The future of business intelligence: 10 top trends in 2026

1. Governance of BI use

More business users than ever have access to BI data, not just in static reports but as an analytics resource. That's especially true in self-service BI environments, which enable users to analyze data themselves rather than rely on skilled BI professionals to run queries for them. This increased data access is a good thing overall. But it's driving a heightened focus on data security and privacy protections, particularly with cyberattacks and regulations on data use both increasing.

In fact, data security and data governance are now the most critical concerns for many businesses when they deploy BI applications. To help address those concerns, BI vendors have incorporated compliance features for regulations such as HIPAA and GDPR into their management consoles. In addition, new technologies that help govern BI use are emerging -- analytics catalogs, for example.

Just as a data catalog provides an inventory of available data sources, an analytics catalog is a centralized application where users can find relevant BI dashboards, reports and other analytics artifacts. It also provides guidance on which ones are appropriate for their work and how to use the data appropriately. This ensures that not only data sets but the entire decision-making process driven by BI is well governed.

The growing use of AI in BI applications adds more governance challenges. When an AI tool generates an insight or a recommendation, data administrators must document the AI model that produced it, the data used to train the model, and the level of confidence in the output. Explainability and accountability are common requirements both internally and in new AI regulations. These AI-specific governance concerns are now a priority for data teams alongside their regular data governance practices.

2. Data quality management as the foundation of reliable analytics

High-quality data is essential for effective decision-making. That's certainly true in BI applications. Moreover, as AI and machine learning become mainstream components of BI initiatives, the accuracy of AI and ML models depends heavily on the quality of their source data.

As a result, organizations are investing in new tools and processes to ensure data consistency and reliability. Some of these data quality tools are now driven by AI and machine learning themselves. For example, they use predictive analytics to impute missing data values or large language models (LLMs) to ensure that product names and other values are consistent.

The increased use of data pipelines as a dynamic source of data for BI and analytics applications is another notable data quality management trend. It's often said that the best measure of data quality is whether the data is fit for purpose. But when data has diverse analytics purposes, different quality levels might be appropriate for each use case. Data pipelines offer a more adaptive, less static approach to cleansing, conforming and consolidating data than traditional extract, transform and load, or ETL, processes. That's making pipeline architectures increasingly popular.

3. AI as a tool in BI workflows

Organizations of all sizes are exploring ways to integrate generative AI (GenAI), machine learning and other AI technologies into their business operations. Increasingly, this includes incorporating AI tools into BI and analytics workflows to assist data analysts.

A common example is a chatbot that uses natural language processing (NLP) to provide a simple interface to BI data. NLP-driven natural language query and search capabilities can help users explore data more effectively. Such chatbots might also generate queries in SQL or another query language under the hood.

The following are more use cases for AI in BI applications:

• AI automates data preparation by replacing manual data cleansing, transformation and integration work with faster, more accurate processes.
• AI offers a degree of personalization, providing insights to individual users based on their role and previous interactions with data sets. For example, a GenAI tool might recommend appropriate reports or highlight specific KPIs in a dashboard.
• Companies are incorporating predictive analytics driven by machine learning algorithms into the BI process to anticipate market trends and shifts in customer behavior.

With help from AI tools, BI applications are also beginning to analyze unstructured content alongside traditional structured data. Multimodal AI software extracts insights from images, documents, audio and video that can be integrated with BI data. For example, a retailer might analyze recordings of customer service calls to augment sales data, or a manufacturer could combine images from product inspections with production-line reports. This type of analysis isn't mainstream yet, but it's no longer experimental.

4. Agentic AI in analytics applications

While regular AI tools assist analysts by automating tasks and making suggestions, agentic AI goes a step further: It analyzes data autonomously. Rather than waiting for a user to ask a question, an AI agent can monitor BI data, identify issues, formulate hypotheses, run analyses and present findings without human intervention.

For example, an agentic AI tool might notice an anomaly in supply chain data, investigate possible causes by querying related data sets and create a summary of its findings along with recommended actions.

When AI operates as an agent, it makes many analytics decisions itself. The human analyst's role shifts from directly working with the data to reviewing and acting on the agent's conclusions. Looking further into the future, there's also the potential for AI agents to initiate actions independently in operational workflows after analyzing data.

Like multimodal AI, the agentic model is still maturing, but it's rapidly becoming a defining feature of advanced BI platforms. However, its development raises questions about governance and oversight of AI agents. BI and analytics leaders need to develop best practices for validating AI-generated data analysis results before an organization acts on them.

5. Increased focus on data and AI literacy

As data becomes more essential to work throughout the enterprise, it's clear that data literacy and now AI literacy are crucial skills for decision-makers. As a result, more organizations are investing in training programs to improve these skills among all levels of employees, not just BI teams and other data specialists.

Corporate leaders often talk about the need to establish a data-driven culture. Collaboratively using BI data to support decisions across multiple teams and departments is much easier when users have a common baseline understanding of what the data means for their unit and the organization, as well as the skills needed to use it effectively.

For example, the ability to create effective data visualizations is a key component of data literacy. To simplify this, BI vendors have built guided visualization best practices into their tools. But BI leaders must ensure that users can identify appropriate uses for different types of graphics and generate visualizations that are clear and easy to understand.

AI literacy, in this context, involves understanding how to work with AI-generated data insights. That includes recognizing appropriate analytics use cases for AI tools and knowing whether to trust an AI recommendation or investigate it further.

6. Low-code and no-code application development

As business users become more data-literate, they look to use analytics tools to make data-driven decisions in various business scenarios. A common approach is to create BI applications that support decision-making for specific functions, such as finance, sales, equipment maintenance and HR.

