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AI isn’t replacing analysts, it’s amplifying them.

Over the past few years, we’ve moved from manual reporting to automated data flows and now to intelligent, conversational systems that help us analyse information faster than ever.

The evolution of analytics

Data analysis has evolved through several key stages:

1. Automation: Systems began to capture and process data with minimal human input.
2. Standardisation: Organisations built repeatable structures, relational databases, SQL models, and shared metrics.
3. Expansion: New technologies emerged, data lakes, non-relational databases, and distributed storage, offering more flexibility but also more complexity.

Now, with Generative AI and Large Language Models (LLMs), we’ve entered a new phase: simplification. Analysts can interact with data directly in plain language, without needing to master every technical syntax.

From queries to conversations

Traditionally, analysts have spent hours writing and debugging SQL queries. Today, AI-powered tools can translate natural language into SQL almost instantly.

You can simply type:

“Show me total sales by region for the last quarter.”

…and the system will generate and run the SQL for you in the background, returning instant insights without the need to open Excel or Power BI first.

These systems are not replacing dashboards, but they accelerate discovery. Instead of waiting for a full report, analysts can get a quick snapshot or validate an idea in seconds.

The rise of Self-Serve BI

This new generation of tools is often called Self-Serve BI, platforms that let business users query data directly using plain English prompts.

At Excel in BI, we’re experimenting with a prototype called SelfServeBI, a solution that lets users explore metrics without needing deep technical knowledge. The goal is to shorten the distance between data and decision, empowering teams to answer their own questions safely, consistently, and quickly.

Imagine asking:

“What products grew fastest this month compared to last?”
or
“How many customers churned after our price update?”

Within seconds, you get a chart and a summary written in simple language, no formulas, no manual lookups.

Why this matters for analysts

AI isn’t taking jobs, it’s changing the job description.
Analysts who embrace AI tools will spend less time on repetitive work and more time on strategy, storytelling, design, and decision support.

This shift means:

• Fewer manual queries
• Faster time to insight
• Stronger data literacy across the organisation
• More collaboration between analysts and business users

In short, analysts become insight creators, not just data retrievers.

The limits of AI (for now)

While Generative AI can draft SQL and create basic visuals, it can’t yet replace a full BI workflow.
Building accurate dashboards still requires:

• Clean, well-modelled data
• Understanding of business context
• Validation and governance

Think of AI as your assistant, not your autopilot.

What’s next

The next few years will see rapid growth in self-service analytics powered by natural language. We’ll see:

• AI copilots embedded in every BI platform
• More integration between chat interfaces and live databases
• Automated explanations of insights and anomalies

It’s an exciting time to be in analytics, the tools are evolving fast, but the purpose remains the same: help people make better decisions with data.

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Want to learn how to build tools like this?

Join our free webinars at Excel in BI where we explore practical use cases of AI in analytics, from automating reports to creating your own self-serve BI experience.

Excel in BI
Helping NZ professionals turn data into clarity, confidence, and business growth.