What to Ask Vendors Who Claim to Be AI-Native

Almost every BI and analytics vendor now says it is AI-native. Some were built around AI from day one. Others added a chat box to a product designed a decade ago. From the outside, the demos can look the same.

The difference shows up after you buy: in how accurate the answers are, who can actually use the product, what it costs as usage grows, and whether your security team signs off. If you want the background on the category first, start with our guide to AI-native BI.

This checklist is for analysts, business users and data leaders evaluating AI-native business intelligence. Each question includes why it matters and what a good answer sounds like. Use it in demos, RFPs and security reviews.

Architecture: was AI built in or bolted on?

1. If you removed the AI, what would be left?

Why it matters: In a bolted-on product, AI is a feature layered over a traditional dashboard tool. In an AI-native product, AI shapes the core workflow: how data is explored, how analysis is built, and how insights are surfaced.

A good answer: The vendor can explain how AI is involved from connecting data to sharing results, not just in one chat panel. We wrote more about this distinction in AI Native vs. AI Bolted On.

2. Does the AI produce real, editable analysis or just a text answer?

Why it matters: A paragraph of text is hard to verify, reuse or share. Analysts need output they can inspect, adjust and build on.

A good answer: AI output becomes standard artifacts such as charts, tables and dashboards that people can edit, save and share like anything else in the product.

3. Can the AI proactively surface insights, or does it only answer what you ask?

Why it matters: Many important findings are things nobody thought to ask about. A product that only responds to prompts leaves that value on the table.

A good answer: The vendor can show the product finding trends, outliers or drivers on its own, and explain how those findings are generated and checked.

Semantic layer and accuracy

4. How does the AI know what your business terms mean?

Why it matters: "Revenue," "active customer" and "churn" mean different things at different companies. Without business context, AI will confidently produce plausible but wrong numbers.

A good answer: A clear description of how metric definitions, relationships and business context are captured, who maintains them, and how the AI uses them when generating queries.

5. If we import an existing semantic model, exactly what carries over?

Why it matters: "We support your semantic layer" can mean anything from full fidelity to reading a list of field names. Joins, calculated metrics and filters may or may not come along.

A good answer: A specific, honest list of what is imported and applied, what is imported but not yet applied, and what you will need to recreate. Ask to see it working on your own model.

6. How can someone check whether an answer is right?

Why it matters: Accuracy claims in a demo prove little. What matters is whether your team can verify an answer before acting on it.

A good answer: People who build analysis can see the underlying SQL or logic. The vendor can explain how it handles ambiguous questions, for example by asking for clarification instead of guessing. Be wary of accuracy percentages that come without a methodology.

Trust, governance and security

7. Do you train models on our data?

Why it matters: Your data may include customer, financial or employee information. You need to know whether it could influence a model that serves other customers.

A good answer: A plain "no," backed by contract language, plus a clear list of which model providers are involved and what data is sent to them.

8. Where are you in security certification, and how is data protected?

Why it matters: Your security team will ask, and vague answers slow down procurement.

A good answer: A precise status, such as "SOC 2 Type II complete" or "pursuing SOC 2 Type II," not a fuzzy "SOC 2 compliant." Encryption should be explained in plain English: data is encrypted while it travels over the network and while it is stored.

9. Who sees what, and do permissions follow the user?

Why it matters: AI makes it easy to ask about anything. Without the right controls, it can also make it easy to see things people should not.

A good answer: Clear roles that separate people who build analysis from people who consume it, and an explanation of how existing warehouse permissions are respected. Ask for a demo of two different users asking the same question.

Data stays in your warehouse

10. Do you copy our data, or query it where it lives?

Why it matters: Copying data into a vendor's store creates another place to secure, another refresh to manage and another version of the truth.

A good answer: The product queries your cloud warehouse directly, and the vendor is specific about what, if anything, is cached or stored on its side and for how long.

11. Whose credentials are used when the AI runs a query?

Why it matters: A single shared service account can quietly bypass the row- and column-level security your data team has set up.

A good answer: Where the warehouse supports it, users query as themselves, so the warehouse's own permissions apply to every AI-generated query.

Pricing for AI usage

12. How do you charge for AI, and what happens when usage grows?

Why it matters: Token, credit or query-based pricing puts a price tag on every question. Teams start rationing curiosity, which defeats the purpose of AI analytics.

A good answer: A pricing model you can forecast, with no surprise overages for asking more questions. Ask for a worked example at two or three times your expected usage. (Here is why Golden doesn't charge for tokens.)

13. What is included in each plan, and what costs extra?

Why it matters: AI features are sometimes gated behind add-ons or the top tier, which changes the real price considerably.

A good answer: Public, per-user pricing that states which AI capabilities are in each plan. Compare it against your shortlist on a like-for-like basis; see Golden's pricing as one reference point.

Who can actually use it

14. Can a business user get value without an analyst in the room?

Why it matters: The promise of AI-native BI is broader access. If only analysts can drive the product, you have bought a faster tool for the same small group.

A good answer: Let a non-technical colleague use the trial unassisted for an afternoon. Watch where they get stuck.

15. What does an analyst gain, not just a business user?

Why it matters: Tools that only simplify can frustrate experts, who then go back to their old stack.

A good answer: Analysts get speed plus control: access to the generated logic, the ability to refine it, a wide range of visualizations, and a way to publish trusted analysis for others.

How Golden answers these questions

We built Golden as an AI-native BI product for cloud warehouses, so we try to hold ourselves to this list. Golden queries your warehouse directly, and BigQuery and Redshift users query as themselves. Editors can see the SQL behind an analysis; consumers see the results. We don't train models on customer data, data is encrypted both while it moves over the network and while it is stored, and we are pursuing SOC 2 Type II. Golden generates insights and dashboards in minutes, offers about 60 visualizations, and paid plans are $30 and $45 per user with no token charges. More detail is on why Golden is AI-native, and if you are moving off a legacy tool, see our Tableau alternatives guide.

FAQ

What does AI-native mean in business intelligence?

AI-native BI is analytics software designed around AI from the start, so AI is part of how data is explored, analyzed and shared, rather than a chat feature added to a traditional dashboard tool.

How do I evaluate AI-native analytics vendors?

Ask about architecture, how the AI understands business definitions, how answers can be verified, whether data stays in your warehouse, whether models are trained on your data, how AI usage is priced, and whether non-technical users can succeed unassisted. Test on your own data, not demo data.

What is the biggest red flag when a vendor claims to be AI-native?

Answers you cannot verify. If no one on your team can see the logic or SQL behind an AI-generated result, you cannot trust it for real decisions.

Should AI analytics be priced per token or per user?

Per-token or per-query pricing makes costs harder to predict and can discourage people from asking questions. Per-user pricing is easier to forecast. Whatever the model, ask for a cost estimate at higher usage than you expect.

Does AI-native BI require moving data out of my warehouse?

It shouldn't. Look for products that query your cloud warehouse directly and respect the permissions you already have there.

Put Golden to the test

Bring this checklist and your own data. Sign up for Golden and see how it answers each question.