How to choose a data platform for analytics and AI

The way people work with data is changing quickly.

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 Claude, ChatGPT, Cursor, and other AI tools to investigate questions, build dashboards, and automate workflows that previously required an analyst.

That changes what organizations should expect from their analytics platform, and it's exactly why so many teams are still evaluating AI tools the wrong way.

It's no longer enough to bolt a chatbot onto an existing BI tool or give analysts an AI assistant that helps them write SQL. People increasingly expect to ask questions, follow up, build what they need, and work with data wherever they already use AI.

But that creates a challenge for data teams. How do you enable all of that without ending up with different agents using different business definitions, dashboards scattered across local machines, and answers nobody can reliably inspect or trust?

In our State of Data Teams report, 31% of data leaders named data quality and lack of trust as their top concern about adopting AI on their data, nearly twice as often as any other barrier.

The next generation of analytics platforms needs to solve both sides of this problem: give people the flexibility to do more with AI, while providing the shared context and governance that makes the results trustworthy.

Here are four criteria worth prioritizing when evaluating an AI analytics platform in 2026.

1. Agent functionality: How much can you actually accomplish with AI?

Most analytics vendors now offer some form of AI. But there's a substantial difference between an assistant that generates SQL or configures a chart and an agent that can carry out an entire analytical workflow.

The real test is how far a user can get in natural language.

Can someone start by asking why revenue dropped, investigate the drivers through follow-up questions, and turn that work into a dashboard or interactive app? Can they then refine the result, share it with their team, and automate the analysis so it runs again next week?

And can the same agentic experience support different kinds of analytical work, from quick business questions and executive reporting to forecasting, statistical analysis, and more sophisticated workflows?

What to evaluate:

• End-to-end workflows: Can agents move from questions to exploration, building, and reusable artifacts without forcing users into separate tools?
• Breadth of capabilities: Can they support self-service BI, dashboards, interactive apps, and more sophisticated analysis, including SQL and Python when needed?
• Conversational continuity: Can users ask follow-up questions, iterate on generated work, and continue exploring from an existing dashboard or app?
• Proactive work: Can agents perform recurring analysis, monitor changes, and deliver results without someone initiating every question?
• Inspectability: Can users review the underlying logic and make changes when the agent doesn't get something right?

This distinction matters because AI features scattered across different interfaces don't necessarily add up to a unified agentic experience. A chatbot might answer questions, a copilot might help build dashboards, and another assistant might write Python, but the user can still end up restarting the work each time they move between them.

In Hex, for example, Threads supports conversational analytics, generative data apps can turn a prompt into a reusable dashboard or interactive application, and Agent Tasks can automate recurring analytical work. Users can also continue asking questions from published apps, while more sophisticated logic remains accessible underneath.

What actually matters is how much useful analytical work people can accomplish with it, from beginning to end.

2. Interoperability: Can your analytics platform work where your company does?

An increasingly agent-first world changes another assumption behind traditional analytics platforms: that users will log into a dedicated interface whenever they want to work with data.

Your engineers are already using Claude Code and Cursor. Business teams are asking questions in ChatGPT and Claude. People are building workflows in Slack, coding agents, and other tools that will continue evolving.

You can't realistically expect all of that activity to move into one analytics interface.

Instead, your analytics platform needs to work with the broader AI ecosystem, giving external agents access to governed analytical capabilities without forcing people to abandon the tools they prefer.

This is where interoperability and headless access become important.

Protocols like Model Context Protocol (MCP) and programmatic interfaces such as APIs and CLIs make it possible for external agents to interact with other systems. MCPs with Hex breaks down how that works in practice. But simply exposing a database connection isn't the same as making your analytics platform available to an agent.

What to evaluate:

• External agent access: Can Claude, ChatGPT, Cursor, Codex, and other tools interact with the analytics platform through MCP, APIs, or CLI?
• Depth of functionality: Can external agents discover existing analytical assets, run investigations, and create or modify reusable work, or are they limited to retrieving data?
• Shared governance: Do external agents inherit the same permissions, trusted definitions, and business context as users working inside the platform?
• Centralized visibility: Can the data team inspect what external agents are asking and producing, or does that activity become another ungoverned channel?

That last point is particularly important.

When someone exports a CSV into ChatGPT or builds a dashboard locally with a coding agent, the work may be useful, but the data team often loses visibility into the logic, context, and answers being produced. Two employees can arrive at different numbers without realizing they're working from different assumptions.

An interoperable analytics platform should make those workflows more powerful without allowing them to become disconnected from the company's governed data foundation.

