Ask an AI assistant which marketing channel generated the most revenue last quarter and it will probably give you an answer in seconds. It may even explain the result, identify a trend and suggest what to do next. There is just one problem: is the number actually right?
As marketers bring ChatGPT, Claude and other AI tools into analytics, that question is becoming increasingly important. The attraction is obvious. Instead of waiting for an analyst to build a report, marketers can ask a question in plain English and receive something that looks remarkably like an informed analysis. But looking convincing and being correct are not the same thing.
Dreamdata’s new AI offer addresses precisely this problem. Its MCP server allows marketers to connect AI assistants to the company’s B2B go-to-market data while keeping definitions such as pipeline, revenue and attribution governed by Dreamdata’s underlying semantic layer. The idea is that different people can ask the same question in different ways without the AI quietly changing what the numbers mean.
AI can interpret the data. It should not invent the accounting
This distinction sounds technical, but it goes to the heart of how AI will be used in marketing.
Large language models are extremely good at language. They can explain, summarize and connect information in ways that make complex analysis much easier to consume. But marketing analytics depends on definitions that are far less flexible.
What exactly counts as a pipeline? Are renewals included in revenue? Which attribution...
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