From dashboards to AI agents: How the BI & Analytics market is changing

Greater pressure to make decisions, closer scrutiny of spending

The Business Intelligence & Analytics (BIA) market is undergoing a fundamental realignment. Volatile markets, geopolitical uncertainty and rising costs are putting companies under pressure to improve performance and adapt. This increases the need for timely, reliable information to support decisions.

At the same time, these pressures are making companies more selective about where they invest. They are consolidating their software landscapes, reviewing budgets closely and judging BIA software by its demonstrable contribution to efficiency, business performance management and better decisions.

Data architecture is putting pressure on BIA platforms to evolve

Reviewing an existing BIA landscape therefore leads directly to the question of architecture. The growing number of data sources and the evolution of data platforms add to the pressure for change.

Companies replacing legacy data warehouses often find that their new platforms also offer capabilities for data modeling, semantics, AI and analytics.

This raises fresh questions about which tasks the BIA platform should handle and which are already covered by the data platform. As functionality overlaps and integration approaches change, companies need to reassess how the platforms work together. Modernizing the data architecture also means reviewing the role of the existing BIA platform.

People and AI agents place new demands on analytics

The purpose of BIA is still to support decisions. A modernized data architecture alone, however, does not ensure that business users can understand and use relevant information more quickly. New AI interfaces and the promises being made in the market are also shaping expectations of how easily users can access relevant insights in the context of their work.

A dashboard can highlight a change in revenue. But that alone does not explain which products or regions are driving it, or whether action is needed. If business users have to search through several reports, apply filters and combine the results themselves, much of the analytical work still falls to them.

This is where the way business users work with data and insights is evolving. Modern analytics user experiences include alerts on relevant changes, the ability to ask follow-up questions conversationally, and insights delivered directly in the application where a decision needs to be made. Dashboards remain one possible entry point, but they are no longer the only reference point when designing how analytics is used.

Analytics for agents adds another requirement: AI agents also need analytical information to carry out their tasks. Their primary need is not dashboards, but programmatically accessible data and metrics with clear definitions and traceable calculations. The requirements for delivering analytical information therefore extend beyond use by people.

Consolidation and new competition reshape the market

The changing division of responsibilities between BIA platforms, data platforms and business applications is also shifting competitive boundaries. At the same time, new providers such as Golden Analytics, Gravity, Supersimple and Ridge AI are entering the market with AI and analytics tools. Meanwhile, consolidation continues among established enterprise BIA providers, as the recent acquisitions of Domo and Pyramid Analytics show.

What the market realignment means for buyers

The “Enterprise BI & Analytics Platform” category used by BARC continues to describe an important part of the market. A broader view must also include products that perform specific analytical tasks outside these platforms.

This makes it harder for companies to assess their options. Choosing software is no longer just about comparing dashboards, reports and self-service capabilities.

The question of whether traditional BIA frontends are still needed in the age of LLMs is too narrow to guide a purchasing decision. The real dilemma is that additional data and analytics offerings expand buyers’ options, but can also increase costs and complexity without delivering a matching increase in value.

Buyers therefore need to assess the value these products add to their existing software. This assessment must account for the requirements of both business users and AI agents, integration with the data architecture, and the costs of integration and operation. A broader feature set alone does not justify an additional investment.

What comes next in the series

This article launches our research series on the realignment of the BI & Analytics market. In the next article, we examine whether traditional BIA frontends have had their day in the age of LLMs. Further articles will explore the different market segments and their offerings, and outline criteria for software selection and architecture decisions.