Table of Contents
Introduction
Over the past few years, I’ve immersed myself in the world of data—collecting, cleaning, analyzing, and most importantly, visualizing. Like many professionals, I relied heavily on dashboards to bring data stories to life. Yet as I delved deeper into user needs, I realized a key challenge: the more dashboards I built, the harder it became to answer everyone’s unique questions in a single “master view.”
This led me to an important conclusion: within the next couple of years, we’re likely to see the rise of on-demand AI insights—where stakeholders simply ask a question in plain language and instantly receive a custom dashboard, presentation, or data narrative. In this article, I’ll explore why traditional dashboards might fade in importance, how AI can fill the gap, and what role existing BI tools like Looker and Tableau play in this evolution.
Why Traditional Dashboards Are The New Newspapers
Most dashboards are designed with a handful of core questions in mind—questions that analysts and business owners assume will be asked repeatedly. The trouble arises when new questions pop up or when someone needs a slightly different perspective. This can lead to constant rework, crowding dashboards with filters and tabs, or spinning up entirely new ones.
In short, static dashboards struggle to keep up with the fluidity of real-world decision-making. Business leaders often want immediate, custom insights without wading through a sea of irrelevant charts.
The Shift to On-Demand AI
So, what if instead of building and maintaining massive, one-size-fits-all dashboards, we prepared our datasets for AI-driven Q&A? In this future scenario:
- Stakeholders Type a Question: “How did our Q4 revenue by channel compare to last year’s holiday period?”
- AI Processes the Query: The AI draws on carefully curated data sources—already aligned, cleaned, and modeled.
- Results Generated On-the-Fly: The stakeholder sees a dynamic presentation: charts, commentary, or even a story-like walkthrough tailored to that exact question.
No more sifting through static visuals, applying (and re-applying) filters, or waiting for a busy analytics team to create something new. It’s a win-win: business users get speed and agility, while data teams have more time to focus on strategic work like data governance, advanced modeling, and predictive analytics.
Looker vs. Tableau: Two Approaches
(Opinion)
Many BI and analytics tools are evolving to fit into this new AI-centric paradigm, but not all are keeping pace in the same way. From my experience and observations:
- Looker:
- ML-Ready Data Modeling: Looker’s architecture is highly configurable, emphasizing a centralized data model (LookML). This design can position Looker as an ideal platform for machine learning integrations. By standardizing the definitions of metrics and dimensions, Looker can serve as a consistent “source of truth” for AI-driven insights.
- Future-Focused Approach: Looker’s emphasis on governed data layers and robust APIs suggests they are preparing to integrate more deeply with AI and ML workflows.
- Tableau:
- Dashboard-Centric Heritage: Tableau is widely regarded for its user-friendly dashboarding and data visualization capabilities. Its visual drag-and-drop interface revolutionized self-service BI.
- Slower ML Adoption: While Tableau has made strides in adding AI features (like “Ask Data”), some argue it remains more dashboard-focused. It excels at building rich, interactive dashboards for specific questions—yet it may require additional tooling or integrations to fully match the on-demand AI model.
This doesn’t mean Tableau won’t adapt—just that, in my opinion, Looker is somewhat more aligned out-of-the-box with the future of ML-ready data.
Benefits of AI-Driven Insights
- Speed & Agility: Real-time, ad hoc query responses reduce bottlenecks in decision-making.
- Personalized Queries: Each user can get exactly the view they need, instead of shoehorning their question into a prebuilt dashboard.
- Analytics Scalability: As business needs expand, AI tools can handle more queries without the manual overhead of constantly updating dashboards.
- Empowered Data Teams: Freed from repetitive dashboard requests, data analysts and scientists can focus on higher-impact strategic tasks.
Potential Challenges
- Data Preparation: AI tools require well-structured, trusted data sources. If your datasets aren’t “clean,” the AI insights won’t be accurate.
- User Adoption: Stakeholders used to traditional dashboards may need training to trust and embrace an AI-driven approach.
- Ethical & Privacy Concerns: Automated systems must be transparent, especially when dealing with sensitive or personal data.
Looking Ahead
(Opinion)
Will dashboards completely disappear? Probably not. Static visuals still have a place in reporting and storytelling. However, the real innovation lies in AI-driven, on-demand insights that respond instantly to ever-changing business questions.
This shift will influence how we design data pipelines, govern datasets, and equip teams with new skills. As tools evolve (and as Looker, Tableau, and others race to offer more advanced AI capabilities), the line between “reporting” and “analysis” will blur. Ultimately, this marks a giant leap forward for data-driven decision-making—making it more agile, personalized, and scalable than ever before.
Conclusion
Data professionals have spent years perfecting dashboards, but it’s time to acknowledge their limitations. Embracing AI-driven tools that generate custom insights on the fly could be a game-changer, streamlining the analytics workflow and elevating the conversation around data from “which visual do I click?” to “what decisions can I make right now?”
Thank you for reading! Connect with me for more insights on the evolving landscape of data, analytics, and business intelligence.