Business intelligence examples: How companies use BI in 2026

Business intelligence (BI) has traditionally been synonymous with dashboards and reports. Gartner describes analytics and BI as the traditional foundation for developing insights, reports, and dashboards. A data team builds a view of the business, stakeholders monitor the numbers, and when someone has a new question, they request another report or analysis.

But that's only one way BI can work.

Today, people can ask questions of their data in plain language, build interactive analytical tools, and automate recurring investigations. As frontier models become more capable of real-world data analysis, these workflows are becoming accessible to people who don't know SQL or how to build a traditional dashboard.

The goal of BI hasn't changed: helping people make better decisions with data. What's changing is how many ways people can get there, and how much they can accomplish themselves.

Here are the different forms business intelligence can take, followed by real examples of how companies are putting them to work.

The four types of business intelligence

BI isn't a single workflow. Depending on the question and who needs the answer, it can take several forms.

1. Self-service analytics

Self-service BI lets business users answer questions and explore data without waiting on the data team.

Traditionally, that meant navigating dashboards, dragging fields into charts, or using visual query builders. Increasingly, self-service analytics is becoming conversational.

Someone can ask why revenue declined, follow up by customer segment, investigate an unexpected result, and keep exploring without knowing SQL. AI agents can perform the underlying analysis while keeping the queries and business logic available for inspection.

2. Dashboards and reporting

Dashboards remain one of the most common forms of BI. They give teams a shared view of important metrics, from sales pipeline and financial performance to product engagement and customer retention.

They're particularly useful when people need to monitor the same numbers regularly or communicate results across an organization.

AI is changing how dashboards get built, too. Rather than configuring every chart and calculation manually, users can increasingly describe the report they want and have an agent generate it.

3. Interactive data apps

Some business questions need more than a dashboard.

A sales leader might want to adjust forecasting assumptions. A finance team might need to compare different spending scenarios. A product manager might want to investigate customer segments through custom controls.

Interactive data apps make sophisticated analytical logic accessible through business-friendly interfaces, allowing users to change inputs, explore scenarios, and interact with the underlying analysis.

With AI, these experiences can increasingly be generated from natural-language prompts rather than built entirely by hand.

4. Operationalized and proactive analytics

A lot of BI work is recurring. Every Monday, someone reviews pipeline movement. Every week, finance checks performance against plan. Customer teams monitor account health and potential churn.

Traditionally, someone had to open a dashboard, investigate changes, and communicate what mattered.

AI agents can increasingly perform that work proactively. Tools like Hex Agent Tasks can run recurring analyses and deliver findings through Slack or email, turning analytics into part of the business's operating rhythm.

These four types aren't mutually exclusive. A question might begin as self-service exploration, become a dashboard or interactive app, and eventually turn into an automated workflow.

Business intelligence examples in practice

The best way to understand these different approaches is to look at how companies use them to solve real problems.

1. Sales pipeline analysis: Algolia

Sales leaders need to understand why pipeline changes, not just see the latest total.

At Algolia, leadership regularly asked why open pipeline had shifted between two dates, sometimes by millions of dollars. Answering required reconciling deals that had closed, slipped, or been reassigned, a process that could take hours.

The data team built an interactive pipeline app in Hex that lets sales leadership investigate those changes themselves. What had been a recurring analyst request became a reusable workflow, cutting troubleshooting from hours to minutes and saving approximately 120 hours annually.

BI in action: Interactive data apps that let business users investigate complex pipeline changes without rebuilding the analysis each time.

2. Company-wide self-service: Parallel Web Systems

At Parallel Web Systems, employees previously relied on spreadsheets and AI-generated SQL to answer data questions, often resulting in inconsistent metrics and fragmented analytical work.

With Hex, the company established a shared foundation for trusted analytics, accessible through Slack, Claude, and other AI tools. Employees can ask questions and investigate data independently, while the data team maintains the business context and visibility needed to keep answers consistent.

Today, more than 75% of Parallel's employees self-serve analytics through Hex, up from roughly 5% previously.

BI in action: Governed conversational self-service that makes data accessible across the company without creating disconnected, unverified answers.

3. Revenue and GTM reporting: Calendly

Consistent metrics are essential when sales, marketing, and finance teams all rely on the same numbers. At Calendly, the go-to-market analytics team developed a Standardized Metric Library to address conflicting interpretations of important business metrics.

Combined with more efficient analytical workflows, that investment helped the team achieve a 2x improvement in efficiency. Instead of repeatedly reconciling different definitions, teams could spend more time analyzing performance and supporting business decisions.

BI in action: Governed dashboards and reporting built around shared metric definitions.

4. AI-native analytics and dashboard building: LangChain

Business intelligence is also changing how analytical work gets created and maintained.

At LangChain, the team migrated off its legacy BI platform in six weeks, with Hex's AI agent handling roughly 90% of the dashboard rebuilding. Today, the entire company has access to data through Hex. Teams can investigate questions, build dashboards, and create analytical tools without routing every request through analytics engineering.

LangChain's customer engineering team also uses Claude Code and the Hex CLI to bring analytical work from coding agents into shared, governed Hex projects. Meanwhile, analytics engineering uses Context Studio to identify gaps in business context and improve the foundations agents rely on.

