How to Evaluate an AI Analytics Agent Before You Buy

The best way to evaluate an AI analytics agent is to test it on your data, your business definitions, and questions your team has already answered. Do not judge it by whether a polished demo produces a plausible chart.

A useful evaluation should determine whether the agent:

• Uses the right metric, population, date, and comparison.
• Produces numerically correct results without unsafe joins or double counting.
• Handles ambiguous questions without silently guessing.
• Completes multi-step investigations, not just single queries.
• Respects data permissions in both its analysis and its explanation.
• Shows enough evidence for a person to review the answer.
• Produces consistent results when the same work is repeated.
• Fails clearly when data is missing, stale, or unsuitable.
• Learns from corrections without creating a second source of truth.

The scorecard below turns those requirements into a 100-point test. Run it during a proof of concept before giving an AI agent access to consequential reporting or decision workflows.

What Should an AI Analytics Evaluation Prove?

An evaluation should prove more than whether the product can translate a question into SQL.

A business answer can fail at several levels:

1. Interpretation: The agent misunderstood what the person meant by “revenue,” “customer,” or “last quarter.”
2. Data selection: It chose the wrong table, field, date, filter, or population.
3. Query logic: It used an invalid join, counted records twice, or applied the wrong aggregation.
4. Analysis: It calculated the number correctly but used a weak comparison or stopped before investigating the change.
5. Explanation: It stated a conclusion that the evidence did not support.
6. Governance: It exposed restricted data or relied on an unapproved definition.
7. Operations: It worked once but could not be reviewed, corrected, or run reliably again.

Your test should cover every layer. A correct total from the wrong metric is still wrong. So is a valid query followed by an unsupported causal claim.

Microsoft warns that Power BI Copilot can return inaccurate results and may not produce the same answer for the same prompt, model, and data. Its guidance recommends testing models for consistently correct results and having users critically review outputs. Google similarly recommends validating output from Conversational Analytics before using it. These are useful expectations for every AI analytics product, not only those two systems.

Do This Before the Vendor Demo

The quality of the evaluation depends on the test set. Build it before the vendor configures the demo so that the product is tested against the business as it exists.

Choose a bounded use case

Pick one real workflow, such as:

• Investigating weekly revenue movement.
• Preparing a customer health review.
• Answering product adoption questions.
• Producing a recurring executive report.

Use one or two governed data sources at first. A narrow scope makes failures easier to diagnose. It also prevents a large implementation project from being mistaken for a successful evaluation.

Create a set of known answers

Select 20 to 30 questions that analysts have already answered. Include the original query, dashboard, notebook, or report used to produce each answer. Record:

• The approved metric definition.
• The expected value or acceptable range.
• Required filters and exclusions.
• The correct date field and reporting period.
• Valid joins and data grain.
• Known data-quality limitations.
• The person who can approve the answer.

Use recent, representative periods. Avoid building the whole test around unusually clean data.

Add questions designed to fail

A test set made only of well-formed questions measures the happy path. Add vague, impossible, restricted, and misleading requests. The agent should sometimes ask a question, warn the user, or decline to answer.

Examples:

• “Show revenue last month” when several revenue measures exist.
• “Which campaign caused churn to increase?” when the data shows association, not causation.
• “List the salaries of everyone on my team” from a user without payroll access.
• “Compare conversion before and after the migration” when tracking was broken during the change.
• “Give me today’s final sales” when the source refreshes only once each night.

Separate setup from testing

Let the vendor configure the approved semantic model, business context, and permissions. Then freeze the setup before running the scored test. Otherwise, every miss can be repaired during the demo and counted as a success.

Record how much vendor help the setup requires. A product that performs well after six weeks of custom modeling may still be the right choice, but that work belongs in the buying decision.

The 100-Point AI Analytics Agent Scorecard

Score each category as pass, partial, or fail. Award all available points for a pass, half for a partial result, and zero for a failure.

Treat permission leaks and fabricated evidence as hard-stop failures regardless of the total score. For a high-impact workflow, an unreconciled error in a material metric should also stop the evaluation until the cause is understood.

