How to evaluate AI analytics platforms

Every AI analytics vendor can show you an impressive demo. Ask a question in plain language, watch an agent write some SQL, and a few seconds later you've got a dashboard.

But what happens when you connect it to your actual data?

Your questions aren't always straightforward. Business definitions live across semantic models, documentation, and people's heads. Users ask follow-ups nobody anticipated. And when an agent gets something wrong, your data team needs to figure out why.

That's where the differences between AI analytics platforms become much more apparent.

If you're evaluating a new platform, here are four areas worth testing, with specific prompts, pass/fail criteria, and a scorecard you can use to compare vendors.

Before you start: Make your POC realistic

Choose 20–30 questions from your data team's backlog or recent stakeholder requests. Include simple lookups, ambiguous questions, and investigations requiring multiple steps.

Run the tests against your own warehouse, using existing business definitions and documentation. Include both analysts and business stakeholders, and have your own team drive the product rather than watching a vendor demo.

Most importantly, use the same questions and evaluation criteria for every vendor.

1. Agent functionality: Can users go from questions to finished work?

AI analytics should go beyond generating SQL or answering questions about an existing dashboard. Test whether someone can investigate an unfamiliar question, follow interesting leads, and build something reusable entirely in natural language.

POC test:

Start with a question and progressively increase its complexity:

• "Why did enterprise expansion revenue decline last quarter?"
• "Break that down by customer segment and region."
• "Build an interactive dashboard showing the biggest drivers, with filters for segment and region."
• "Run this investigation every Monday and send a summary of what changed to Slack."

Pass: The agent preserves context across follow-ups, performs deeper analysis when necessary, creates a shareable dashboard or app, and can schedule a fresh investigation without requiring manual BI authoring.

Fail: The agent stops at answering questions, requires users to switch tools to build something, or can only refresh an existing dashboard rather than conduct recurring analysis.

What to watch: Can a nontechnical stakeholder complete the workflow without help from an analyst?

2. Interoperability: Can external agents work with governed analytics?

Your employees already use Claude, ChatGPT, Cursor, and other AI tools. Your analytics platform should make trusted analytical capabilities available in those environments, not force everyone into another interface.

But supporting MCP doesn't necessarily mean an external agent can do everything the native agent can.

POC test:

Connect Claude or ChatGPT through the vendor's supported MCP server or API. Ask the same question in both interfaces:

• "What was our net revenue retention last quarter, broken down by customer segment?"

Compare the results, definitions, and generated logic.

Then open Cursor or Claude Code and try:

• "Create a dashboard showing weekly product adoption by customer segment and save it as a reusable project."

Pass: External agents use the same governed definitions and permissions, produce consistent results, and can create or modify persistent analytical work through supported integrations.

Fail: Integrations only retrieve answers, require rebuilding business logic, or produce inconsistent results across interfaces.

Governance check: Repeat the questions with two users who have different data permissions. Confirm that restricted information stays inaccessible, including through joins and follow-up requests.

3. Context architecture: Can you reuse the knowledge you already have?

Semantic models are useful for defining trusted metrics and relationships. But agents also need warehouse metadata, documentation, business rules, and existing analytical work to answer questions nobody anticipated.

Test whether a platform can use those sources without forcing your team to recreate everything in a proprietary modeling environment.

POC test:

First, ask a question using an existing dbt, Snowflake, or Cube semantic definition:

• "What was our gross revenue retention last quarter, using our finance team's definition?"

Then ask a question that requires additional documentation or warehouse data outside your existing semantic models:

• "Is the recent drop in activation related to the onboarding events we started tracking last month?"

Pass: The platform reuses existing definitions, draws on relevant documentation and warehouse metadata, and can investigate appropriately governed data beyond predefined semantic models.

Fail: Every metric must be recreated in a proprietary model, or the agent cannot explore a new analytical question until someone models it in advance.

What to watch: Does the agent explain which sources and definitions it used? If context is missing, does it ask for clarification or confidently invent an answer?

4. Context management: Can you improve accuracy over time?

Getting an answer right during a POC is one thing. Maintaining accuracy as hundreds of employees start asking questions is another.

Business definitions change, documentation gets outdated, and agents encounter new edge cases. Manually reviewing conversation logs isn't a realistic strategy at scale.

POC test:

Choose a question where the agent is likely to miss an important business definition:

• "How many active customers did we have last month?"

If the answer is wrong, inspect the generated logic and context to identify why.

Then update the relevant definition, documentation, or reference query and rerun the original question alongside several related questions.

Pass: Your team can inspect what happened, identify the context gap, make a reusable correction, and run evaluations to confirm accuracy improved without introducing regressions.

Fail: Monitoring consists mainly of usage logs or user feedback, with no practical way to diagnose failures, improve shared context, and verify the results.

What to watch: How much manual work is required to identify recurring problems and implement improvements? Can changes to external context sources be made programmatically?

AI analytics POC scorecard

Score each test based on what your team actually observes:

• 2 = Pass: Completes the workflow as expected.
• 1 = Partial: Works with significant limitations or manual intervention.
• 0 = Fail: Cannot complete the test.

A high score doesn't excuse critical failures. A platform that generates excellent dashboards but exposes restricted data through an external agent shouldn't pass your evaluation.

It's also worth checking the economics before signing. Ask how seats, AI credits, tokens, warehouse queries, and background compute are billed, then estimate costs using your expected production usage rather than a handful of demo prompts.

The best platform is one your team can keep improving

AI agents are getting remarkably good at analytical work. The challenge now is making those capabilities reliable and accessible across an entire company.

The best platform isn't necessarily the one that performs best in a scripted demo. It's the one that lets people investigate, build, and automate analytical work, while giving data teams the ability to maintain trust as usage grows.

That's what Hex is designed to bring together: natural-language exploration and building, interoperability with external agents through MCP and CLI, flexible business context, and tools like Context Studio to observe, evaluate, and improve answer quality.

For more guidance on evaluating your next BI investment, including migration and organizational considerations, check out our BI Buying Guide.

Frequently asked questions

How long should an AI analytics POC take?

A focused evaluation can often run in one to four weeks, depending on setup requirements and the workflows being tested. Establish your questions and acceptance criteria upfront, and give each vendor enough time to complete the same tests.

Do you need a semantic model before evaluating an AI analytics platform?

Not necessarily. Semantic models help establish trusted metrics, but agents can also work with appropriately documented warehouse data. Test both modeled and non-semantically modeled questions to understand the platform's flexibility.

Who should participate in the evaluation?

Include analysts who can verify accuracy, business stakeholders who will use the product, and someone responsible for governance. You need to establish both that the answers are trustworthy and that the experience works for the intended users.

Should AI analytics replace your existing BI tool?

It depends on what you're trying to accomplish. Some organizations start alongside existing BI, while others aim to consolidate more of their analytics stack. Use the POC to determine which workflows the new platform can replace, improve, or complement.