Hex, for example, provides an MCP server and Hex CLI that allow external agents to work with governed analytics. The same context can support questions originating inside Hex, Slack, or other AI environments.

A genuinely interoperable platform gives the agents people already use a shared, governed foundation for working with company data, instead of trying to replace them.

3. Context architecture: How do agents know what to trust?

Even the most capable AI agent can't reliably analyze your business if it doesn't understand how your organization defines its metrics, which data sources are trustworthy, or what business rules should apply.

And as frontier models improve, the context surrounding them becomes an increasingly important determinant of answer quality.

The traditional approach to governing BI has centered heavily on semantic models: defining metrics, dimensions, and relationships in advance so people can consistently query them.

Semantic models remain valuable, particularly for important metrics that need to compute consistently. But they aren't the only context an AI agent needs.

An agent investigating why revenue declined might need a governed revenue metric, but it could also need documentation about a recent pricing change, a trusted customer segmentation table, a fiscal-calendar convention, and an existing analysis explaining how refunds are handled.

That information may already exist across your warehouse, dbt project, GitHub repositories, documentation, and analytical assets.

Requiring the data team to recreate all of it inside a proprietary semantic model can create a substantial upfront modeling burden, ongoing maintenance, and unnecessary vendor lock-in.

It can also constrain what agents are capable of answering. As frontier models become better at complex analytical tasks, restricting them exclusively to predefined metrics and relationships can leave much of that capability unused.

What to evaluate:

• Multiple forms of context: Can agents use semantic models alongside warehouse metadata, documentation, Markdown guides, code, endorsed assets, and other business knowledge?
• Existing investments: Can the platform reuse context already maintained in dbt, Snowflake, Databricks, GitHub, or other systems without requiring teams to rebuild it?
• Portability: Can context be synced into and out of the platform, or does it become locked inside a proprietary modeling language?
• Open-ended reasoning: Can agents explore questions beyond predefined semantic paths while still respecting trusted definitions and permissions?
• Consistency: Can all analytical workflows and external agents draw from the same governed context rather than maintaining their own copies?

Portability is especially important as AI becomes more distributed.

The 2026 State of Data Teams report found that adoption of standalone semantic layers had increased substantially as teams sought to avoid vendor lock-in. The same concern now extends beyond metrics to the broader context that agents rely on.

A platform that requires every instruction and business definition to live inside its own proprietary semantic layer can make changing BI tools or adopting new agents unnecessarily expensive.

Hex takes a more flexible approach through Context Management. Semantic models can be authored in Hex or synced from systems like dbt MetricFlow, Cube, and Snowflake Semantic Views, while Guides, Endorsements, warehouse metadata, and existing analytical assets provide additional context. Guides can also be maintained as files outside Hex and synced into the platform.

The goal is to let business knowledge live where it makes sense, rather than forcing everything into one abstraction.

A good context architecture should make agents more capable without making your organization more dependent on one vendor.

4. Context management: How do you keep agents accurate as your business changes?

Even if an agent produces accurate answers during a proof of concept, that doesn't mean those answers will remain accurate six months later.

Businesses change constantly. Metrics are redefined, tables are deprecated, new products launch, organizational structures shift, and people begin asking questions the original context wasn't designed to support.

This creates a problem known as context drift: the knowledge agents rely on gradually becomes incomplete or outdated relative to the business.

Traditional approaches to AI quality often involve reviewing conversation logs, collecting user feedback, and manually updating instructions when something goes wrong.

That might work when a few people are testing an agent. It becomes unrealistic when hundreds of employees are asking thousands of questions across multiple interfaces.

And the more places agents operate, the harder it becomes to maintain answer quality without a centralized way to observe what's happening.

What to evaluate:

• Observability: Can the data team see what people and agents are asking across every interface, including external agents, and inspect which context informed each answer?
• Diagnosis: Does the platform surface recurring warnings, failed questions, and missing context without requiring manual transcript review?
• Suggestions: Can it turn those signals into actionable recommendations for improving semantic models, metadata, Guides, or other context sources?
• Evaluations: Can teams test whether context changes improve answer quality before deploying them?
• Programmatic management: Can external agents use APIs or CLI tools to inspect context issues, implement improvements, and manage the system at scale?

The last point is increasingly important.

Context management shouldn't be limited to clicking through an administrative interface. As coding agents become more capable, data teams should be able to use them to help maintain the context infrastructure itself.