Test 1: Known-Answer Accuracy — 15 Points

Start with questions that have approved answers. Compare more than the final number.

For each response, check:

• Metric and dimension selection.
• Date field, period boundaries, and time zone.
• Filters, exclusions, and cohort logic.
• Join path and aggregation.
• Treatment of nulls, refunds, cancellations, and late-arriving data.
• Rounding and units.

Ask the agent for its work before revealing the expected answer. If the result does not match, require it to identify the discrepancy. Do not accept “the numbers are close” until you know why.

A pass means the answer reconciles for the right reasons. An agent that lands on the expected value through the wrong query has failed the test.

Test 2: Ambiguous Metric Definitions — 10 Points

Use business terms with multiple legitimate meanings. Revenue, active user, conversion, churn, and customer are common examples.

Run three versions of the question:

1. A fully specified request.
2. A request whose correct meaning can be inferred from approved project or audience context.
3. A request that remains genuinely ambiguous.

The agent should answer the first, apply documented context to the second, and clarify the third. It should not choose whichever field has the most convenient name.

This test depends on the quality of the underlying data model and context. Google’s Looker guidance notes that unclear labels, ambiguous definitions, and similarly named fields can cause incorrect field selection. Microsoft provides verified answers and AI instructions to map company terms and business logic for the same reason.

For a deeper explanation, see Semantic Layer vs. Business Context: What an AI Analytics Agent Actually Needs.

Test 3: Join and Grain Safety — 15 Points

Choose questions that require datasets at different levels of detail. Examples include combining account-level subscriptions with invoice lines, orders with product events, or campaigns with individual conversions.

Include at least one case where a naive join inflates the result. Ask the agent to state:

• What one row represents in each source.
• The relationship between the sources.
• The keys used to join them.
• Any deduplication or pre-aggregation it applied.
• How it checked the result for fanout.

Compare subtotals before and after the join. Test a segment with no matching records. If the system can show SQL or code, inspect it rather than relying on the narrative.

A pass requires a correct result and a defensible join strategy. Hiding the query turns a correct-looking chart into a weak evaluation result.

Test 4: Multi-Step Investigation — 15 Points

An AI analytics agent should do more than return a metric. Give it a question such as, “Why did renewal revenue fall last month?” and see whether it can complete a structured investigation.

A strong process should:

1. Confirm that the reported movement exists.
2. Check data freshness and changes in measurement.
3. Choose an appropriate baseline.
4. Break the movement into useful segments.
5. Rank the segments that account for the change.
6. Test plausible explanations against available evidence.
7. Separate supported findings from open questions.
8. Produce a reviewable conclusion.

Penalize the agent if it calls the largest segment the “cause” without testing other explanations. A contribution to a decline is not automatically its cause.

For a worked method, see Why Did Your Metric Drop? A Guide to AI Root Cause Analysis.

Test 5: Permission Enforcement — 15 Points

Run the same questions as an administrator, analyst, and restricted business user. Test access at every stage:

• Source rows and fields.
• Semantic models or datasets.
• Generated SQL, code, and query results.
• Cached or remembered content.
• Saved reports and shared links.
• Scheduled delivery destinations.

Do not test only whether the final table hides a restricted column. The explanation, intermediate query, chart labels, citations, and exports must respect the same boundary.

Also test indirect requests. A user may ask for an aggregate over a very small group, request a ranking that reveals an individual, or ask the agent to summarize a restricted saved analysis.

Any unauthorized disclosure is a hard-stop failure. Document the exact identity, role, source policy, and sharing setting used in the test so the result can be reproduced.

Test 6: Evidence and Generated SQL — 10 Points

Require every consequential answer to show its work. The review surface should include:

• The metric definition and its source.
• The datasets, fields, and date range used.
• Filters and exclusions.
• The generated query, code, or equivalent analytical steps.
• Intermediate results that support the conclusion.
• Warnings, assumptions, and unresolved questions.

