First, What Is Agentic AI?
Before diving into the numbers, it’s important to understand what we mean by 'agentic AI.' Unlike familiar tools like chatbots or generative AI that respond to direct commands, agentic AI systems are designed to be proactive and autonomous. They can independently
set goals, make decisions, and execute multi-step tasks to achieve a broader objective with minimal human supervision. Think of an AI system that doesn't just write marketing copy when asked, but can plan and execute an entire email campaign, analyze its performance, and adjust its strategy based on the results. These AI 'agents' can communicate with each other and other software systems, acting as a coordinated, autonomous workforce. The goal is to automate not just simple tasks, but complex workflows.
The Productivity Paradox Explained
The central finding from McKinsey's 2026 Technology Trends Outlook is that this powerful technology doesn't guarantee success. While many individuals report personal productivity boosts from AI, enterprise-level gains are proving elusive for many. The report found that nearly one-third of companies saw team productivity decline after introducing agentic AI tools. This phenomenon, sometimes called a "productivity J-curve," can occur when new general-purpose technologies are implemented. There's an initial dip as the organization struggles with integration before the benefits begin to materialize. The McKinsey report specifically points to operational problems that arise when companies simply layer these advanced tools onto existing, unchanged processes.
More Activity Doesn't Mean More Value
A key reason for the productivity drop is a frequent mismatch between activity and actual value. In the context of software development, McKinsey noted that introducing agentic AI often leads to a massive increase in the volume of code being written. However, this doesn't automatically translate to better or more products. One study cited in the report found that while AI tools boosted coding activity by an incredible 180%, the number of shipped product releases only went up by 30%. This highlights a critical lesson: agentic AI can make people busier without making the business more effective. Without clear goals and redesigned processes, teams can end up spinning their wheels, generating low-value output at a faster rate.
The Problem: Automating Broken Processes
The core issue identified by McKinsey is that AI agents don't fix flawed workflows; they amplify them. When a company introduces an autonomous system into a process that is already inefficient, siloed, or poorly coordinated, the AI simply executes those broken steps faster and at a greater scale. The friction and bottlenecks that previously slowed down human teams become high-speed roadblocks for AI. High-performing organizations, the report notes, are far more likely to have fundamentally redesigned their workflows around AI's capabilities. Those that merely layered the technology on top of old ways of working were less than half as likely to see significant productivity gains. Success isn't about the tool itself, but about re-imagining how work gets done.
The Path Forward for Indian Businesses
For the rapidly digitizing Indian market, this report serves as a vital cautionary tale. The allure of leaping ahead with the latest AI is strong, but the investment is wasted without groundwork. Business leaders should not ask, "How can we use agentic AI?" but rather, "How must our workflows change to unlock the value of agentic AI?" This involves mapping out end-to-end processes and identifying the handoff points and coordination costs between teams—the very areas where agentic AI can deliver the most value. It also means addressing the human side. A significant trust gap exists, with a McKinsey study showing 46% of developers actively distrust the accuracy of AI tools. Building trust, upskilling employees, and managing the integration of a hybrid human-AI workforce are just as critical as the technology itself.
