What is business intelligence reporting

Business intelligence (BI) reporting has traditionally followed a familiar pattern. A data team builds dashboards and reports, business stakeholders monitor the numbers, and when someone needs a new breakdown or wants to understand why something changed, they submit another request.

The result is often a growing library of dashboards, recurring reporting work, and a backlog of questions the data team struggles to keep up with. But AI is changing that model.

Today, business users can increasingly ask questions in plain language, investigate results, build custom dashboards and reports, and automate recurring analysis without learning a BI interface or waiting on an analyst.

The purpose of business intelligence reporting hasn't changed: helping people make better decisions with data. What's changing is how reports get created, how people interact with them, and how much analytical work can happen without the data team in the loop.

What is business intelligence reporting?

Business intelligence reporting is the process of collecting, analyzing, and presenting business data in a format that helps people understand performance and make decisions.

Reports typically bring together data from systems like customer relationship management (CRM) platforms, billing systems, and product databases. That data is often consolidated in a warehouse such as Snowflake, Databricks, or BigQuery, where data teams prepare it for analysis and establish consistent definitions for important metrics.

A BI platform then makes that information accessible through reports, dashboards, and other analytical experiences.

For example, a monthly sales report might show revenue by region, performance against targets, and changes from the previous month. A finance report might compare actual spending against budget, while an operations report could track overdue invoices or inventory levels.

Traditionally, these outputs were built and maintained by analysts. Business users could consume them, adjust filters, or request additional analysis when the existing report didn't answer their question.

Reporting and analytics are closely related, but there's a useful distinction. Reporting primarily communicates what happened, while analytics investigates why it happened and what might happen next. Thomas H. Davenport has explored this distinction in MIT Sloan Management Review.

Increasingly, AI is bringing these workflows together. Someone can start with a report showing declining sales, investigate the cause conversationally, and turn the resulting analysis into a new report or interactive application.

Four common types of BI reports

Organizations use different types of BI reports depending on the decisions they need to make, how often they need updated information, and who is doing the analysis.

1. Operational reporting

Operational reports help teams monitor day-to-day business activity and make decisions based on current performance.

Examples include daily sales reports, inventory availability, customer support volumes, and accounts payable aging reports.

An accounts payable manager, for example, might review invoices overdue by 30, 60, and 90 days to prioritize collections and identify potential cash flow problems.

These reports typically refresh frequently and focus on metrics that need regular attention.

2. Strategic and executive reporting

Strategic reports help leaders understand overall business performance and progress toward longer-term goals. Examples include quarterly board reports, executive scorecards, financial performance reviews, and company-wide KPI reporting.

A leadership team might review revenue growth, profitability, customer retention, and progress against annual targets in a monthly executive report. As TDWI's research on business intelligence has explored, strategic BI helps organizations connect performance measurement to broader business objectives.

These reports emphasize consistency and comparability over time, since leaders need confidence that the numbers they're reviewing follow agreed definitions.

3. Ad hoc reporting

Ad hoc reports answer specific questions that weren't anticipated when existing reports or dashboards were built.

A sales director might ask why open pipeline changed by several million dollars between two dates. A marketing leader might want to understand how campaign performance differed by customer segment.

Traditionally, those questions often become tickets for the data team. An analyst writes a query, prepares the report, and sends it back. AI is changing this workflow by letting people ask these questions in natural language and investigate the results themselves.

4. Self-service reporting

Self-service reporting gives business users the ability to create or customize reports without needing an analyst to do it for them.

Traditional self-service BI relied on drag-and-drop builders, spreadsheets, and visual query interfaces. These made report creation more accessible, but still required people to learn how to navigate datasets, select fields, and configure visualizations.

Self-service conversational anaytics removes much of that complexity.

Instead of manually assembling a report, someone can ask for revenue by region, follow up by customer segment, and generate a visualization or report through natural language.

These categories aren't mutually exclusive. An executive report might be scheduled, while also allowing leaders to investigate specific metrics through self-service exploration.

Why traditional BI reporting breaks down

Traditional BI reporting works well when the questions are predictable.

A finance team needs the same monthly close report. Sales leadership reviews pipeline every Monday. Executives monitor a consistent set of KPIs. The challenges appear when the next question falls outside what someone already built.

A dashboard might show that conversion declined, but not whether the decline was driven by customer mix, a pricing change, or a particular acquisition channel. Someone has to investigate.

Historically, that meant returning to the data team. As those requests accumulate, data teams can find themselves spending more time building reports and responding to follow-ups than doing deeper analytical work.

Meanwhile, business users often find workarounds. They export data into spreadsheets, build their own calculations, or increasingly analyze company data in ChatGPT and Claude.