For example, an agent working in Claude Code could inspect suggested improvements, update a Markdown guide or dbt definition, and help implement the change through an existing development workflow.

Hex Context Studio is designed around this feedback loop. It gives data teams visibility into agent activity, groups recurring problems into actionable suggestions, and supports evaluations to test whether changes improve answer quality. Hex's CLI also makes context-management workflows accessible programmatically, allowing teams to use external coding agents to help maintain and improve their analytics environment.

The result is a different operating model for the data team.

Instead of trying to anticipate every question before launching AI analytics, teams can start with useful context, observe where agents struggle, and continuously improve the foundation as adoption grows.

Keeping AI analytics trustworthy takes tooling built for the long haul, maintained continuously rather than configured once.

Don't forget total cost of ownership

The four criteria above determine what an AI analytics platform can actually deliver. But teams should also evaluate what it takes to deploy and maintain that platform at scale.

The platform's license price is only one part of the equation.

Consider the additional products and infrastructure a deployment may require: a separate semantic layer, observability tooling, warehouse compute, AI inference, additional authoring environments, and the engineering time needed to maintain integrations and context.

A platform with inexpensive seats can become much more expensive if it requires several other tools to deliver the complete workflow.

There are also switching costs. If metrics, AI instructions, and business logic are locked into proprietary formats, moving to another platform can mean rebuilding much of your context infrastructure.

Compare total cost across the complete stack, not just the initial license quote. And consider whether the platform can consolidate workflows that currently require separate BI, notebook, AI chat, and context-management tools.

How to compare AI analytics platforms in a proof of concept

The best way to evaluate these criteria is to test them against real questions, workflows, and business context rather than a vendor's polished demo environment.

Use the same scenarios across every shortlisted platform.

It's worth including both business users and data practitioners in the evaluation. Business users should be able to complete useful workflows in natural language, while the data team should be able to inspect the underlying work and maintain the context that makes those answers trustworthy.

Also test the workflows across interfaces. An agent that works well inside the vendor's application but loses its context when called from Slack or Claude may create exactly the fragmentation you're trying to avoid.

Look past the flashiest demo answer. The strongest platform supports the full range of analytical work your organization needs, wherever that work happens, with consistent context and governance underneath.

The right AI analytics platform should work across your entire AI ecosystem

Choosing an AI analytics platform in 2026 is increasingly about more than selecting a better interface for analyzing data.

You're choosing the foundation that people and agents across your organization will use to ask questions, investigate problems, build analytical tools, and automate work.

That foundation needs to be flexible enough to support sophisticated workflows, interoperable enough to work with the agents people already use, and governed by context that can evolve alongside the business.

Employees don't need to use the same analytics interface. They need confidence that wherever they choose to work with data, they're building on the same trusted foundation.

When you're ready, request a demo to see agentic analytics, interoperable workflows, and governed context work together on your own data.

Frequently asked questions

What's the difference between an AI-native BI platform and a traditional BI tool?

Traditional BI tools primarily focus on dashboards, reporting, and predefined analytical workflows. AI-native platforms put natural language on top of those same dashboards and reports, letting users ask questions, investigate data, build reusable analytical artifacts, and automate work through conversation instead of configuration.

The distinction is less about whether a product has AI features and more about how much of the analytical workflow those agents can perform.

Why does interoperability matter when choosing an AI analytics platform?

Employees increasingly work with data through external agents such as Claude, ChatGPT, Cursor, and Codex. An interoperable analytics platform lets those agents access governed analytical capabilities and shared business context rather than requiring users to move into a separate application or export data into disconnected workflows.

Do AI analytics platforms require a semantic layer?

Not necessarily. Semantic models are valuable for governed metrics and relationships, but AI agents can also use warehouse metadata, documentation, endorsed data sources, Guides, and other forms of business context.

The important question is whether the platform can use those sources together while preserving consistent definitions and governance.

How can you evaluate whether an AI analytics platform understands your business context?

Test it with questions that require company-specific definitions, terminology, and business rules. Inspect the SQL and context used to produce each answer, then introduce a missing definition and see whether the platform can correct it in a reusable way.

A strong evaluation should test not only initial accuracy, but also whether the platform can identify and improve context gaps over time.

What should be included in the total cost of an AI analytics platform?

Include licensing, warehouse compute, AI inference, semantic modeling, observability, additional analytical tools, implementation effort, and ongoing maintenance. Also consider switching costs associated with proprietary context or semantic models.

The relevant comparison is the total cost of supporting the organization's analytics and AI workflows, not just the advertised seat price.