BI in action: AI-generated dashboards, company-wide self-service, and analytical workflows that extend beyond the BI interface.

5. Marketing and sales analytics: Clay

Go-to-market teams need to understand which activities are driving results and where to focus their efforts. At Clay, Hex supports analytical workflows that help teams work with sales and marketing analytics, investigate results, and make information accessible to stakeholders.

These workflows illustrate how BI can go beyond distributing reports. Instead of waiting for a new dashboard every time a question changes, teams can explore performance and build reusable analytical views around the decisions they're making.

BI in action: Sales and marketing reporting combined with more flexible self-service exploration.

Business intelligence examples across industries

BI isn't limited to technology companies. The same principles apply wherever teams need to understand performance and make decisions from data.

Retail and supply chain: Bissell Homecare used analytics to respond to changing inventory requirements following the April 2025 tariffs. According to Supply Chain Dive, scenario analysis that previously took two to three weeks was completed in 48 hours, helping the company make faster inventory decisions.

Healthcare: Health First used real-time clinical information to improve hospital operations. According to Healthcare IT News, the organization reported eliminating 517 avoidable hospital days monthly while reducing administrative work and average length of stay.

Financial services: Banks use predictive analytics to identify suspicious transactions, prioritize fraud investigations, and reduce false-positive alerts. For example, BMO has been involved in initiatives to apply AI to financial-crime investigations, illustrating how analytics can help investigators focus on higher-risk activity.

Logistics: Estes Express used planning and optimization analytics to reduce empty miles across its transportation network. FreightWaves reported that Optym's HaulPlan reduced empty miles by 5%, while DriverPlan delivered a 75% reduction in scheduling time.

These examples illustrate a broader point: business intelligence isn't defined by the chart or interface. It's defined by the decisions and operational improvements the analysis makes possible.

How AI is changing business intelligence

The use cases above aren't new. Companies have always needed to understand revenue, forecast demand, investigate customer behavior, and monitor operations. What's changing is who can perform that work and how quickly.

A business user who once needed an analyst to build a report can increasingly ask the question themselves. A dashboard that once took days to create can be generated from a prompt. A recurring analysis that required someone to manually investigate the numbers can become an automated task.

But there's an important trade-off. When everyone can analyze data and generate dashboards, it's easier to end up with inconsistent metrics, unverified answers, and analytical work scattered across individual chat sessions.

That's why modern BI needs more than capable AI models. It needs shared business context, governed data access, and a way to inspect and improve the work agents produce.

In Hex, semantic models, Guides, Endorsements, and warehouse metadata help ground AI-generated answers in trusted business definitions. Context Studio gives data teams visibility into what people are asking and where the underlying context needs improvement.

This need for trust is reflected in Hex's State of Data Teams research, where 31% of data leaders identified trust and data accuracy as their top concern about adopting AI on company data.

The goal is to make self-service, dashboards, apps, and automated analysis more accessible without creating disconnected versions of the truth.

What makes a business intelligence example successful?

Whether the output is a dashboard, a conversational answer, or an interactive app, the most useful BI workflows have a few things in common.

• A clear business question: The analysis starts with a decision someone needs to make, not simply a chart someone wants to build.
• Trusted data and definitions: Everyone can understand where the numbers came from and how they were calculated.
• Reusable work: An answer can become a dashboard, app, or recurring workflow rather than disappearing into a one-off analysis.
• Business-user autonomy: The people closest to the decision can investigate and act without depending on the data team for every follow-up.

As AI expands what's possible, these foundations become more important, not less.

The best BI examples aren't necessarily the most sophisticated dashboards. They're the ones that help people get trusted answers and do something useful with them.

Request a demo to see how Hex supports these workflows, or start with our BI buying guide.

Frequently Asked Questions

What are common examples of business intelligence?

Common examples include sales pipeline reporting, financial dashboards, marketing attribution, customer churn analysis, inventory forecasting, and operational performance monitoring.

Modern BI also includes conversational self-service, interactive analytical apps, and automated workflows that investigate and surface important changes.

What are the four types of business intelligence?

A useful way to categorize modern BI is conversational self-service, dashboards and reporting, interactive data apps, and operationalized or proactive analytics.

These aren't mutually exclusive. An analysis can move between them as the business need evolves.

How do different departments use business intelligence?

Sales teams use BI to analyze pipeline and forecasting. Marketing teams measure campaign performance and attribution. Finance monitors revenue, spending, and budgets. Product teams investigate engagement and retention, while customer success teams monitor account health and churn risk.

The underlying goal is the same: helping people make better decisions with data.

How is AI changing business intelligence?

AI is making more analytical work accessible through natural language. Users can increasingly ask questions, investigate results, build dashboards and apps, and automate recurring analysis without needing to write SQL or learn a traditional BI authoring interface.

The challenge is maintaining consistent metrics, permissions, and governance as more people and agents work with company data.

Do you need a data warehouse for business intelligence?

Not always. BI tools can connect directly to databases, spreadsheets, and business applications.

For larger organizations, a centralized warehouse such as Snowflake, Databricks, or BigQuery often provides a more scalable foundation for combining data, maintaining consistent definitions, and supporting governed analytics.