What is an AI analytics platform?

A year ago, getting an AI agent to write SQL and answer a question about your business data was impressive. Today, frontier models are increasingly capable of real-world data analysis, and people are using tools like Claude, ChatGPT, and Cursor to investigate questions, build dashboards, and automate workflows that previously required technical expertise.

That changes what organizations should expect from their analytics tools.

People no longer want to wait for someone to build a dashboard, learn a complicated BI interface, or file a ticket every time they have a new question. They increasingly expect to ask questions, explore the answers, and build what they need in plain language.

But that creates a new challenge for data teams. When employees are analyzing CSVs in ChatGPT, building dashboards in Claude, and using coding agents to query company data, how do you ensure everyone is working from the same trusted definitions? And how do you maintain visibility into the answers and analytical work being produced?

An AI analytics platform brings those capabilities together: enabling people and agents to ask, explore, build, and automate analytical workflows, all grounded in shared business context and governance.

Instead of manually fulfilling every request, the data team's role increasingly shifts toward maintaining the data and context foundations that make trustworthy self-service possible.

What is an AI analytics platform?

An AI analytics platform is a system that uses AI agents to help people analyze data, investigate questions, build analytical tools, and automate workflows through natural language, while maintaining the governance and business context needed to produce trustworthy results.

Unlike traditional BI tools that primarily provide dashboards and predefined exploration interfaces, AI analytics platforms allow agents to perform much of the analytical work themselves.

This is part of the broader shift toward agentic analytics. In its February 2025 Market Guide for Agentic Analytics, Gartner describes agents taking on work throughout the data-to-insight process, including planning, executing, and orchestrating analytical tasks. Gartner also predicts that by 2027, half of business decisions will be augmented or automated by AI agents.

While the category is still evolving, a few capabilities distinguish an AI analytics platform from a traditional BI tool with an AI assistant:

• End-to-end agentic workflows: Agents can move from questions and follow-up analysis to dashboards, apps, and recurring tasks rather than stopping at a generated query.
• Natural-language interaction: Business users can investigate data and build what they need without learning SQL or a complex BI interface.
• Interoperability: Analytical capabilities can be accessed from external AI tools through interfaces like MCP, APIs, and CLI.
• Shared business context: Agents use trusted definitions, documentation, permissions, and other organizational knowledge to produce consistent answers.
• Continuous improvement: Data teams can observe where agents struggle and improve the context behind their answers over time.

This shift is already showing up in data-team priorities. Hex's State of Data Teams research found that AI and automation went from 4% to 27% of data leaders' top goals in just six months.

How an AI analytics platform differs from traditional BI

Traditional BI was designed primarily around dashboards and reports. Analysts and data teams prepared the data, defined metrics, built dashboards, and gave business users an interface for consuming the results.

That approach made reporting more accessible, but it only went so far.

A dashboard might show that conversion dropped eight points last week. Understanding why could still require an analyst to investigate segments, join additional data, test hypotheses, and build another report. As we've written before, dashboards were never the destination for most business questions.

AI analytics changes that model in several ways.

From predefined questions to open-ended exploration. Rather than navigating existing dashboards or learning a drag-and-drop interface, users can ask questions in natural language, follow up, and investigate new directions as they emerge.

From dashboard consumption to dashboard creation. AI agents can increasingly generate dashboards, interactive apps, and other analytical artifacts directly from a prompt. Building is no longer necessarily limited to people who know SQL or a BI tool's authoring interface.

From manual analysis to proactive work. Instead of requiring someone to check a dashboard and investigate changes, agents can perform recurring analysis and surface findings automatically.

From a single interface to an ecosystem of agents. People increasingly work with data from Claude, ChatGPT, Slack, coding agents, and other tools. An AI analytics platform needs to support those workflows rather than requiring everyone to use one dedicated interface.

Importantly, dashboards aren't going away. They remain valuable for monitoring, reporting, and sharing important metrics. What's changing is how they're created, how people interact with them, and what happens when someone has a question the dashboard wasn't designed to answer.

What can an AI analytics platform actually do?

What actually separates these platforms is how much of the analytical workflow their agents can perform, not just whether AI shows up somewhere in the product.

Ask questions and investigate data in natural language

The starting point is conversational self-service.

A business user might ask, "Why did enterprise churn increase last quarter?" An agent can identify relevant data, write and execute queries, investigate potential drivers, and explain what it found.

The user can then ask follow-up questions, change assumptions, or investigate a specific segment without starting the analysis over.

