EVALUATION GUIDE
Do not ask whether it can answer a polished demo question. Ask whether it can preserve your business context, show its plan, handle ambiguity, and improve after your team accepts an answer.
THE SIMPLE TEST
WHY THIS MATTERS
Most modern AI systems can produce a confident-looking answer. The real evaluation is whether the system can understand the business meaning of the question, expose the steps it intends to take, and only run work that your data team can inspect.
A useful AI BI analyst should become safer with use. Every accepted answer should strengthen the shared context your organization relies on, instead of becoming another one-off chat transcript.
The market evidence points in the same direction. BARC's 2025 BI & Analytics Survey shows that BI tools still create value, but adoption remains the hard problem. Evaluate AI BI as an adoption system: can real users inspect meaning, resolve ambiguity, and reuse accepted logic without waiting on another translation cycle?
THE SIX TESTS
Does the system build a reusable understanding of your business, or does each answer start from the prompt?
It should ingest existing assets such as schema, prior SQL, dashboards, metric definitions, and team vocabulary. The result should be an inspectable context layer your team can improve.
Can a business user and an analyst see what the system believes the question means before SQL runs?
Look for resolved terms, filters, time windows, joins, cohorts, calculations, and assumptions. A trustworthy AI analyst should show the plan, not just the answer.
What happens when a term, metric, join path, or time period is unclear?
The system should ask, refine, or refuse instead of inventing a definition. This is usually the fastest way to separate a demo from a governable workflow.
Is SQL generated directly from language, or compiled from a checked analytical plan?
The same accepted plan should lead to the same SQL. Analysts should be able to inspect the plan and understand why the query is valid.
Does the accepted answer make the next related question easier, safer, or faster?
Validated definitions, segments, calculations, and plan patterns should persist. If everything disappears after the chat, the organization is not learning.
Who helps the system land with business users and data teams in the first quarter?
Ask who owns onboarding, validation, feedback loops, and adoption. A good architecture still needs an operating motion.
LIVE EVALUATION
A strong system should be willing to show the interpretation layer before execution. The refusal path matters as much as the success path.
MARKET MAP
The question is not which product has the most AI. The question is which job you need done.
Good for creating, summarizing, and modifying BI artifacts inside an existing reporting product.
Ask whether the answer becomes reusable business context, or stays inside a report workflow.Good for governed question answering close to the warehouse or lakehouse platform.
Ask whether the context is portable across the rest of your analytics stack.Good for giving business users a search-like interface over governed analytical concepts.
Ask whether reasoning plans compound outside the search experience.Good for accelerating technical analysts as they write SQL, Python, notebooks, dashboards, and investigations.
Ask whether business users can inspect and reuse the plan without becoming analysts.Good for reasoning, drafting, code help, and explanation when the right context is supplied.
Ask what verifies execution and persists accepted analytical logic after the chat ends.Built for recurring business questions where users need answers and data teams need inspectable control.
Ask whether the system builds a Context Graph, shows the plan, verifies ambiguity, and compounds accepted plans.WHERE SPOTONIX FITS
Spotonix constructs a customer-specific graph from your data, logic, history, and accepted analytical plans.
Questions become formal plans over business primitives such as Segments, Calculations, cohorts, filters, joins, and time windows.
The plan is shown before execution. Missing definitions or ambiguous terms are refined or refused.
Accepted work persists, so the next related question starts with more verified context than the last one.
NEXT STEP
In a first working session, Spotonix can walk through the Context Graph, the proposed plan, the ambiguity path, and what would compound after the answer is accepted.