Data teams at WeWork, Glassdoor, and Foursquare are already on Airflow 3. Are you?

Join the Airflow experts at Astronomer on Save your spot

Sponsored

Welcome to DataPro Issue 160.
This week, we’re bringing you a powerful edition packed with expert insights from across the AI landscape. In our latest Packt Talks session, Abhishek sits down with

Only 10 seats left. Join Book Now and Save 20% – Only 10 Seats Left

Cheers,

Merlyn Shelley

Growth Lead, Packt

Advertise with Us

Every company believes their automation is working, until it suddenly breaks. A small formatting change, a new policy variation, or a shift in user behavior can turn even the most expensive automated workflow into a bottleneck. This is the silent problem most businesses face: systems built to save time often create more manual work in the long run.

In the latest episode ofPackt Talks, our GLAbhishek Kaushikdives into this challenge withPablo Moreno, AI Product Manager atBoard, a leader in financial planning and performance management software. Together, they unpack why traditional automation struggles to keep up with modern complexity and howagentic AIis rewriting the blueprint for scalable, resilient operations. The conversation sets the foundation for a deeper exploration of real-world cases, emerging frameworks, and the future of human-AI collaboration, insights that shape the rest of this article.

The Automation Myth: Why Your Workflows Still Break

Traditional automation has helped companies save time for decades, but its biggest weakness remains unchanged: it can’t adapt. When business conditions shift, when documents look different, or when people ask slightly different questions, automated systems often fail. This mismatch between rigid workflows and fast-changing environments has widened over the last decade. Agentic AI steps in as a new paradigm, one where automation doesn’t just follow rules but makes decisions within defined boundaries.

Takeaways

Automation saves time but fails in dynamic, unpredictable scenarios.
The pace of change in the last 5–10 years has outpaced deterministic workflows.
Agentic AI thrives where traditional automation breaks, handling complexity without endless reprogramming.
77% of enterprises are exploring agentic AI, with 30% already seeing major reductions in manual work.

Problem in Focus: When Rule-Based Automation Turns into Technical Debt

Many organizations rely on hyperautomation, systems built on stacks of rules, templates, and RPA bots. But every new rule becomes a maintenance cost. A single formatting change can break an automated pipeline. In the pharma industry example Pablo Moreno shares, scanning marketing documents for legal disclaimers turned into an expensive cycle of bot retraining.

Takeaways

Rule-heavy automation becomes brittle and expensive at scale.
Minor variations (orientation, typos, layout changes) often break RPA workflows.
Over-reliance on deterministic rules leads to high maintenance debt.
The real cost of automation isn’t deployment — it’s constant upkeep.

How Agentic AI Fixes the RPA Problem

Agentic AI doesn’t rely on rigid patterns. It understands context, recognizes variations, and adapts to change. In the pharma compliance case, agents could detect legal phrases regardless of spelling errors, text rotation, or synonyms. Instead of adding rules, the system learned patterns. This shift replaced hours of manual review and repetitive bot tuning.

Takeaways

Agentic AI extracts meaning, not formatting, reducing the need for rules.
Agents adapt to document variance with little intervention.
Maintenance drops because the system generalizes from examples.
Compliance workflows become faster, cheaper, and more reliable.

Why Crew AI Works: Solving the Complexity Problem

Choosing the right framework matters. Pablo highlights Crew AI as his preferred approach because it enables multi-agent processes while retaining simplicity. Unlike low-code/no-code tools that struggle once workflows become nonlinear or multi-context, Crew AI supports scalable architectures, evolving use cases, and quick iteration cycles.

Takeaways

Crew AI enables multi-agent workflows without heavy engineering overhead.
Low-code/no-code tools are fast but tend to break once complexity grows.
Crew AI supports new agents, tools, integrations, and iteration loops.
Scalability and adaptability should guide platform decisions, not speed alone.

The HR Chatbot Challenge: When Reality Is Messy

A multinational pharma company attempted to build an HR chatbot for 100,000 employees across 40 countries. The complexity was enormous: policies varied by 20–30 percent, acronyms differed across teams, and employee questions were full of local nuance. The breakthrough came from building a robust knowledge base, including an acronym dictionary, and extensively training the system to recognize contextual differences.

Takeaways