Those workflows can be faster and more flexible, but they introduce other problems. Different teams may calculate the same metric differently. Analyses become scattered across individual files and chat conversations. And the data team often has little visibility into what people are asking, how answers are generated, or whether they're accurate.

The problem isn't that dashboards and reports are no longer useful. It's that reports alone can't anticipate every question people will ask about their business.

And in an AI-first world, people increasingly expect more than a predefined collection of reports.

How AI is changing business intelligence reporting

AI is changing BI reporting in several important ways, from how people investigate reports to how they build and automate them. The shift is already underway. In Hex's State of Data Teams research, the share of data leaders naming AI as a top priority increased from 4% in Spring 2025 to 27% by December 2025.

As frontier models improve at real-world analytical tasks, more of the reporting workflow can happen through natural language.

Ask questions and investigate reports conversationally

The first major change is that people no longer need to rely exclusively on the reports and exploration paths someone else built. Imagine a sales leader reviewing a dashboard that shows pipeline declining. Instead of submitting a request to the data team, they can ask:

"Why did enterprise pipeline fall last month?"

Then follow up:

"Was that mostly existing customers or new business?"

"Which regions contributed most to the decline?"

"How does that compare with the previous quarter?"

The AI agent can investigate those questions, generate SQL, execute the analysis, and return results in the same conversation.

With Hex Threads, users can explore their warehouse data conversationally while retaining visibility into the queries and business logic behind the answers.

The important distinction is that reporting no longer has to end with the information already displayed. A report can become the starting point for a deeper investigation.

Build dashboards and reports with AI

AI is also changing who can create reports in the first place. Traditionally, producing a new report required someone to select data sources, configure calculations, build charts, format the layout, and publish the result.

Modern AI analytics platforms increasingly let users describe what they want and have an agent build it. Someone might ask for a sales performance dashboard showing actual versus forecasted revenue, with controls for region, product, and time period.

Rather than manually assembling the report, the agent can generate the underlying analysis, visualizations, and interactive experience. Hex Generative Apps takes this further by allowing teams to build interactive, code-backed data applications through natural language.

That matters because not every reporting requirement fits a static dashboard. Finance teams might need to adjust forecasting assumptions. Sales leaders might want to investigate different pipeline scenarios. Customer teams may need custom reporting experiences for individual accounts.

AI makes it easier to create those experiences without every new request becoming a substantial development project.

Turn recurring reporting into reusable workflows

A useful report becomes more valuable when other people can reuse it.

At EliseAI, preparing quarterly business review (QBR) decks was a recurring challenge. The process required roughly two days of work, in part because non-technical employees depended on pre-built dashboards and data-team support.

The team built an interactive QBR application in Hex that produces a customized deck in just three clicks, reducing preparation time by 97%. Instead of repeatedly assembling the same analysis, customer-facing teams could generate the reporting they needed through a reusable application.

As analytics engineer Hannah Lee described the shift, requests moved away from the data team's queue and toward requests for access to Hex. This is an important distinction between generating a one-off answer and creating something the organization can keep using.

The report becomes a shared analytical asset rather than another file someone needs to recreate next quarter.

Automate reporting and recurring analysis with agents

AI is also making reporting more proactive.

Traditionally, automated reporting meant refreshing a dashboard or sending a predefined report on a schedule. Someone still needed to open it, interpret the changes, and investigate anything unexpected. Agents can increasingly perform more of that analytical work themselves.

For example, a sales leader might want a weekly pipeline summary that doesn't just list the latest numbers, but also investigates what changed, identifies unusual movements, and highlights deals that deserve attention. With Hex Agent Tasks, teams can schedule recurring analyses and deliver results through Slack or email.

Similar workflows could include weekly customer retention reviews, daily product activation analysis, or monthly budget variance investigations. The shift is from automatically distributing information to automatically performing more of the analysis behind the report.

That doesn't eliminate dashboards or scheduled reporting. It expands what's possible when teams want more than a refreshed set of numbers.

What makes AI-generated reporting trustworthy?

Making reports easier to generate introduces an important challenge: how do you ensure the results are accurate, consistent, and governed?

This matters because generating plausible-looking analysis is different from understanding a business. An AI agent might know how to write SQL but not how your company defines revenue, which tables are approved for reporting, or how refunds should be handled.

In Hex's State of Data Teams research, 31% of data leaders cited data quality and lack of trust as their biggest barrier to AI adoption.

Three capabilities are particularly important.

Shared business context. AI agents need access to trusted metric definitions, warehouse metadata, documentation, and other business knowledge. Semantic models can help establish consistent calculations, but they aren't the only useful source of context. Hex supports existing semantic models from tools like dbt MetricFlow, Cube, and Snowflake alongside Guides, Endorsements, and other context.

