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How to Evaluate AI Analytics Assistants Before You Buy

A documentation-based evaluation of AI analytics assistants: governed metrics, permission isolation, reproducible calculations, uncertainty, and deployment-specific costs.

Analytics documents linked to a protected data vault, under the heading Evaluate AI Analytics Assistants.

An AI analytics assistant should help people ask questions about governed business data while preserving the meaning of the metrics and the limits of their access. A convincing chart is only the beginning of acceptance. Before buying, establish whether an answer uses the right definition, can be reconstructed, and stays within the questioner's permissions.

This guide is documentation-based, using official public sources reviewed on November 8, 2026. We have not connected a warehouse, executed generated queries, tested permissions, or measured answer quality. The checks below are proposed acceptance criteria, not reported test results or a product ranking. Current prices and your organization's entitlements are not verified.

Start with the job and the existing stack

Separate three purchasing needs. Business users need understandable answers about approved metrics. Analysts need inspectable calculations or SQL that they can reconcile with a trusted result. Data engineers and developers need assistance building and maintaining platform workflows. These needs can overlap, but one successful demonstration does not validate all three.

Snowflake Cortex Analyst is relevant to the first two needs: Snowflake documents natural-language-to-SQL functionality grounded in semantic views, with legacy semantic-model YAML still supported. Its official documentation recommends semantic views for new implementations. That establishes a documented architecture, not whether your definitions or generated SQL will be correct. The same documentation says Cortex Analyst is unavailable in the People’s Republic of China and recommends transitioning to Cortex Agents. Ask which supported product surface a new proposal uses; no comparative quality claim is established here.

ThoughtSpot provides another evaluation route through Spotter. Its official product page describes natural-language business questions and semantic-layer grounding. Treat those as vendor-described capabilities. Ask which model and connection the proposed deployment will actually use, rather than assuming every advertised integration is included.

For teams already using Looker, evaluate Looker / Conversational Analytics against the existing LookML semantic layer. Google's Looker overview describes conversational queries grounded in that layer and warns that plausible output can be incorrect. This is a Looker assessment; BigQuery capabilities and Looker Studio features do not establish what this Looker configuration supports.

Write the acceptance sheet before the demo

Record the current warehouse or BI system, connection method, metric owners, semantic-layer location, refresh schedule, and identity model. Identify who will maintain joins and definitions when the underlying schema changes. Require the vendor to identify any additional modeling work and the person responsible for it.

Choose three to five questions the team must answer correctly. A synthetic fixture could use a fictional product line's weekly net revenue, return rate, and regional breakdown. Specify the grain: one row per order, item, return event, or reporting period. Fix the currency, timezone, week boundaries, refund treatment, and denominator before writing an expected answer.

Include a question with missing information, one with no matching rows, and one outside a user's access. Keep the expected SQL or calculation and expected result under analyst review. These are proposed fixtures; no dataset has been loaded or run for this guide. A documentation review can narrow a shortlist, but cannot pass an execution-dependent gate.

Apply five acceptance gates

1. Governed metric definitions

Ask “What was net revenue last week?” and then change the wording without changing the intended metric. Check whether the assistant uses the approved definition, joins, filters, and time window. A correct total reached through a wrong join should fail: another reporting period may expose the error.

Ask how synonyms, conflicting definitions, and model changes are managed. Require a named owner and a review process for approved examples. Do not accept a manually corrected demonstration as evidence that the underlying definition has been fixed for future users. Record which configuration produced each result.

2. Permission isolation

Plan separate tests for an authorized analyst, a region-restricted business user, and a user without access. Check rows, columns, metadata, follow-up answers, and shared outputs. The assistant should not become an alternative path around the intended data boundary. Establish which identity actually executes the query, especially in an embedded application.

Snowflake documents role-based access controls and privileges for Cortex Analyst. Google's Looker setup documentation distinguishes content access, data access, and feature permissions. Neither source proves that a buyer's roles, service credentials, or sharing settings are configured correctly. Permission isolation remains untested here and needs evidence from the proposed deployment.

3. SQL or metric traceability

Require enough evidence for an analyst to reconstruct each answer: the question, resolved metric, selected model, filters, time range, generated SQL or equivalent query representation, and returned result. Ask where this information is visible, who can access it, and how long it remains available.

