Dashboards Are Dead? What Comes Next in Generative BI Platforms (2025)

Why data narratives and dashboards need each other—not to replace one another, but to work as true companions

The Recurring Death of Dashboards

Here we go again. Dashboards are dead—killed by AI this time. Except they weren't dead in 2020 when COVID 'killed' them. Or in 2019 when UX designers 'killed' them. Or any of the other times someone declared victory over visual analytics. Here's what's actually happening: dashboards aren't dying. They're getting smarter

The real story isn't about dashboards dying. It's about an industry learning, slowly and painfully, that the future isn't "dashboards OR data narratives"—it's dashboards WITH intelligent data storytelling built right in. This is the story of how we got here, what we learned, and where we're actually going.

A Brief History of Dashboard Doomsaying

The Pre-2020 Rumblings

The critique of dashboards didn't start in 2020. For years, practitioners had been documenting familiar problems: cognitive overload from too many metrics crammed onto one screen, "death by dashboard" syndrome where organizations accumulated hundreds of unused reports, and the fundamental issue that dashboards showed you what happened but rarely explained why.

In August 2020, UX design authority Jared Spool articulated a perspective that had been percolating for years in a viral Twitter thread: "Dashboards report on current status. Users don't act on status. They act on change in status. Dashboards are passive when the user needs something active. They are a failure before it happens." Thread Reader App

His argument was sophisticated and touched on something real. Dashboards, he suggested, were often the wrong solution to a problem that hadn't been fully understood. They were what people asked for, but rarely what they actually needed.

The 2020 Flashpoint: When Everything Changed

Then came April 9, 2020. Taylor Brownlow, a data analyst frustrated with the limitations of her tools, published "Dashboards are Dead" on Towards Data Science. The article reached 60,000 readers in the first weekend alone and would eventually accumulate over 250,000 views. Medium

What made Brownlow's essay resonate wasn't just good timing—though the pandemic certainly helped. It was the specificity of her frustrations. She described 67-page dashboards, endless filter requests, users exporting data to Excel to do their own analysis anyway, and worst of all, the trust problems when numbers didn't match expectations. "People started disparaging dashboards as 'wrong,' and blatantly ignoring them," she wrote.

The timing was explosive. COVID-19 was forcing organizations to make real-time decisions with life-or-death consequences. Static dashboards built over weeks suddenly felt dangerously obsolete. Gartner's 2020 Top 10 Data and Analytics Technology Trends predicted that "dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration," and that "the amount of time users spend using predefined dashboards will decline." Gartner

This wasn't just analyst prediction—it was validation of what many were feeling. And the vendors smelled blood in the water.

ThoughtSpot launched an aggressive "Dashboards are Dead" marketing campaign, complete with whitepapers and webinars. Other vendors followed with their own alternatives: notebooks, search-based analytics, automated insights, anything that wasn't a traditional dashboard. The gold rush was on.

The Backlash: 2021-2023

But something interesting happened over the next few years. The initial excitement faded, and a more nuanced understanding emerged.

In 2023, Taylor Brownlow herself published a follow-up reflection: "What I've come to see more clearly in the last few years is that 'Dashboards are Dead' has a lot less to do with dashboards than, well, everything around them. In the end, the root of my discontent was the relationships, communication, processes, and people." Medium

This was a crucial admission. The problem wasn't the dashboard as a tool—it was how organizations were using them to patch over deeper issues. Bad communication between data teams and stakeholders. Unclear requirements. Lack of trust. Process problems.

Companies that had rushed to eliminate dashboards in favor of alternatives found themselves facing the same fundamental problems, just in a different form. And a pattern became clear: declaring things "dead" was often just thinly veiled marketing for alternative products.

The AI Wave: 2023-Present

Now we're in the middle of the latest wave: generative AI as the dashboard killer. Text-to-SQL tools promise you can just ask questions in natural language and get answers. LLM-powered agents offer to write code on the fly. Why would you need a dashboard when you can just chat with your data?

The promise is seductive: eliminate the middleman, eliminate the visualization, just ask and receive. But early adopters are discovering the same issues that plagued previous "solutions": technical complexity, hallucinations and inaccuracies, loss of context, and most critically, the elimination of the very thing that made dashboards valuable in the first place—the ability to see patterns at a glance.

You can generate a paragraph describing a trend, but sometimes a line chart communicates in one second what takes a minute to read. The human visual system is remarkably good at pattern recognition. Text-to-SQL doesn't change that fundamental truth.

The False Choice: Dashboards vs. Data Narratives

Why the Binary Thinking Failed

The entire debate has been framed around a false dichotomy. On one side: the visualization camp, arguing that well-designed dashboards with proper KPIs provide instant comprehension and monitoring capabilities. On the other: the narrative camp, insisting that data storytelling with context and interpretation is what users actually need.

Both sides are right. And both sides are wrong.

The trap that companies fell into—and some continue to fall into—is believing they had to choose. Products were built that eliminated dashboards entirely in favor of narrative feeds. Other products doubled down on visualization while ignoring the desperate need for context and explanation.

Real-world casualties litter the landscape: BI tools that generate beautiful paragraphs about data but make you hunt for the actual numbers. Conversational interfaces that answer your specific question but give you no sense of the broader context. Narrative platforms that tell you what changed but don't show you the trend.

What BI Users Actually Need

When you actually watch people use data tools—not what they say they want, but what they actually do—a clear pattern emerges.

They need immediate visual information. A dashboard showing revenue trending down is instantly comprehensible. No words required.

They need context and interpretation. But they also need to know WHY revenue is trending down. Is it seasonal? Is it a problem with a specific product line? Is it affecting all regions or just one?

