existing workflows or stand alone?.

The less obvious point is that "where" is about data and memory as much as screen placement. AI context is not like a fixed menu or form. It is put together on the fly from the prompt, the conversation history, retrieved documents and hidden state, and it changes from one moment to the next How does AI context differ from conventional software context?. Nielsen Norman Group adds a useful twist. What an AI "remembers" is a product decision about which stored memories get pulled into each request, not a property of the model How do AI products decide what to remember between conversations?. So an AI can sit in exactly the right spot on screen and still feel isolated, because it can't see the documents, tickets or history that give the user's task its meaning. Kat Holmes's list of design skills for the AI era includes "context maps and ontologies" next to conversation design for this reason. Someone has to design what the AI is connected to, not just how it talks What design skills do teams need for the AI era?.

Atlassian's guidelines for its Rovo AI assistant are the most concrete example in the collection. They say AI should connect *individual* work to *team* knowledge. It should know where you are, avoid interrupting your flow, show what it is doing, and support several people and agents working together How should AI design connect individual work to team knowledge?. In other words, avoiding isolation also means social integration. A field experiment at Procter & Gamble suggests why this matters. Individuals using AI matched the output of two-person teams. AI also broke down functional silos: people produced more balanced solutions that reached beyond their own professional background Can generative AI replace the benefits of having a human teammate?. An AI wired into shared knowledge can carry some of that cross-team perspective. A standalone chatbot can't.

There is also a time dimension. Microsoft's 18 guidelines for human-AI interaction are grouped by when they apply: at first contact, during use, when the AI is wrong, and over time How should AI systems be designed across different interaction phases?. An AI that only works well in the moment, and never learns from corrections or adapts as the relationship goes on, is isolated in a different way. Evans gives a reality check. Even well-placed tools often fail because workers don't see their own tasks as automatable, and adoption depends on decisions that cut across departments Does easier tool-building actually solve enterprise adoption problems?. So integration points include the org chart and the workflow, not only the interface.

The takeaway you might not have expected: an "isolated" AI experience is usually not a layout problem. It is a context problem. The most important integration decision may happen out of sight, in what the AI is allowed to know, remember and share across a team.