Business intelligence has traditionally meant dashboards. A team decides what questions matter, builds a report to answer them, and then waits for a new build whenever the question changes. This model works, but it is slow, and it puts a permanent bottleneck between a question occurring to someone and them getting an answer.

Conversational business intelligence is changing that. Analysts now estimate that by the end of 2026, more than half of enterprise analytics queries will be generated through natural language, search, or voice input, rather than built manually through traditional dashboard interfaces. The conversational BI market itself is projected to grow at over 20 per cent annually through 2028, reflecting how quickly this shift is being adopted across industries.

Why this depends on the data layer, not just the AI

The single most important factor in whether conversational AI on business data actually works is the quality of the semantic layer underneath it. A semantic layer maps business terms, such as “active customer” or “completed job,” to the correct underlying tables, joins, and calculations, so that when someone asks a plain-language question, the AI queries the right thing and returns a consistent answer every time.

Without this layer, conversational AI tools tend to fail in a specific way. Recent research into enterprise AI analytics has found that most failures are not the AI hallucinating information outright, but rather choosing the wrong table, joining data at the wrong level, or aggregating figures incorrectly, all because the underlying data lacked clear, governed definitions. Organisations with mature semantic layers and business glossaries see meaningfully higher accuracy than those relying on AI to interpret raw, ungoverned data structures directly, in some cases several times higher.

This is precisely why platforms such as Power BI’s Copilot function well when they are querying a well-structured semantic model, and struggle when asked to interpret disconnected or inconsistent tables. The AI is only ever as reliable as the business logic it has been given to work with.

What good conversational BI delivers

Done properly, conversational AI on top of a governed semantic model allows any team member, not just analysts, to ask direct questions and get an answer grounded in the organisation’s actual definitions, with the reasoning and data source shown alongside it. This does not eliminate the need for a data team. Organisations adopting this approach typically do not reduce data team headcount, but instead see far more output and business impact from the same team, because analysts shift from building repetitive reports towards maintaining and improving the semantic layer itself.

For any organisation that has already invested in a modern data platform and semantic models, this is one of the fastest ways to get visible, practical value from that investment, since the foundational work has already been done.

Getting started well

The most effective approach is to start with a small set of well-defined, frequently asked business questions, confirm the AI answers them correctly and consistently against the governed semantic model, and expand from there. This proves accuracy early and builds the organisational trust needed before conversational AI is rolled out more broadly across teams. Check our our services now.