Evidence should be connected to specific claims. A generic list of sources at the end is less useful than seeing which query and result support each finding.

Test whether a reviewer can reconstruct the answer after the chat ends. If the analysis disappears into a transcript or cannot be rerun, it will be difficult to govern in recurring business work.

Test 7: Repeatability Across Runs — 5 Points

Run the same question at least five times with the same data, model, context, and permissions. Then compare:

• Final values.
• Filters and query logic.
• Selected segments.
• Rankings and written conclusions.
• Warnings and confidence language.

Natural-language wording can vary. Material numbers and claims should not change without an identifiable reason.

Next, rerun the analysis after a controlled data refresh. The method should remain stable while the values update. For recurring work, prefer a saved and rerunnable analysis over asking the agent to recreate the method from a prompt each time.

Test 8: Data-Quality Failures — 5 Points

Give the agent a source with a known problem:

• A stale refresh.
• Missing dates.
• A sudden increase in null values.
• Duplicate records.
• A tracking definition that changed mid-period.
• A partial backfill.

The agent should detect or surface the limitation before giving a confident conclusion. It should explain what is affected, what remains usable, and what would be needed to finish the analysis.

Award zero if it quietly treats missing data as zero, compares incompatible periods, or invents a clean trend across a measurement break.

Test 9: Questions the Agent Should Decline — 5 Points

Good judgment includes knowing when not to answer. Test requests that are:

• Outside the connected data.
• Blocked by permissions.
• Too ambiguous to interpret safely.
• Framed as causal when the evidence is only observational.
• Based on a population too small to report safely.
• Dependent on data that has not arrived.

A useful refusal is specific. The agent should state why it cannot answer, identify the missing information or permission, and suggest a safe next step. A vague refusal is better than a fabricated answer but less useful than a precise one.

Test 10: Review and Correction Workflow — 5 Points

Deliberately correct a metric definition, filter, or analytical assumption. Then test what happens next.

Ask:

• Is the correction attached only to this answer, this project, or the whole organization?
• Can an authorized owner approve it?
• Does it update an existing governed definition or create a duplicate?
• Is the previous version preserved?
• Can the correction be removed or replaced?
• Do saved analyses show which version they used?

The agent should not turn every comment into permanent company policy. Corrections need scope, ownership, and history.

How to Interpret the Score

Use the score to guide the next decision, not to manufacture certainty.

• 80–100 points: Candidate for a controlled pilot, provided there are no hard-stop failures.
• 65–79 points: Promising, but limit the pilot to low-risk workflows and require a specific remediation plan.
• Below 65 points: Do not expand access. The system needs material improvement or a narrower use case.

Compare vendors by category, not just total. A tool may score well because it is excellent at simple questions while failing the multi-step work you need. Weighting can change by use case, but permissions, evidence, and correctness should never be traded away for a smoother interface.

Track three forms of effort alongside the score:

1. Setup effort: Time required to connect data, prepare models, and load business context.
2. Review effort: Analyst time needed to verify and repair each result.
3. Maintenance effort: Work required when schemas, definitions, permissions, or operating rules change.

An agent that saves two minutes per question but adds ten minutes of review is not ready to scale.

Red Flags During an AI Analytics Proof of Concept

Be cautious when a vendor:

• Will only demonstrate on its own sample data.
• Tunes each test question after seeing the expected answer.
• Reports an accuracy percentage without publishing what counted as correct.
• Shows charts but not the metric definition, filters, query, or evidence.
• Treats every mismatch as a prompt-writing problem.
• Requires business logic to be copied into a separate system with no update path.
• Cannot explain how permissions apply to generated and shared artifacts.
• Describes association as causation.
• Claims that a semantic layer eliminates hallucinations.
• Has no clear process for versioning corrections and rerunning work.

A product can still be useful with limitations. The problem is not the existence of failure modes. It is the inability to find, explain, and control them.

