Richard Tomlinson has spent the bulk of his career helping organizations work with their own data, including getting their own people to trust it. In both BI and product roles, he has watched the same failure pattern repeat itself: dashboards built with good intentions that people quietly stopped using. Now that AI can generate a chart in seconds, his experience tells him that “self-service” has to mean something more than fast.
In this exchange, Richard explains why dashboards lose trust long before they lose good design, what AI genuinely changes about that problem and what people have to bring to a chart that no AI assistance can supply on its own.
Why do people stop using BI dashboards?
Tell me about a dashboard you’ve seen fail. Not one that looked bad, one that people simply stopped opening.
Richard Tomlinson:The dashboards that fail are not necessarily ugly. Often they fail because someone sees a number they don’t believe and can’t quickly understand why it is there.
Picture an executive opening a revenue dashboard on Monday morning and seeing a figure that’s different from the report Finance circulated on Friday. They don’t know whether the dashboard is using a different revenue definition, a different refresh time, a different filter or simply the wrong data. If they have to ask an analyst to find out, the dashboard has already failed one of its most important jobs.
Trust tends to disappear much faster than it is built. Users rarely file a ticket that says they no longer trust the BI platform. They quietly stop opening it and go back to spreadsheets, analysts or manually prepared reports.
That’s why I think good visualization starts before the visualization itself. A clear chart can’t compensate for an unclear metric, a questionablelytics environment, this matters even more, because users can generate many more analyses much faster. If the underlying data, semantics and governance are inconsistent, AI ends up just helping you produce inconsistent charts faster
The best modern analytics experiences connect the visualization back to its data and its meaning, so there is one version of the truth across every surface instead of five. For example, Databricks AI/BI Dashboard datasets inherit Unity Catalog governance, and the platform maintains lineage between data assets. That means a user who doubts a number can trace it back to its
What should leaders prioritize when adopting AI dashboard tools?
As AI transforms how we use and interact with data, what should leaders prioritize when bringing on new AI dashboarding tools?
Richard Tomlinson:I like the idea of trust debt, because organizations accumulate it every time users encounter two versions of the same metric or an unexplained discrepancy, an outdated dashboard, or an answer they can’t verify.
AI can make that debt substantially worse, because it changes the economics of analytics. Organizations used to build hundreds or even thousands of curated dashboards over years. With generative AI, anyone can create a new chart in seconds and with agentic authoring, AI can now quickly create an entire multi-page dashboard, including datasets, visualizations, filters and layout.
Databricks’ Genie Code, for example, can take a natural-language objective, find the relevant data, build datasets, create visualizations, configure filters, organize pages and refine the result much faster than that used to take. That is a real productivity gain, but it changes what leaders need to govern, since you can’t review every AI-generated chart by hand.
So, my answer is that I would prioritize four things:
- Govern the data, so AI inherits the same permissions and controls that apply everywhere else.
- Govern the meaning, so important measures and dimensions come from reusable business definitions instead of being reinvented every time someone generates a chart.
- Preserve provenance, so a user can understand what data and calculation produced an important result.
- Design for inspection, so that visualizations invite follow-up questions, filtering and drilling rather than presenting an unexplained answer as fact.
Good governance actually enables more self-service. With strong foundations, AI can safely generate far more analytical experiences without a central BI team handcrafting every one.
Where do AI-generated charts break down?
When an AI assistant generates the chart, it is making decisions based on the shape of the data, not the decision the person is trying to make. Where does that break down?
Richard Tomlinson: I would adjust that premise a bit. Modern AI assistants use more than the shape of the data. They can interpret the request and its context, too. If I say, “Show how revenue has changed over the last 12 months,” that implies a time series. If I ask, “Which five regions generate the most revenue?,” the same data might point toward a ranked bar chart. Ideally authors should be able to describe visualizations in natural language, which is why Databricks already enables Genie Code to plan and build multiple visualizations as part of a larger dashboard objective.
Where AI still struggles is purpose. A dataset tells you what values are available. A prompt tells you what someone asked. Neither one reliably tells you who is going to use the visualization, what decision they are trying to make, what comparison matters most, what should attract attention first, what level of precision is required, what context the audience already understands or what could be misinterpreted.
Let’s say a metric has fallen from 94% to 91%. AI can chart that decline correctly, but whether it is catastrophic or irrelevant noise depends entirely on business context that lives nowhere in the dataset. That’s why the human role is shifting from drawing charts to specifying intent and exercising judgment. AI can do the mechanical work of building a visualization, but someone still has to decide what it’s supposed to communicate and whether it’s accurate.