Inspectable answers. Users and analysts should be able to understand how a report was generated, including the underlying queries, data sources, and calculations. This makes it possible to investigate incorrect results rather than treating AI-generated numbers as a black box.

Continuous improvement. Business definitions change, new questions emerge, and context becomes outdated. Manually reviewing every AI-generated answer isn't realistic at scale. Context Studio helps data teams monitor questions, identify missing or unclear context, and evaluate improvements over time.

These capabilities help ensure that as more people create reports and investigate data independently, the organization isn't left with dozens of conflicting versions of the truth.

For high-stakes reporting, such as financial statements and board materials, human review and reconciliation remain important regardless of whether the report was created by an analyst or an agent.

What to look for in a BI reporting tool

The right reporting platform depends on your organization's needs, but AI is changing which capabilities deserve the most attention.

Traditional requirements like dashboards, scheduling, connectivity, performance, and permissions still matter. Increasingly, though, teams should evaluate how much more people can accomplish beyond consuming reports.

Consider these questions:

• Reporting and visualization: Can the platform support the dashboards, scheduled reports, and executive reporting your organization depends on?
• Conversational self-service: Can business users ask questions, investigate results, and follow up entirely in natural language?
• AI-powered building: Can users generate dashboards, reports, and interactive apps from prompts, or does AI only assist with traditional report-building interfaces?
• Automated analysis: Can recurring reporting workflows become proactive agent-driven investigations rather than just scheduled dashboard refreshes?
• Trust and governance: Can the platform reuse your existing business context, expose generated logic, enforce permissions, and help your data team improve answer quality?
• Integrations and cost: Can analytics extend into tools like Slack, Claude, and ChatGPT while retaining governance? How do seat pricing, warehouse compute, and AI usage affect costs as adoption grows?

A useful evaluation should test real reporting workflows rather than relying exclusively on a vendor demo.

Start with a report your organization already uses, ask a question it wasn't designed to answer, follow the investigation somewhere unexpected, and see whether you can turn the result into something reusable.

Then inspect the logic and context behind the answer.

That's a better measure of what an AI-native reporting platform can actually deliver than simply checking whether it has a chatbot.

Business intelligence reporting in the AI era

Business intelligence reporting isn't going away. Companies will continue to need financial reports, executive dashboards, operational scorecards, and other standardized ways to communicate performance.

What's changing is everything that happens around those reports.

People can increasingly investigate the questions reports raise, build new analytical experiences through natural language, and automate recurring work that previously required manual analysis.

Hex brings those workflows together in one governed analytics platform. Business users can ask questions and explore data through Threads, build dashboards and interactive apps with AI, and automate recurring analysis through Agent Tasks. The data team maintains visibility and control through shared context and Context Studio.

The result is a reporting environment that does more than distribute numbers. It helps more people understand what those numbers mean and act on them.

Request a demo to see how Hex can help your team move beyond traditional BI reporting, or start with our BI buying guide.

Frequently Asked Questions

What's the difference between a BI report and a dashboard?

A BI report generally presents detailed information about business performance, often for a particular period, audience, or decision. Reports may contain multiple pages, tables, explanations, and supporting analysis.

A dashboard typically provides a more consolidated, visual view of important metrics for ongoing monitoring. Both can be interactive, scheduled, and governed. The main difference is how the information is organized and consumed, rather than a strict technical boundary.

Can AI-generated reports be trusted for executive reporting?

Yes, provided they meet the same accuracy, governance, and review standards as reports built manually.

Organizations should ensure AI-generated reports use approved data sources and metric definitions, expose the underlying calculations, and enforce relevant permissions. For board reporting, financial statements, or other high-stakes decisions, analysts should validate important results before publication.

Do we need a semantic model before using AI for BI reporting?

Not necessarily. Semantic models are valuable for maintaining consistent metric definitions, relationships, and calculations, especially when many people or applications rely on the same KPIs.

But useful business context also exists in warehouse metadata, documentation, existing SQL, and endorsed datasets. Platforms like Hex can draw on these different sources of context, allowing teams to start with what they already maintain and introduce more formal semantic modeling where it's useful.

Will AI replace traditional dashboards and scheduled reports?

Not entirely. Dashboards and scheduled reports remain useful when teams need to monitor consistent metrics or distribute standardized information regularly.

AI expands those workflows by helping people investigate unexpected results, build new analytical experiences, and automate analysis that previously required manual intervention.

Rather than replacing every dashboard, organizations can use AI to reduce the need to build a new dashboard for every question.