They need the ability to ask follow-up questions. "Okay, I see the problem in the Northeast region. Now show me which specific stores are struggling."

And they need actionable insights, not just data displays. "Given this pattern, what should we actually do about it?"

The mistake was ever thinking these were separate needs requiring separate tools.

The Real Solution: Integrating Dashboards with Narratives

Here's what actually works: data narratives built ON dashboards, not instead of them.

Dashboards need to stay because they provide instant visual comprehension, pattern recognition at a glance, monitoring and alerting capabilities, and shared context across teams. When everyone is literally looking at the same dashboard, conversations get easier.

But data narratives need to integrate because they explain the "why" behind the numbers, surface insights you might have missed, guide action with context, and make data accessible to non-technical users who can read a story but might struggle with chart interpretation.

The approach isn't complicated: narratives that reference specific visualizations. Storytelling that updates in real-time with the data. Conversational analysis that knows what chart you're looking at. Context that enhances rather than replaces the visual information.

This isn't theoretical. It's the direction that successful products are moving, learning from the failures and false starts of the past five years.

The PowerBI Lesson: When Half-Measures Fail

Microsoft saw the trend early and attempted to address it with Power BI's "Smart Narratives" feature. On paper, it's exactly what we're talking about: automated text summaries that explain what's happening in your visualizations.

In practice, it's a cautionary tale about the difference between checking a feature box and actually solving the problem.

The smart narratives feel bolted on rather than integrated. They generate generic summaries that often state the obvious without adding real insight. The Copilot integration, while promising, struggles with the legacy architecture of Power BI itself. You can ask questions, but the answers often feel disconnected from the visual context you're already seeing.

This isn't meant to bash Power BI—it's a powerful platform that serves millions of users well. But the smart narratives feature reveals what happens when you try to add intelligence to an existing structure that wasn't designed for it from the ground up.

The lesson here is about technical execution matching conceptual promise. Having the right idea isn't enough. The integration needs to be deep, not superficial. The narratives need to understand the full context of what the user is looking at, not just generate summaries from individual charts in isolation.

What this teaches us about true integration: it requires rethinking the entire user experience, not just adding a text box to existing dashboards. It means designing for the conversation between visual and verbal information. It means accepting that "dashboard" and "narrative" aren't separate products—they're different aspects of the same experience.

The Future: Dashboards and Data Stories

Data Narratives FROM Dashboards

The future isn't text OR visualization. It's intelligent systems that generate narratives directly from what you're seeing.

Imagine looking at a revenue dashboard and seeing automated insight generation that references specific visualizations: "Revenue in the Northeast region dropped 15% compared to last month, primarily driven by the Electronics category shown in the bottom-right chart."

Contextual storytelling that updates with real-time data: "This represents the third consecutive week of decline, breaking the pattern we saw in Q1."

Natural language that points to visual evidence: "Notice the spike in customer complaints (top-left chart) that coincided with the revenue drop—this suggests a product quality issue rather than market conditions."

This is data narratives FROM dashboards—using the visual context to make the narrative more precise, more actionable, and more connected to what the user is already seeing.

Data Storytelling FROM Dashboards

The reverse is equally important: using the power of data storytelling to make dashboards more accessible and actionable.

Executive summaries generated from visual data: Busy leaders don't have time to study every chart. A narrative that captures the key insights from a complex dashboard serves a real need.

Drill-down narratives that explain trends: Click on an anomaly and get an explanation, not just more charts. "This spike is unusual compared to historical patterns. The last time we saw similar behavior was during the holiday season, but this is occurring in August."

This is data storytelling FROM dashboards—extracting narrative meaning from visual patterns and presenting it in a way that guides understanding and action.

The New Dashboard Paradigm

The dashboards of the future won't look radically different from today's. They'll still have charts and graphs and key metrics. But the experience will be fundamentally different.

Real-time + intelligent: Not just showing current numbers, but understanding what those numbers mean in context.
Visual + verbal: Charts that come with explanations, narratives that reference specific visuals.
Monitoring + exploring: Passive observation when things are normal, active investigation when something interesting appears.
Human + AI collaboration: The AI handles pattern detection and insight generation, humans provide judgment and context.

This isn't science fiction. The technology exists today. What's been missing is the right philosophy about how these pieces fit together.

Annie's Approach: Making It Real

We built Annie based on a simple conviction that emerged from watching this entire debate unfold: dashboards and data narratives aren't competitors. They're companions.

The philosophy is straightforward: build narratives that live alongside dashboards, reference them directly, and enhance them—rather than trying to replace them. Let users ask questions in natural language, but show them the visual answer. Generate insights automatically, but present them in the context of the charts they explain.

This is the approach we believe represents the actual future of business intelligence: not dashboards dying and being replaced, but dashboards evolving to become smarter, more conversational, and more helpful.

BI Dashboards Are Alive and Evolving

The pattern will continue. Five years from now, someone will declare dashboards dead again. Maybe it'll be because of some new technology we can't imagine yet. Maybe it'll be another marketing campaign from a vendor with an alternative to sell.

And once again, we'll go through the cycle of excitement, experimentation, and eventual realization that the problem was never really the dashboard itself.

The lesson from the past five years is clear: tools don't compete when they serve fundamentally different needs. They complement each other. The future belongs to platforms that understand this—that integrate rather than eliminate, that enhance rather than replace.

Dashboards aren't dead. They never were. They were just waiting to get smarter.

The visualization that helps you spot the pattern. The narrative that helps you understand it. The conversation that helps you explore it. The action that helps you respond to it.

All of these are part of the same experience. And that experience is just getting started.

Want to see dashboards and data narratives working together?
That's what we built Annie to do—bringing intelligent data storytelling directly into your visual analytics, not as a replacement, but as the companion your dashboards always needed.