ChatGPT can help you plan and partially build dashboards, but it cannot create a fully functional, interactive dashboard on its own. Understanding what it can and cannot do will save you time and set the right expectations.
What ChatGPT does well is the thinking and planning side of dashboard creation. You can describe your data and goals, and it will suggest which metrics to track, recommend chart types for different kinds of data, write Excel formulas or Python code for data transformations, and even generate sample data structures. If you are stuck on how to organize a dashboard layout, ChatGPT can outline a logical structure with sections for KPIs, trends, and detailed breakdowns. It is genuinely useful as a brainstorming and formula-writing assistant.
ChatGPT with the Code Interpreter (Advanced Data Analysis) feature goes a step further. You can upload a CSV or Excel file, and it will write Python code to analyze the data and generate static charts using libraries like matplotlib or seaborn. These charts can be downloaded as images. This is helpful for one-time analysis, but the output is a set of static images, not an interactive dashboard that filters, updates, or refreshes with new data.
The gap between what ChatGPT produces and what most people mean by "dashboard" is significant. A real dashboard lets users click filters, drill into details, see data update in real time, and share a live view with colleagues. ChatGPT cannot host anything, connect to live data sources, or create a shareable web application. Every time your data changes, you would need to upload a new file and regenerate everything from scratch.
For actually building and hosting interactive dashboards, you need a platform designed for that purpose. AgentUI bridges this gap directly. Like ChatGPT, it accepts natural language descriptions of what you want. But unlike ChatGPT, it produces a live, hosted dashboard application that connects to your data sources, includes interactive filters and drill-downs, and stays current without manual uploads. You get the convenience of describing what you want in plain language combined with the result of a real, working dashboard.
The practical takeaway is this: use ChatGPT to think through your dashboard requirements, get formula help, and prototype ideas. Then use a dedicated dashboard builder to turn those ideas into something your team can actually use every day. Trying to force ChatGPT into producing a production dashboard will leave you with screenshots and code snippets instead of a tool people can interact with.