In the past, building such applications required a team of BI developers and could be a lengthy process. Today, many BI platforms include low-code or no-code development capabilities for generating and deploying applications. These lightweight environments streamline development for BI teams. Because high-level development skills aren't required, business users can also handle some of the work themselves.

A newer capability takes this approach further. Called vibe analytics by some proponents, it lets users describe what they want to an LLM in natural language; the analytics tool then interprets the request and builds the desired visualization, dashboard or application. It's an analytics version of vibe coding in software development. Users simply explain their intent -- for example, "I want to see sales trends for the last quarter by region and product category." With this conversational method, they don't need to learn even a simplified development interface.

7. Data warehouse modernization, data lakehouses and real-time data

For many years, the data warehouse has been the backbone of BI, reporting and some forms of advanced analytics. Traditional data warehouses commonly store an extensive set of historical data on business operations structured as a logical model that BI tools can efficiently query.

However, today's BI applications place an increasing variety of demands on these enterprise data stores. Modernizing data warehouses to meet the new demands is a significant trend among companies. For starters, that includes automating and streamlining various data warehousing tasks through the use of AI, machine learning and other technologies.

Over the past 20 years, many organizations also deployed data lakes in their big data environments. These platforms are repositories of raw, unprocessed data for use primarily in data science applications. In addition, data lakes are sometimes a source for data warehouses, where the raw data is cleansed and transformed for BI uses.

More recently, the data lakehouse architecture has become popular. As the name suggests, it's a hybrid of a data lake and a data warehouse, with the flexibility of the former and the performance and data management capabilities of the latter. For BI users, a data lakehouse streamlines access to a wider range of data and enables machine learning and other advanced analytics processes to be more easily integrated into BI workflows. For BI, data science and data management teams, it provides a single data platform that supports all types of analytics applications.

Increasingly, that includes the convergence of streaming data with data lakehouses, where it's treated as a core component of the analytics environment. This convergence enables organizations to move BI applications toward a continuous intelligence model, where insights are updated on the fly as events occur and data is captured. For use cases such as fraud detection, dynamic pricing and monitoring business operations, real-time analytics in a data lakehouse delivers business value that traditional BI based on periodic data updates can't.

8. Semantic layers as infrastructure for BI and AI-driven analytics

As AI becomes more central to BI, organizations are rediscovering the value of semantic layers. A semantic layer, also sometimes called a universal semantic model, sits between raw data and the applications that consume it. This layer defines business metrics, hierarchies, relationships and rules in a consistent way for analytics tools and users.

The use of semantic layers has long been a best practice in BI, ensuring that terms such as revenue or customer count mean the same thing across different reports and dashboards. But they've become newly prominent because of AI. When an LLM generates SQL queries or answers questions about business data, it needs to understand the meaning of the data it's working with. Without a well-defined semantic layer, AI systems are prone to hallucinating metrics: They invent calculations that sound plausible but don't match how the organization actually measures performance.

BI vendors are positioning the semantic layer as foundational infrastructure for AI-enabled analytics. For example, a strong semantic model serves as the "single source of truth" that AI agents consult when analyzing data autonomously. It constrains the agent's behavior, ensuring that generated queries and calculations conform to established business definitions.

Semantic layers also address analytics consistency issues. When business definitions are codified in a shared layer, different teams can build their own reports, dashboards and BI applications with metrics that are consistent across the board. This is especially valuable in large enterprises where multiple BI tools are used. Many organizations now maintain their semantic layer as part of a data lakehouse, where it's versioned and governed alongside the analytics data.

9. Analytics as code

Analytics as code is another new development approach that blends data analysis with coding-based methodologies borrowed from software engineering.

BI tools use artifacts such as measures, dimensions and hierarchies to model data. But it's difficult to reuse these objects in different dashboards or applications. As a result, commonly used ones, such as a geographical hierarchy, must be re-created numerous times -- an error-prone and time-consuming process. Collaborating on such objects is also difficult, so individual BI analysts often develop their own.

Similar reuse and collaboration problems have already been solved in application development, where technologies that support version control and collaborative work are standard. Analytics as code brings these well-established practices into the BI world. In keeping with the low-code and no-code trend, this doesn't mean every BI or data analyst now needs to become a developer. Rather, the artifacts they create are saved as code and can then be versioned and shared among different users.

As with semantic layers, BI vendors that adopt this approach often enable the artifacts to be stored in a data lakehouse alongside the corresponding source data.

10. Expanded use of embedded analytics

Embedded analytics, which integrates BI capabilities into business applications, has become a more popular deployment option in recent years. It gives business users actionable insights within their operational workflows, without needing to switch to a separate analytics application. This helps support real-time decisions. Because data analysis features are embedded in familiar applications, it also encourages users to adopt analytics and reduces the need for specialized BI training.

An embedded analytics deployment can be as simple as an individual page in an application that contains a report or a dashboard. A more sophisticated approach embeds a data visualization directly in the operational application's UI -- right at the point of decision-making. Some embedded analytics tools go further, offering suggestions or prompts for actions to take, letting users trigger them directly from the UI and then tracking the business outcomes.

In many cases, these capabilities can be embedded in applications so naturally that users don't even know they're using sophisticated BI and analytics technology as part of their work.

BI remains important to businesses into the future

BI is well established in the enterprise -- and it isn't going away. BI applications deliver tangible and meaningful business value in organizations, and they'll continue to be a critical contributor to business success, even as AI becomes the more impactful, or at least more exciting, technology.

Editor's note: This article was updated in January 2026 for timeliness and to add new information.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.