"[The causaLens AI Agent] is focused on removing the mundane tasks… one of the steps it’s doing is cleaning data… if you ask any data scientist, they spend 70-80% of their time on [data] cleaning & exploration.

Our causaLens AI Agent is focused on removing this process [for humans] completely. All we are focused on now is drawing insights, not in the process of building pipelines for cleaning the data or joining them. 

This allows our data scientists to focus on higher value tasks.”

If you're running a modern agency, you’ve probably faced this problem: more clients, tighter deadlines, and no time to build the analytics team you really need. For years, Croud—a global marketing agency working with some of the world’s leading brands—solved this by tapping into a global freelance network. When data science capacity fell short, they’d spin up temporary support fast.

But as Puneeth Nikin, Croud’s Head of Data Science, put it: “Speed is everything in an agency. Deliver an insight too late, and it’s already useless.”

Today, they’ve found a new approach. They’re not hiring more people. They’re deploying AI agents.

 

From Talent Bottlenecks to Autonomous Scale

Croud was one of the first marketing agencies to embed our AI Agents directly into their analytics workflow. These agents handle the heavy lifting: ingesting and cleaning data, running exploratory analysis, and accelerating the prep work that used to take hours or days.

Their first agent? An EDA (Exploratory Data Analysis) agent—designed to automate the front end of any analytics request. It cleans incoming data, joins tables, and produces a shareable summary of key variables, outliers, and potential insights in just minutes.

“We deployed an EDA agent that automates the initial analysis phase,” Puneeth explained. “What used to take hours of wrangling now happens automatically. We just tell the agent what we want to explore, and it delivers a structured summary we can act on.”

This doesn’t just make things faster, it raises the floor on what’s possible. Teams can spin up quality analysis instantly, even if a data scientist isn’t available.

In practice, this means Croud can deliver faster and better work without needing to scale its full-time team. That’s a game-changer in the agency world, where margins are tight and timelines even tighter.

 

Beyond Productivity: Changing the Consulting Model

What makes this shift more than just a tech upgrade is what it unlocks.

Before agents, Croud’s team had to train every new freelancer, manage handoffs, and accept the trade-off between scale and specialization. Now, with agents tailored to their domain knowledge, they’ve eliminated the onboarding lag and started to think bigger.

“It’s not just about replacing tasks. It’s about orchestrating entire analytics workflows, end to end,” Puneeth said. “As agents take on more of the pipeline, our data scientists become supervisors—overseeing strategy, model design, and delivery.”

In other words, the AI doesn’t just make humans faster, it frees them to do better work.

 

Implications for Agencies: More Than Automation

What’s happening at Croud hints at a broader shift in how agencies will operate in the AI-native era.

Here’s what that future looks like:

• Fewer ad-hoc hires. Instead of scrambling for freelance support, agencies will spin up agents trained on their own data, processes, and domain expertise.
  
• Faster onboarding. A custom agent doesn’t need weeks to “ramp up”; it’s trained once and deployed across teams. 
• Higher margins. By reducing manual labor and rework, agentic delivery models help agencies escape the time-for-money trap. 
• Democratized insights. Teams outside of data science, from PPC to social to brand, can now interact with data through agents, without needing SQL or dashboards. 
• Strategic repositioning. With data scientists moving into orchestration roles, agencies can deliver more sophisticated, more customized, and more scalable analytics than ever before.

And that last point is key. When agencies evolve from “service providers” to “intelligence orchestrators,” the entire value proposition changes; they’re not just delivering decks, they’re delivering decisions.

 

What’s Next at Croud?

Puneeth and team aren’t stopping at one agent. They’re already working on expanding the agent layer to cover more of the workflow—building toward a fully orchestrated analytics system that adapts to client needs in real time.

It’s not a vision of the future. It’s what’s happening now.

As Puneeth said:

“The structure of what a data scientist does today will look very different tomorrow. And the work causaLens is doing is accelerating that shift.”