Using Artificial Intelligence (AI) to build better, faster, and more effective dashboards is one of today’s most exciting promises. Despite all the hype, the truth is simple: AI not only can, but it should be used to enhance data visualization, never to replace the professional behind it.

Effective data visualization best practices go far beyond the charts themselves. They require clarity about the business problem, a deep understanding of the target audience, technical expertise, and visual sensitivity. When applied strategically, AI becomes a powerful ally, saving time, reducing rework, and accelerating the journey to a final product. But for this to succeed, AI must be applied at the right stages, with clear goals and realistic expectations.

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Applying AI to Data Visualization: Best Practices and Tips

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Below, I’ll share how AI can be strategically applied across ten key stages of the dashboard creation process, based on my years of hands-on experience in this field.

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1. Understanding Information Needs

The first step to creating a good dashboard has nothing to do with data, but with people.

That means talking to users, understanding the use case, gathering requirements, and identifying what truly matters to be delivered based on the user’s experience and available time to use the tool and perform analyses. At this stage, AI can be a great ally by:

• Transcribing and summarizing stakeholder interviews
• Turning notes into clear and objective requirements
• Spotting gaps and suggesting questions that haven’t yet been asked
• Grouping open-ended survey responses and running sentiment analysis

The tip here is simple: use AI to organize and enrich listening. But the critical interpretation is still yours.

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2. Creating the Visual Mockup

With requirements mapped, it’s time to sketch a first interface: a static mockup that shows how the data will be presented, even if not yet connected to real sources. AI can help in several ways:

• If you already have a proprietary generative AI model trained on past creations, such as a library of charts, visualizations, and design templates, it can significantly speed up this step. Otherwise, tools like DALL·E or MidJourney can quickly generate prototypes from simple text descriptions
• Generative AI can also recommend which visualizations are most appropriate for your data and objectives.

Still, good judgment stays with you: layout decisions, accessibility, and visual hierarchy always depend on context and audience. AI helps you move faster, but not always in the right direction on its own.

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3. Validating the Mockup with Clients

Once created, it is time to show the mockup to the client. The idea here is to confirm whether the design truly delivers what they need, considering not only the data presented but also the time they will have to analyze everything. How AI can support:

• Simulating A/B tests with different mockup versions using fictitious data
• Summarize client feedback meetings
• Creating automated forms for multiple clients to provide structured feedback
• Analyzing messages and emails to identify patterns of questions, complaints, or praise

Here, AI works especially well as a digital assistant, helping to maintain track of everything without needing to carry a notebook to every meeting.

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4. Checking Data Availability

Now that the mockup is approved, it’s time to check whether the data actually exists. This may seem trivial, but it’s not: sometimes the data doesn’t exist yet, and other times there are multiple versions of the same variable, especially in fields like geography and dates. AI can help by:

• Automatically verifying whether the mapped variables actually exist in the Data Warehouse
• Generating SQL scripts based on textual descriptions of charts and fields
• Performing data profiling: identifying missing data, outliers, or inconsistent patterns
• Suggesting validation methods based on business rules (for example, detecting inconsistencies with expected values)

This stage requires close collaboration between engineers, analysts, and architects. AI can make this technical dialogue easier and ensure that everyone is speaking the same language and working with the same data.

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5. Validating Data with Clients

Once mapped, data must be validated to ensure it matches client expectations. And here, extra attention is required: similar names can hide different meanings. AI can help by:

• Generating data samples to send for validation
• Creating friendly reminders for when the client forgets to provide feedback
• Adjusting queries based on feedback, in case something is rejected
• Writing didactic explanations for non-technical users

This stage calls for both precision and empathy. And AI can take a lot of the bureaucratic weight off your shoulders.

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6. Adjusting the Mockup & Sign-Off

With the data validated, it may be necessary to revise the mockup. Sometimes, promised data doesn’t exist. Sometimes, metrics need to be redefined. AI supports this step by:

• Suggest adjustments to the charts based on client feedback and the available data
• Highlighting accessibility improvements (contrast, font size, readability)
• Sending automated reminders for sign-off

Here is where the project gains real shape, and AI keeps it on schedule.

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7. Data Engineering

Now it’s time to move from draft to reality. Although queries are mostly created during data validation, they must now be automated, pipelines organized, and tables properly documented. AI can:

• Speed up pipeline development with dbt + LLMs
• Detect anomalies, duplicates, and inconsistencies
• Help in the alignment of development and production environments

In addition, code review powered by AI is an increasingly common practice and helps prevent bugs and rework.

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8. Building the Dashboard

With everything ready, the final step is to build the dashboard in your visualization tool of choice (e.g., Tableau, Looker, Omni). Here, calculated fields, dynamic filters, and interactions come to life. AI can support by:

• Creating complex calculated fields based on business rules
• Suggesting tooltips, explanatory texts, and visual adjustments
• Generate comments to document the reasoning behind each metric
  
  

This way, you ensure that the dashboard not only works but is also understandable and sustainable over time.

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9. Validating the Dashboard

Once the dashboard is complete, it must undergo a final and critical validation: Does it truly answer the business questions? Does it match the data? Does the layout work within the time the user has to explore it? Many companies call this stage User Acceptance Testing, but whatever the name, it is essential to confirm that all informational needs are met before the product goes into production. AI comes in as:

• An assistant for sending dashboards for validation, drafting professional emails, and sending friendly reminders when needed
• A tool to measure reading time, engagement, and interaction with the charts
• Support for acceptance testing and continuous feedback

The idea is to ensure everything is in place before the product goes live.

The final validation ensures the dashboard truly works: does it answer the business questions, match the data, and fit within the user’s time?

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10. Documenting the Dashboard

Finally, a stage that is often forgotten: documentation. Without it, future maintenance becomes a nightmare. Turnover, vendor changes, mergers… There are plenty of reasons to document data and the product properly, preventing the loss of knowledge about the business and its products in the future. AI can:

• Generate documentation based on conversations, code, and the dashboard itself
• Create summaries that explain metrics, data sources, and applied logic
• Integrate with corporate chatbots to answer FAQs

Documenting with AI is faster, and it can be the difference between a useful dashboard and an abandoned one.

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Conclusion: AI as a Bridge, Not a Shortcut

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Across these ten stages, the message is clear: AI should never be treated as a shortcut to bypass steps. Instead, it should be understood as a bridge, one that connects needs to meaningful insights more quickly. Used this way, AI not only helps deliver results faster but also contributes to building a product of superior quality.

Data visualization best practices still demand human reasoning, empathy, and contextual understanding. It requires knowledge spanning psychology, design, coding, mathematics, business, and others. That’s why AI cannot replace a professional with significant experience and a strong set of soft skills. However, it can help even the best professionals become more productive. AI is here to expand our capabilities, not to replace our judgment.

When used consciously and strategically, AI enables analysts and designers to deliver dashboards that are more effective, sustainable, and aligned to what matters most: decision-making with confidence.

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This article is a collaboration between  Shearwater and DataViz Insights.

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