Advanced Analytics in an Agentic World
Analytics used to be pretty straightforward: you take a handful of human analysts, put them in a room and leave them to dig out insights that give your business an edge.
We're moving into a world where it isn't just human teams doing the heavy lifting. It's teams plus AI agents. Together, they’re shrinking the time from raw data to real insights.
It's not hyperbole to say tha this is the biggest shift the analytics industry has ever faced.
New Ways of Working
How humans and AI collaborate is shaping up into two main patterns:
1. Human-in-the-Loop
Analysts working with AI copilots. Not handing over control, but partnering. Copilots suggest, help clean data, highlight interesting patterns with analysts as they both work through a problem together.
2. Agent Delegation
Other times, it’s about handing off specific tasks. Agents take on jobs—analysing, cleaning, building—and report back. These agents are take Like having an extra set of hands that works around the clock and doesn’t miss deadlines.
Types of Agents
There’s no one-size-fits-all here. Different work needs different kinds of agents.
Basic Analysts
The everyday workhorses: basic exploratory data analysis, quick reports, simple charts. You might imagine this as your default, off-the-shelf analyst, not specialised to any particular type of analysis but generally capable.
Industry/Company-Specific Analysts
Every industry, every company, has its own language around data. The way a hospital measures success isn’t the same as a SaaS startup or a supply chain company. We’ll need agents tuned to those nuances—ones that "get" the way different teams think.
Data Engineers
Good analysis starts with good data. These agents help clean up the mess—old spreadsheets, mismatched schemas, systems that were never designed to talk to each other. This is arguably the highest entropy (i.e. hardest to automate) task in advanced analytics.
Data Scientists
When the goal is building live, predictive systems that improve operations vs basic analytics, data scientists are needed. These agents will specialise in building solutions like churn prediction, dynamic lead scoring, anomaly detection using data cleaned up by data engineering agents.
Where This Is Headed (6–12 Month Horizon)
Over the next year, agents will quietly but fundamentally reshape how analytics gets done.
Leading organisations will shift from doing analysis to prioritising it. With agents able to explore datasets, draft models, and surface patterns independently, the real constraint becomes human judgment: What’s worth acting on? Strategic decision-making will happen faster, fuelled by a constant flow of machine-generated insights.
Smaller organisations will close the capability gap. What once required teams of analysts (wrangling data, building models, designing dashboards) will be within reach for much leaner teams. But the flood of insights won’t all be good. The challenge will shift from scarcity to discernment: being able to filter noise from signal when everything is one click away.
Across the board, organisational structure will bend. Roles built around manual throughput (report prep, dashboard maintenance, data cleaning) will thin out. In their place, new functions will emerge: agent orchestrators, AI auditors, prompt library curators.