Compare the calculation with the approved baseline. Inspect joins, distinct counts, null handling, and aggregation grain. If a product exposes a governed metric reference rather than raw SQL, determine whether that reference is sufficient to reproduce the answer. A readable explanation is helpful, but it cannot substitute for the actual calculation path.

4. Uncertainty and refusal

Use ambiguous prompts such as “Which region performed best?” without defining performance. Look for clarification or an explicit assumption that the reviewer can inspect. Also ask about unavailable dates, unsupported metrics, and inaccessible data.

Distinguish no records, zero, missing data, query failure, and denied access in the scoring sheet. Require a correction path when the answer is wrong, including escalation to the metric owner. Do not award a pass because the response sounds confident or because a second prompt happens to produce the expected number.

5. Embedding, sharing, and export

Decide whether users need the native BI interface, an embedded application, or a shareable result. Evaluate that exact route. Google's Looker overview documents private and signed embedding for Conversational Analytics, but an embedding description does not establish your license, authentication design, or export rights.

For any supplier, request proof that identity and restrictions survive the intended sharing path. Check whether an export preserves metric context and filters, and how access changes affect saved conversations or links. Treat demonstration-only functionality, contract-dependent options, and unverified exports as procurement questions rather than accepted capabilities.

Keep adjacent product categories separate

The directory entry for Databricks Assistant / Genie Code belongs in a platform-workflow discussion. Databricks describes Genie Code as a coding and data assistant for technical practitioners. Its separate Genie Agents documentation describes a domain-specific natural-language data interface, formerly called Genie Spaces. Do not treat the Assistant / Genie Code entry as an interchangeable business-questioning counterpart to Cortex Analyst. Confirm the named surface before comparing it.

Likewise, Microsoft Fabric AI needs a workload-specific evaluation. Microsoft's Fabric overview of Copilot separates experiences across workloads, including Power BI and SQL-related work. A coding demonstration in one workload does not pass business-metric acceptance in another. Confirm the exact workload, tenant settings, capacity requirements, and release status.

Copilot in Excel and Gemini in Google Sheets deserve a spreadsheet evaluation when the task centers on workbook or sheet analysis. Official Microsoft guidance and Google guidance describe their respective analysis features. Those features alone do not establish governed cross-user metric semantics or warehouse permission inheritance. Enterprise knowledge search has a different acceptance target again: retrieving supporting documents cannot validate a revenue calculation.

For a workbook-centered purchase, use the separate spreadsheet copilot evaluation checklist to plan formula, lookup, and pivot checks. Passing those tasks does not establish the governed BI acceptance gates above.

Run a bounded pilot and define the exit

For a future authorized pilot, use synthetic data and the same reviewed questions for each shortlisted configuration. Obtain the required administrator permissions first. Record outputs, query evidence, identity, model version, analyst corrections, and failure reasons. Set a review period and acceptance thresholds before results arrive; do not invent a universal accuracy target.

Pause procurement if restricted information leaks, material calculations cannot be reconstructed, or ambiguous questions silently change definitions. Also pause if no team owns the semantic model or the supplier cannot establish the offered product surface. Preserve the existing BI workflow until the acceptance owner signs off. An unresolved item should stay unresolved, rather than receiving a provisional pass.

Review costs separately from answer quality. Snowflake's AI pricing documentation distinguishes invocation routes and says generated SQL execution incurs warehouse compute charges. ThoughtSpot pricing and Looker pricing are public procurement starting points. No numeric prices or allowances are adopted here. Request a dated quote with currency, region, billing cycle, product surface, usage assumptions, implementation, and exit terms. Do not assume a chat interface reduces the warehouse bill.

FAQ

Can public documentation replace a pilot?

It can establish documented scope and prerequisites. It cannot prove your metrics, permissions, costs, or failure behavior. Account-dependent checks remain untested until an authorized deployment supplies evidence.

Should every answer include raw SQL?

Require a reproducible calculation path appropriate to the product. Raw SQL can help analysts, while a governed metric reference may be useful if its definition and execution can be inspected. Validate both the calculation and the narrative interpreting it.

Which assistant should we buy?

Shortlist around the warehouse or BI system you already govern. Prefer the configuration that passes your agreed gates with maintainable definitions and acceptable terms. This documentation review establishes no winner.

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