What AI does well in BI
AI is best at the work between a question and a first draft. The jobs it does reliably today:
- Answering one-off questions. A natural-language query such as "which products fell last month?" becomes a chart, and conversational analytics lets people follow up without starting again.
- Drafting charts and dashboards. An AI chart generator turns "monthly revenue by region as a line chart" into a chart; an AI dashboard builder drafts a whole page from "a sales overview for the regional managers".
- Writing and fixing formulas and queries. A formula assistant for calculated columns writes "revenue minus cost" as a formula; text to SQL drafts a query, and an AI query fixer repairs one that failed.
- Explaining. Summarizing a dashboard, explaining why a KPI changed, putting a slow query or a failed chart into plain words.
- Setting things up. Turning "email me the sales dashboard every Monday at 9" into an automation, suggesting column mapping and clean-up steps when a file is imported, finding which columns link two tables, and answering "how do I…?" inside the product.
All of these are what is now called generative BI: the AI produces a draft, and a person decides whether to keep it.
What AI should not do on its own
The risks in AI analytics are not exotic. They are the old BI risks, made faster.
- Inventing numbers. An assistant that writes figures into a paragraph, instead of returning a chart built from the data, cannot be checked. Insist on answers that are real charts or tables from your data.
- Using its own definitions. If the AI decides for itself what "revenue" or "active customer" means, it will disagree with the dashboards, and people will stop trusting both.
- Seeing past permissions. An AI that answers from data the asker could not open on a dashboard is a leak with a friendly interface.
- Changing things without asking. A generated chart, formula or automation should be a draft until a person saves it. Nothing should be published, sent or scheduled by the AI alone.
- Running in the background. AI that runs on every page load or on a schedule spends money nobody approved and sends data out nobody asked to send.
Keeping AI answers matching the dashboards
The single best protection is a semantic layer: one shared set of business definitions that people and the AI both use. Relate the tables once, describe what the columns mean, define metrics such as margin and churn in one catalog, add synonyms ("turnover" means revenue, "debtors" means receivables) and certify the tables that hold the official figures. An AI that reads those definitions calculates margin the way finance does.
The second protection is permissions that apply everywhere. Row rules and column masking should hold for AI answers exactly as they do for dashboards, downloads and scheduled reports, so a branch manager asking about sales sees only their branch. The third is scope: admins should choose which tables the AI may use and give it company-wide instructions, so it does not answer from a stale copy somebody uploaded once.
Finally, separate what the AI needs from what it could see. Helpers that draft charts, formulas and queries need table and column names and types, not your rows. Only a question about the data itself needs to read results, and then only within the asker's permissions.
Controlling AI cost: click-only AI
AI costs money every time it runs, so the way a tool decides when to run it matters more than the price per request. The predictable model is click-only AI: the AI runs only when someone presses a button, never on page load, on save or on a schedule. The button should say what it costs before the click, the charge should apply only when the AI produced an answer, and the credits used should show under every answer.
Pair that with free, automatic checks that use no AI at all: a query check that rates a query before it can be saved, speed-up tips for a slow dashboard, quick fixes for a crowded chart, and simple computed facts such as the biggest change on a dashboard. Those catch the common problems without spending anything, and the AI is kept for the questions that need it.
A checklist for evaluating AI BI tools
Ask every vendor the same questions, and ask to see each one working on your own data:
- Are AI answers real charts built from our data, or text with numbers in it?
- Does the AI use our saved metrics, synonyms and certified tables?
- Do row rules and column masking apply to every AI answer?
- Can admins choose which tables the AI may use, and switch the AI off entirely?
- Which features see our data, and which see only table and column names? What happens if someone types a password into a prompt?
- Is every AI output a draft, checked by the same rules as a person's work, and saved only when someone saves it?
- Does the AI ever run without a click? How is the cost shown before and after each request?
- Can we use our own AI provider, or a private model on our own servers?
- Which checks are free and automatic, and which need AI?
Where Klayara fits. Klayara AI, on the Growth and Enterprise plans, answers questions with real charts within the asker's permissions, drafts charts, dashboards, formulas, queries and automations, and explains dashboards and slow charts in plain words. It reuses your semantic layer, runs only when someone clicks (usually 1 credit), and every result is a draft until you save it. The chart, formula and query helpers see names and types, never your data. See Klayara AI, ask your data and the AI chart builder.