AI is making it possible to build dashboards in minutes rather than days. But as the technology advances, a bigger question is emerging: what happens to the tools, skills and expertise businesses have traditionally relied on to turn data into decisions?

By Kristiyana Raycheva | Data, Analytics & AI  | Stanton House

Could AI tools like Claude eventually replace traditional business intelligence platforms such as Power BI and Tableau?

It's a question I recently posed on LinkedIn, and the response was fascinating.

The post generated nearly 50,000 impressions, reached more than 35,000 professionals and sparked a discussion among data analysts, BI developers, data architects and technology leaders.

What interested me most wasn't the level of engagement, but the consistency of the themes emerging from the conversation.

While opinions differed on whether AI would eventually replace established BI platforms, there was a much stronger consensus around something else: making data easier to visualise doesn't necessarily make it more reliable, meaningful or useful.

And that raises important questions, not just about the future of business intelligence technology, but about the people and capabilities organisations will need as AI becomes increasingly embedded in analytics.

Building a dashboard is getting easier. Trusting it isn't.

One of the most compelling developments in AI is how quickly it can turn raw information into something visual and accessible.

Tasks that previously required specialist knowledge of BI tools, coding or dashboard development can increasingly be completed through natural-language instructions.

For businesses, the potential is significant. Faster reporting, reduced development time and greater accessibility could enable more people across an organisation to explore data without relying on specialist teams for every request.

But several professionals responding to my post highlighted an important distinction between generating a dashboard and producing information that can be trusted.

A dashboard might look polished and convincing, but are the calculations correct? Are the underlying definitions consistent? Is the data complete, secure and up to date?

These challenges aren't new. Organisations have spent years trying to establish consistent reporting standards and reliable data foundations. AI doesn't automatically resolve them simply because it can produce the final visualisation more quickly.

In fact, as AI makes dashboard creation more accessible, there is a risk that businesses could generate more reports, more quickly, without necessarily improving the quality of the information behind them.

The real challenge may be shifting from how quickly we can produce information to how confidently we can rely on it.

Why data governance could become even more important

One of the strongest recurring themes in the discussion was the importance of data governance.

Several contributors pointed to the role of semantic models: the underlying structures that establish consistent definitions, calculations and relationships between data.

Consider a relatively simple metric such as revenue.

Depending on the business, different teams might calculate it using different assumptions, timeframes or exclusions. Without agreed definitions, two dashboards could present different figures while both appearing perfectly credible.

AI can make it easier to create those dashboards, but it doesn't automatically determine which definition the business should use.

That requires agreement between stakeholders, clear ownership and an understanding of how the organisation operates.

Others highlighted challenges around data security, access permissions, auditability and the complexity of integrating information from multiple enterprise systems.

These considerations become particularly important when dashboards inform financial reporting, operational performance or strategic investment decisions.

For Private Equity-backed businesses, where leadership teams often need reliable visibility across multiple functions, systems or portfolio companies, inconsistent reporting can have significant consequences.

AI may accelerate the process of accessing and presenting information, but organisations still need confidence in the foundations supporting it.

And as AI moves beyond presenting information towards recommending actions, the importance of accountability only increases.

Does this mean Power BI and Tableau are here to stay?

The discussion also challenged the assumption that AI and traditional BI platforms are necessarily competing for the same role.

Some contributors believe AI will fundamentally disrupt the way dashboards are developed and consumed. Others argued that enterprise platforms still provide essential capabilities that aren't easily replicated through standalone AI-generated visualisations.

Both perspectives have merit.

AI is already changing what is technically possible. The ability to ask questions of data in natural language, generate visualisations and rapidly explore different scenarios could significantly reduce the time and specialist effort involved in some reporting activities.

But enterprise BI platforms do much more than display charts.

They connect to complex data environments, manage access and permissions, support established reporting processes and provide governed frameworks through which organisations can share information.

For businesses operating at scale, these capabilities remain important.

One possibility raised in the discussion was that AI will increasingly become the interface through which people interact with governed data, rather than replacing the underlying infrastructure entirely.

Instead of navigating multiple dashboards to find an answer, a business leader might simply ask a question and receive an explanation, supported by information from trusted systems.

