From Data to Knowledge Without SQL: The AI Agents Redefining Analytics in 2026

Querying data in natural language is not a shortcut — it is a paradigm shift that democratises access to operational intelligence across organisations.

For years, accessing business data has had an invisible but very real barrier: query language. Getting an answer from a database — "what was the average delivery time last month by subcontractor?" — required someone to translate that question into SQL, run it, and return the result. That someone was, in practice, an analyst or a developer. The real cost was not just their time: it was the latency between the question and the decision.

AI analytics agents remove that intermediary step entirely. The user asks the question in their own words. The system interprets the intent, selects the relevant tables from the data model, generates the query, validates it, executes it, and returns not just raw data but an explanatory narrative and an interactive visualisation — all in seconds. Self-service analytics, which for years promised to empower business teams but in practice remained dependent on technical profiles, is now making a genuine qualitative leap.

In just a few weeks, leading data platform providers have moved to general availability capabilities that were prototypes a year ago.

The pattern is hard to ignore in its breadth and simultaneity:

Databricks has launched Genie Code, an agent that builds pipelines, generates machine learning models and debugs production incidents, claiming it doubles the performance of leading coding agents on internal benchmarks. At the same time, it has made Custom Agents generally available with built-in memory and managed deployment.

Amazon Redshift now features intent-aware query automation designed specifically to accelerate analysis for users without SQL knowledge — the majority of business profiles in any organisation.

ThoughtSpot has introduced an Agentic Data Prep agent and a native spreadsheet interface, making analysis accessible from environments that end users already know.

Unanet has launched Champ, a natural-language agent that lets architecture and engineering firms query their ERP operational data without any technical training.

dbt has made its analytics engineering agents generally available on Enterprise plans, with fully auditable action trails.

At the integration layer, the Microsoft Copilot Studio and Databricks Genie integration brings conversational analytics directly into Microsoft Teams, allowing business teams to get answers from their data without leaving their existing workflows. Underlying all of this: the Model Context Protocol — the open standard for AI agents to communicate with external tools — now registers 97 million monthly SDK downloads, a figure that reflects the pace of adoption across the developer ecosystem.

From an investment trends perspective, the direction is equally unambiguous: Snowflake and OpenAI have signed a $200 million multi-year partnership to bring frontier models into Snowflake Cortex AI. The market is betting, with real money and concrete timelines, on AI-powered analytics as standard infrastructure.

Organisations that previously depended on an analyst to answer every business question have the most to gain — and the most to lose if they delay.

The rise of AI agents in analytics has asymmetric implications depending on organisational size. Large corporations have spent years building mature data teams and sophisticated pipelines. For them, adopting agents is an incremental improvement on an already solid foundation.

For the mid-sized business — with valuable operational data but no dedicated analytics team, still making decisions from static reports or waiting for someone to build an ad hoc query — the change is of a different order entirely. A well-configured AI agent running on your own data can compress weeks of analytical work into minutes and make it accessible to business profiles that previously depended on technical intermediaries.

The risk of inaction is not abstract either. Gartner estimates that AI and agents will drive a $58 billion shift in the data and analytics market before 2027. Organisations that fail to integrate these capabilities into their workflows in the coming months will not merely be "late to a trend" — they will be making decisions on slower, more expensive information than their competitors.

The difference between a conversational analytics prototype and a solution that delivers real value lies in how business context, security, and result validation are handled.

At Aónides, we have been working in this space for some time. Our data analytics and business intelligence solution now incorporates natural language query capabilities, with an approach that goes well beyond chatting with data: the system classifies the intent of each question, selects the relevant tables and metrics from the client's data model, automatically generates and validates the SQL query, and in parallel produces an explanatory narrative, a commented technical query, and an interactive dashboard with the most relevant KPIs.

Several lessons from this work have become clear to us:

Business context is the differentiator. A generic agent running on logistics data does not know what "run" or "stop" means in the operational vocabulary of a transport company. This is simply one example of what happens in a domain like logistics — but the principle applies with equal force across every sector. In a professional services firm, the agent needs to understand what a "billable allocation" or a "framework engagement" means; in a manufacturing company, what distinguishes a "live order" from a "firm purchase order"; in a financial institution, what exactly constitutes a "consolidated position." The real value emerges when the system carries a KPI dictionary and semantic metadata layer that lets it understand questions in each organisation's own business language, not just in the language of its database.

Security is not a footnote. Any agent with access to real data must have explicit validation of permitted operations built in by design. In our implementations, any operation other than read-only is blocked, even when requests are disguised as seemingly innocuous modifications.

Conversation matters. The ability to refine a query — "filter by subcontractor X only" or "switch the chart to a line graph" — without losing the context of the previous question multiplies the system's value for business users. Conversational memory is as important as SQL generation.

If your organisation wants to explore how to apply these principles to your own data — logistics, financial, operational or otherwise — our AI services are designed precisely to support that journey, from initial assessment through to production deployment.

What is conversational analytics? Conversational analytics is the ability to query business data using natural language questions, without needing to know SQL or specialised BI tools. A conversational analytics system interprets the user's intent, accesses the relevant data model, generates the technical query, and returns the result alongside visualisations and explanatory narratives.

How is an AI analytics agent different from a traditional dashboard? A traditional dashboard displays a predefined set of metrics that someone decided in advance were relevant. An AI analytics agent answers questions that were never anticipated, adapts its visualisations to the context of each question, and lets users refine results through conversation. The leap is not just one of interface — it is about who controls which questions can be asked.

Is it safe to connect an AI agent to real company data? Yes, provided the system is properly designed. Sound implementations restrict the agent exclusively to read operations, validate every query before execution, and log all interactions for audit purposes. The risk does not lie in the technology — it lies in deploying it without appropriate controls.

What types of data and industries can benefit from these agents? Any organisation with structured operational data can benefit: logistics and transport, professional services, manufacturing, financial services, retail, healthcare. The common denominator is having valuable data and business profiles that currently cannot access it autonomously. The key is adapting the agent to the vocabulary and KPIs specific to each sector.

How long does it take to implement a conversational analytics agent? It depends on the state of the data model and the organisation's level of analytical maturity. A functional proof of concept on real data can be operational within a few weeks. The underlying work — defining the KPI dictionary, documenting the semantic schema, establishing security controls — is what determines whether the result is a prototype or a genuine production solution.

Conversational analytics is no longer the future of data — it is the present for the teams already using it to make better decisions faster. Major platform providers have turned what was experimental into infrastructure. What remains to be solved — and where the real work lies — is adapting those capabilities to the specific context of each organisation: its data, its business vocabulary, its KPIs and its decision-making workflows.

Organisations that start building that intelligence layer on their data today will have, before the year is out, an operational advantage that will be difficult for those who wait to recover. The question is no longer whether AI agents will transform data analytics. The question is when your organisation will start.

Want to see an AI analytics agent in action on real operational data? Contact us for a demonstration.