First, What Is Agentic AI?
Before diving into the productivity puzzle, it’s crucial to understand what 'agentic AI' is. Unlike generative AI models that respond to prompts, agentic AI systems are designed to be proactive and autonomous. They can independently set goals, make decisions,
and execute multi-step tasks across different software systems to achieve a desired outcome with minimal human intervention. Think of it as moving from a smart assistant that can draft an email for you, to an autonomous chief of staff that can plan an entire marketing campaign, deploy the materials, and adjust the strategy based on performance data. This capability to act with 'agency' is what separates these advanced systems and fuels their transformative potential.
The Surprising Productivity Paradox
The central claim from a recent McKinsey Technology Trends Outlook 2026 is that this powerful technology doesn't automatically equate to gains. The report found that productivity actually fell in nearly 30% of companies after their teams started using agentic AI. This paradox is stark: while many individual employees report feeling more productive with AI tools, organisations as a whole often fail to see a positive impact on the bottom line. In one study cited by McKinsey, AI tools led to a 180% increase in coding activity, but the number of shipped product releases only went up by 30%. This shows that more activity doesn't always lead to more valuable output.
Why New Tech Fails in Old Systems
The core reason for this failure is surprisingly simple: inserting powerful new technology into outdated workflows. When companies deploy AI agents without fundamentally changing how work gets done, they often create new problems. Productivity can decline because employees end up spending more time managing the AI, verifying its output, or cleaning up work that doesn’t fit existing processes. A significant trust gap also exists; one report noted that 46% of software developers actively distrust the accuracy of AI tools, leading to time-consuming verification cycles. Automating a broken or inefficient process doesn't fix it; it just makes the broken process run faster, amplifying its flaws and creating new bottlenecks.
The Solution: A Workflow Revolution
The companies that succeed with agentic AI are the ones that treat its adoption not as a technology project, but as an operating model transformation. This is what McKinsey means by "effective workflow redesign." It involves questioning the entire purpose of a process and rebuilding it with human-agent collaboration at its core. Instead of just automating a single task, leaders must map out how information flows, where decisions are made, and how AI can reduce the 'coordination tax'—the time and effort wasted in handoffs between teams and systems. According to another McKinsey survey, organisations that redesigned their processes before incorporating AI were more than twice as likely to see significant productivity gains.
From Implementation to True Integration
For businesses in India and beyond, the lesson is clear: agentic AI is not a plug-and-play solution. Success requires a deliberate strategy. This begins with involving the employees who actually do the work in the redesign process to ensure the new system is practical and trusted. It means starting with specific, high-impact workflows and measuring the outcomes, not just the activity levels. Ultimately, it requires a cultural shift where continuous adaptation becomes the norm. The competitive advantage in the age of AI will belong to the organisations that master the art of re-architecting how people and intelligent systems work together.