In that scenario, the dashboard itself becomes less central to the user experience, while the quality of the underlying data environment becomes even more important.

The future may therefore be less about AI versus Power BI or Tableau, and more about how AI changes the way businesses access, interrogate and act on information.

If AI can build the dashboard, what happens to the data analyst?

For me, this is where the conversation becomes particularly interesting.

If AI reduces the time required to build dashboards, write queries and generate routine reports, how does that change the value organisations place on data and analytics professionals?

Several people responding to the post suggested that the emphasis of analytics roles could shift away from producing information and towards understanding what it means.

It's an important distinction.

Imagine a dashboard tells a commercial director that sales have fallen by 15%.

The information itself is useful, but it doesn't explain why sales have declined, whether the change is significant, which factors are contributing or what the business should do next.

Answering those questions requires more than the ability to produce a chart.

It requires commercial understanding, analytical judgement, curiosity and the ability to challenge assumptions.

A skilled analyst can identify inconsistencies, understand the wider business context, investigate underlying causes and translate findings into recommendations that stakeholders can act upon.

AI may help accelerate parts of that process, but speed alone doesn't guarantee better decisions.

This suggests that the value of data professionals could increasingly lie in their ability to connect technical expertise with business outcomes.

We're also likely to see continued importance placed on capabilities such as data modelling, governance, architecture and quality assurance, particularly as organisations look to introduce AI into existing data environments.

Rather than removing the need for specialist expertise, AI could change where that expertise creates the greatest value.

Of course, this doesn't mean every analytics role will evolve in the same way. The impact will depend on the organisation's data maturity, technology infrastructure and the nature of the work being performed.

But it does raise an important question for employers: are we still defining analytics roles around the tools people use, or around the problems we need them to solve?

What should employers be thinking about?

For organisations investing in AI-enabled analytics, the temptation may be to focus primarily on the technology: which tools to adopt, how quickly they can be implemented and where efficiencies can be achieved.

Those are legitimate considerations, but the discussion highlighted several questions that deserve equal attention.

Do we have the right data foundations?

Before introducing new AI capabilities, businesses need to understand whether their data is sufficiently structured, governed and reliable to support them. Accelerating access to poor-quality information is unlikely to improve decision-making.

Are we hiring for the capabilities we'll need tomorrow?

Technical expertise remains important, but employers may need to place greater emphasis on analytical judgement, commercial understanding, stakeholder engagement and the ability to work effectively alongside AI.

Are we helping existing teams evolve?

As routine activities become increasingly automated, there is an opportunity to develop employees' capabilities in more complex analysis, interpretation and strategic business partnering. Organisations that focus solely on efficiency risk overlooking the value their existing teams could contribute in different ways.

Are we measuring better decisions, or simply faster reporting?

Reducing the time it takes to produce a dashboard is valuable. But the greater opportunity lies in helping people make more informed decisions, identify opportunities and act with confidence.

Ultimately, the success of AI in analytics shouldn't be measured only by what it can generate, but by the business value it helps create.

The future of analytics is about more than technology

What started as a question about whether Claude might replace Power BI or Tableau quickly became a much broader discussion about trust, governance, business understanding and the future of analytics roles.

There is no simple answer to which platforms will dominate, or exactly how quickly established ways of working will change.

But one thing stood out from the conversation: making it easier to generate information doesn't remove the need for people who understand it.

If anything, as AI makes analytics more accessible, the ability to question outputs, establish reliable data foundations and translate information into meaningful business decisions could become an even greater source of competitive advantage.

For employers, that means thinking beyond the technology itself and considering how their data teams, skills and leadership capabilities need to evolve alongside it.

The question isn't simply whether AI will replace the dashboard. It's whether organisations have the right people and foundations to make better decisions with the information AI provides.

Let's continue the conversation

At Stanton House, we work with organisations to understand their evolving Data, Analytics & AI talent requirements, from specialist technical expertise through to the leadership capabilities needed to deliver business transformation.

If you're considering how AI might change the structure, skills or capabilities of your data and analytics team, I'd welcome the opportunity to exchange perspectives.

Get in touch to discuss your data and analytics hiring priorities.