Behind the scenes, agents may execute multiple queries, inspect intermediate results, and revise their approach as new information emerges. Unlike a simple text-to-SQL interface that produces one query, a capable analytics agent can reason through a multistep investigation.

The underlying work should remain inspectable. Analysts need to be able to review generated SQL, understand which data was used, and validate the conclusions rather than taking the agent's explanation on faith.

In Hex, Threads provides this conversational experience, allowing users to move from quick questions to more involved investigations while retaining visibility into the underlying work.

Build dashboards and interactive apps from a prompt

AI is also changing who can build analytical tools.

Instead of starting with a blank dashboard canvas, users can describe what they want: a sales performance dashboard, a customer segmentation tool, a forecasting simulator, or an interactive report.

An agent can generate the underlying analysis, visualizations, layouts, and controls, then help refine the result through additional instructions.

This goes beyond AI features that simply assist with an existing dashboard builder. A code-backed analytical app can support custom calculations, sophisticated business logic, and interactions that don't fit neatly into traditional chart-and-filter interfaces.

With generative data apps, Hex users can build dashboards and interactive applications in natural language, refine individual elements, and make changes through visual controls or the underlying code.

The finished artifact also becomes a starting point for further exploration. Someone viewing a dashboard can ask a new question about the data rather than exporting it or filing another request.

Automate recurring analytical work

Many analytical workflows repeat on a predictable schedule.

Revenue teams review pipeline movement every Monday. Product teams monitor activation and retention. Finance checks performance against forecasts. Customer teams track account health and emerging risks.

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

AI agents can increasingly take on that work proactively: running an analysis, identifying meaningful changes, and delivering findings to the people who need them.

For example, Agent Tasks in Hex can schedule recurring analytical work and deliver results through Slack or email.

The result is a shift from analytics that waits to be consulted toward analytics that can perform useful work on behalf of the business.

Why an AI analytics platform needs to work with external agents

One of the biggest changes in analytics is that people aren't necessarily starting their work inside an analytics tool anymore.

Engineers use Claude Code and Cursor. Business teams ask questions in ChatGPT and Claude. Employees build automations and workflows in Slack and other AI applications.

These tools are increasingly capable of analyzing data themselves. But giving every agent direct access to the warehouse can create a fragmented environment where different tools use different definitions, generate conflicting answers, and produce work the data team can't easily inspect.

An AI analytics platform needs to serve as a governed foundation for these external workflows.

The Model Context Protocol (MCP) and programmatic interfaces such as APIs and CLIs make it possible for external agents to interact with analytical systems.

But interoperability should mean more than exposing a database connection.

Ideally, external agents can discover existing analytical assets, work with governed business context, execute analysis, and create or modify reusable work. The data team should also retain visibility into those interactions, regardless of which interface originated them.

Hex supports this through its MCP server and Hex CLI, allowing external agents to interact with governed analytical workflows and assets.

The goal is to make sure all of those tools can work from the same trusted analytical foundation, without needing to replace the tools people already use.

Why governed business context determines whether AI analytics can be trusted

The quality of an AI-generated answer depends on more than the underlying model.

An agent can write perfectly valid SQL and still return the wrong number if it doesn't understand how your organization defines revenue, which customer table is trusted, or which business rules apply.

That problem predates AI. Organizations have long struggled with competing definitions of important metrics. AI makes the consequences more significant because agents can produce convincing explanations and visualizations from those inconsistent definitions.

As frontier models become better at analytical reasoning, the context available to them becomes increasingly important.

Business context is more than a semantic model

Semantic models are useful for defining governed metrics, dimensions, and relationships. They help ensure that concepts like ARR, churn, and active customers are calculated consistently.

But they aren't the entirety of what an agent needs to understand a business.

An agent might also need documentation about a pricing change, a fiscal-calendar convention, trusted warehouse tables, instructions for interpreting a dataset, or an existing analysis that explains a known caveat.

That context may already exist in dbt, Snowflake, Databricks, GitHub, documentation, or other analytical assets.

Requiring everything to be recreated inside one proprietary semantic model can introduce significant modeling work and make the context harder to reuse across other tools and agents.

A more flexible context architecture allows different forms of business knowledge to remain where they make sense while making them available to the agents that need them.

In Hex, semantic models can be authored directly or synced from sources such as dbt MetricFlow, Cube, and Snowflake Semantic Views. Guides, Endorsements, warehouse metadata, and existing analytical assets provide additional context without requiring every instruction to be encoded into the semantic model.