Questions to Ask Every Vendor

Use these questions to understand the system behind the demo:

1. Which parts of our existing semantic layer, catalog, and access controls do you use directly?
2. Where is additional business context stored, and who can change it?
3. What happens when two approved definitions conflict?
4. Can users inspect generated SQL, code, filters, and intermediate results?
5. How do you test for join fanout and wrong data grain?
6. How are row- and field-level permissions enforced in saved and shared outputs?
7. Can an analysis be saved and rerun using the same method?
8. What changes between runs, and is that history visible?
9. How does the system respond to stale, incomplete, or unavailable data?
10. Which questions is the agent designed to refuse?
11. How are human corrections approved, scoped, versioned, and reversed?
12. What customer work is required to maintain quality after launch?

Ask for the answer in the product where possible. A live permission test says more than a slide about enterprise security.

How Orion Supports a Reviewable Evaluation

Orion by Gravity connects to sources including Looker, the dbt Semantic Layer, Snowflake, BigQuery, Power BI, Postgres, Shopify, and Google Analytics. Teams can start with the business logic they already maintain instead of rebuilding every definition for a demo.

Orion’s Knowledge Base can store approved metric definitions, operating procedures, analytical methods, and other company context. Specific pages can be enabled for a project, and referenced pages can appear as inline citations in reports.

Analyses can be saved as notebooks and rerun against fresh data. Reviewers can inspect the analytical work, continue the investigation, and share approved outputs. This makes Orion suitable for the evaluation method in this guide: test known answers, inspect the work, correct context, and rerun the same analysis.

No AI analytics agent should be accepted on product claims alone, including Orion. Run the scorecard on the workflow you plan to deploy and require the evidence to reconcile with work your team already trusts.

For related buying guidance, see:

• 10 Best Self-Service Analytics Tools for Business Teams
• 14 Best AI Business Intelligence Tools and Platforms
• How to Automate Weekly Executive Reports Without Another Dashboard

Frequently Asked Questions

How do I evaluate whether an AI data analyst gives accurate answers on company data?

Test it on 20 to 30 questions your analysts have already answered. Compare the metric definition, source, date range, filters, joins, query, intermediate results, and final conclusion. Add ambiguous, restricted, and impossible questions so you also measure how safely the agent fails.

What is a good accuracy rate for an AI analytics agent?

There is no useful universal percentage. The required result depends on the risk of the workflow and on what the vendor counts as correct. Score semantic interpretation, numerical results, query logic, evidence, permissions, and repeatability separately. A high average cannot offset a permission leak or a material reporting error.

How many questions should be in an AI analytics test set?

Start with 20 to 30 known-answer questions for one bounded workflow, plus targeted failure cases. Include simple lookups, ambiguous terms, complex joins, multi-step investigations, permission tests, and data-quality problems. Expand the set as the pilot reaches more teams and decisions.

Should an AI analytics platform show the SQL and evidence behind its answer?

Yes, or provide an equivalent reviewable record of the analytical work. A reviewer should be able to see the metric definition, source, filters, date range, calculations, intermediate results, and evidence supporting each important claim.

Should I test an AI analytics agent on sample data or production data?

Use a controlled copy or approved slice of your own data. Vendor sample data is useful for learning the interface, but it does not test your metric definitions, schema complexity, permissions, data quality, or business exceptions. Follow your organization’s security and privacy rules when selecting the test environment.

What should automatically disqualify an AI analytics tool?

Unauthorized data disclosure and fabricated evidence should stop the evaluation. A material metric error that cannot be explained and corrected should also block deployment for that workflow. Other failures may be acceptable in a narrow pilot if they are visible, contained, and covered by a remediation plan.

Who should participate in the evaluation?

Include the data owner, an analyst who knows the expected answers, the business user who will rely on the output, and a security or governance owner for sensitive workflows. Procurement can compare commercial terms, but it cannot validate query logic or business meaning by itself.

Can a semantic layer guarantee accurate AI answers?

No. A semantic layer can govern metrics, relationships, calculations, and access rules, which removes many sources of error. The agent still needs the right business context, a sound analysis method, reliable data, evidence, testing, and human review.