Comparison · Data / Business Intelligence

Conversational AI on data or a BI dashboard

Query your data in plain language, or build and maintain BI dashboards. What an SME actually needs.

Daniel Levis· Published 15 October 2026

In brief

Choose Conversational AI analytics when questions are varied and ad-hoc; choose a Traditional BI dashboard (e.g. Power BI) when KPIs are stable and must return identical numbers every time. For most SMEs this is not an either/or: use a BI dashboard for recurring, always-identical KPIs, and add conversational AI for the ad-hoc questions that today pile up on the data team. AI is faster for exploring, but it must be governed: without a clean data model and answer checks it can return numbers that look right but are wrong. A dashboard is reliable and repeatable, but rigid and slow to change.

Option A

Conversational AI analytics

You ask your data questions in plain language ('how much did we bill in Lombardy in March?') and an agent turns the question into a query and an answer.

Pros

  • +Ad-hoc questions answered in seconds, no waiting on the data team
  • +Accessible to people who don't know SQL or BI tools
  • +Adapts to new questions without building a new report each time
  • +Great for exploring data and forming hypotheses before formalising a KPI

Cons

  • −Risk of plausible-but-wrong answers if the data model isn't clean and documented
  • −Needs governance: metric control, permissions and an audit of what was asked
  • −Less suited to recurring, always-identical KPIs where repeatability beats flexibility
  • −Quality depends heavily on the structure of the underlying data

Best for

  • Teams with many different, non-recurring data questions
  • Non-technical people who today depend on the data team for every question
  • The exploration phase, before deciding which KPIs to track permanently

Option B

Traditional BI dashboard (e.g. Power BI)

You build dashboards and reports with predefined metrics, auto-refreshed, that the team consults visually.

Pros

  • +Reliable and repeatable numbers: the same metric always returns the same result
  • +Metrics defined once and shared across the whole company
  • +Excellent for recurring KPIs and continuous monitoring
  • +Mature ecosystem, widespread skills and ready-made integrations

Cons

  • −Every new question needs a new report or a change: time and a queue at the data team
  • −Rigid: it only answers well the questions planned at design time
  • −Requires specific skills to build and maintain the dashboards
  • −Sprawl of unused dashboards creates maintenance and confusion

Best for

  • Stable KPIs to monitor continuously (revenue, margin, cash flow)
  • Companies with regulatory reporting or a board that wants the same numbers each month
  • Those with a structured data model and in-house BI skills
Criterion Conversational AI analytics Traditional BI dashboard (e.g. Power BI)
Ad-hoc questions Seconds, in plain language Needs a new report or change
Recurring KPIs Less suited (low repeatability) Its strong point
Skills required Low for the user, high on the data model BI skills to build and maintain
Number reliability Must be governed (error risk) High and repeatable
Flexibility High Low (planned questions)
Governance and audit Must be designed explicitly Consolidated in the tool

The verdict

It's not one against the other: they are two tools for two different needs. The BI dashboard remains the right choice for recurring KPIs, board reporting and anything that must always return the same number. Conversational AI wins for the new, non-repeatable questions that today clog the data team, provided you build it on a clean data model with checks on the answers: without governance it produces credible but potentially wrong numbers. For an SME the healthiest path is to keep dashboards for the standard and add AI on top of already-modelled data, not instead of BI.

FAQ

What people usually ask us.

Does conversational AI replace Power BI?
No, in most cases it complements it. Power BI stays better for recurring KPIs and always-identical reporting; conversational AI covers the ad-hoc questions that otherwise queue up at the data team. The worst move is throwing away a clean BI data model: that's often exactly what makes the AI reliable.
How do I stop the AI giving wrong answers on the data?
Three things: a clean, documented data model, explicitly defined metrics (so the AI doesn't 'invent' how to compute margin) and controls on permissions and audit. We design the data agent with an immutable audit log on every answer and no LLM training on client data. See how we work on AI Agents.
How much does it cost to add conversational AI to our data?
With Soraia it starts at 3,000 € per month with two dedicated people (Project Manager and AI Engineer) and first delivery in 4 weeks. The real cost depends mostly on the state of the data model: if it's already clean you move fast, otherwise part of the work is fixing it. For an estimate on your case, let's talk.
Do we need a dashboard first, or can we start straight from AI?
It depends on data maturity. If you already have stable KPIs and a structured data model, AI adds value immediately on ad-hoc questions. If data is scattered across sheets and systems, it makes sense to structure it first, useful work in either case. In Finance and Sales & Marketing we often start exactly from tidying up the data.
Is our data safe with conversational AI?
Yes, if it's designed well. We work GDPR-compliant, with European or on-premise infrastructure when required, an art. 28 DPA in the contract and no LLM training on client data. Permissions decide who can query what, just like in a BI tool.

Not sure which one fits your case?

20 minutes with the CEO to work out the right choice for your processes. No pitch, no obligation.

Daniel Levis

Daniel Levis

Co-Founder & CEO

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