This matters for both answer quality and portability. As the AI ecosystem evolves, organizations should be able to reuse their business context across tools rather than rebuilding it every time they adopt a new agent.

Context needs to improve as the business changes

Even an agent that performs well during a proof of concept won't necessarily remain accurate six months later.

Businesses change constantly. New products launch, metrics evolve, tables are deprecated, and people begin asking questions that the original context wasn't designed to support.

This creates context drift: the knowledge an agent relies on gradually becomes incomplete or outdated.

Maintaining answer quality therefore requires more than setting up a semantic model before launch or manually reviewing conversation logs when users report a problem.

Data teams need to see what people are asking, which context sources informed their answers, where agents struggle, and what needs to improve.

Context Studio provides that feedback loop in Hex. It helps data teams identify recurring warnings and context gaps, generate actionable suggestions, and evaluate whether changes improve answer quality.

Context management can also become increasingly programmatic. Through the Hex CLI, teams can use external coding agents to help inspect context issues and implement improvements across their existing development workflows.

Instead of attempting to anticipate every possible question upfront, data teams can improve context based on what users and agents actually need.

What teams are seeing in practice

This isn't just a theoretical governance problem.

Hex's State of Data Teams research found that 31% of data leaders identified trust and data accuracy as their top concern about adopting AI on company data.

The same research found teams investing in several approaches to trusted context, including organizing warehouse data, semantic models, governance tooling, and AI rules files. No single abstraction solves every context problem.

At LangChain's data team, AI analytics has changed how the entire company works with data. The team migrated off its legacy BI platform in six weeks, with Hex's agent handling roughly 90% of the dashboard rebuilding. Today, 100% of the company has access to data through Hex, and teams can build dashboards, investigate questions, and work with data without routing every request through analytics engineering.

That flexibility extends beyond Hex itself. LangChain's customer engineering team uses Claude Code to develop analyses and the Hex CLI to turn that work into shared, governed analytics. Meanwhile, the analytics engineering team uses Context Studio to see what people are asking, identify gaps in business context, and improve the definitions and guidance agents rely on.

The result is a different division of labor: business teams can take on more analytical work themselves, while the data team focuses on maintaining the trusted context that makes it possible.

How to evaluate an AI analytics platform

As more vendors introduce AI assistants, it becomes harder to distinguish between products based on feature lists alone.

A useful evaluation should focus on four capabilities:

The strongest way to test these capabilities is through a real analytical workflow rather than a polished vendor demo.

Start with a business question, follow it into a more involved investigation, turn the result into something reusable, and see whether the same context remains available when the work moves into an external agent.

Then deliberately introduce a context gap. Can the platform show why the agent struggled, help the data team fix the underlying issue, and validate whether the correction improves future answers?

The BI Buying Guide walks through this kind of evaluation in more depth, with a fuller framework for scoring vendors across these same four capabilities. Request a demo to run this evaluation against your own data.

Frequently Asked Questions

Does an AI analytics platform replace the data team?

No. It changes the work data teams spend their time on. Instead of manually answering every question or building every dashboard, teams can focus more on maintaining trusted data, governing business context, validating agent behavior, and supporting increasingly sophisticated analytical workflows.

How is an AI analytics platform different from a traditional BI tool?

Traditional BI tools primarily focus on dashboards, reporting, and predefined exploration workflows. AI analytics platforms use agents to perform more of the analytical work directly, including answering questions, investigating data, building reusable artifacts, and automating recurring tasks.

How much of the end-to-end analytical workflow a product's agents can actually perform matters more than whether it simply includes AI features.

Does an AI analytics platform need a semantic layer?

Semantic models are valuable for governed metrics and relationships, but they aren't the only source of business context an agent needs.

A capable platform should also be able to use warehouse metadata, documentation, endorsed assets, business rules, and other context. The important question is whether agents can combine those sources while maintaining consistent definitions and governance.

Can an AI analytics platform work with Claude, ChatGPT, and other AI agents?

Yes, when the platform supports external integrations through interfaces such as MCP, APIs, or CLI.

The depth of those integrations varies. Some only expose data, while others let external agents discover analytical assets, execute workflows, and create or modify reusable work while retaining access to shared context and governance.

What does implementing an AI analytics platform look like?

Start with a small set of trusted data sources and representative business questions. Establish the necessary permissions and context, then expand access as users get reliable answers.

You don't necessarily need to model every business domain before launch. Begin with endorsed tables, useful metadata, and the definitions that matter most, then deepen the context as real-world usage reveals additional needs. Hex's workspace context best practices provide a starting point.