How AI Agents Are Transforming Analytics and the Way Teams Work

Why AI agents in analytics are becoming impossible to ignore

Over the past year, I’ve noticed a real shift in the conversations happening across the businesses we work with. When generative AI first arrived, the energy was around novelty and experimentation. Leaders were curious, excited, slightly overwhelmed — but still trying to work out what it all meant for the day-to-day realities of running a commercial function, an operations team or a data team.

That’s changed. Most organisations I speak to now aren’t asking about “AI” in the broad sense. They’re asking something far more practical: How do we get AI to actually take work off our teams? That’s where AI agents in analytics have started to make a quiet but significant impact.

What’s interesting is that these agents aren’t flashy or dramatic. You often don’t even see them. But they’re gradually taking on the repetitive, time-consuming tasks that have bogged down analysts and data scientists for years.

Things like: writing routine SQL, refreshing dashboards, tidying data, monitoring data quality, summarising reports, highlighting changes that matter, and even running small workflows end-to-end. Nothing headline-grabbing, but exactly the kind of work that keeps analytics teams from doing the strategic thinking they’re capable of.

And that, for me, is the real story.

Where we’re seeing the biggest change

In most organisations, analytics teams spend a huge proportion of their time on what you might call “the plumbing”, the work that needs doing before you can get to the insight. Everyone knows it. Everyone is frustrated by it. But until recently, there was no real alternative.

AI agents in analytics are starting to change the balance.

We’re seeing analysts go from spending days prepping data to having it ready within minutes. We’re seeing teams use agents to prototype analysis, test hypotheses and generate code that would normally take multiple iterations. We’re seeing business users getting nudges, alerts and explanations at the moment they need them, not buried inside a dashboard they rarely open.

The most impressive thing is that this isn’t about replacing people. It’s about removing the friction that stops teams from doing their best work. When an AI agent handles the mechanical tasks, the analyst can focus on the interpretation, the commercial impact, the “so what”, the bit that only humans can do well.

But adoption isn’t automatic, trust still matters

Despite the progress, most leaders I speak to still feel uneasy about letting agents run wild. And they’re right to be. The organisations getting the most value are approaching adoption carefully. They’re keeping humans firmly in the loop, starting with contained, safe tasks and paying close attention to governance.

If there’s one consistent theme, it’s this: Teams need to trust the outputs before they’ll rely on them. Trust isn’t built through glossy demos. It’s built through transparency, iteration and conversation. When people understand what the agent is doing, where its limits are and how decisions are reviewed, adoption grows naturally.

What this means for analytics teams right now

The analytics teams that embrace agents early are discovering a new rhythm, one where their time is spent on analysis, thinking and decision-making rather than maintenance, rework or technical admin.

The teams that wait will find themselves stuck in the same cycle: too much work, not enough time, and very little progress toward the higher-value analytical contribution they want to make.

AI agents in analytics are not a future trend. They’re already here, and they’re reshaping how teams work, think and prioritise.

If you’re starting to explore where agents can help your organisation, which tasks to automate, what skills your teams will need and how to introduce this safely, we’re already helping clients have those conversations and would be happy to share what we’re seeing.