First, What Is Agentic AI?
Before diving into the problem, it’s important to understand the technology. Unlike earlier AI that responds to prompts, 'agentic AI' refers to systems that can act autonomously to achieve goals. Think of it as moving from a smart assistant that answers
your questions to a proactive virtual team member that can plan, make decisions, and execute multi-step tasks with minimal human oversight. These AI agents can coordinate with each other to automate entire business processes, from software development to managing complex logistics. This shift from reactive tool to proactive collaborator is what makes agentic AI so powerful, but also so challenging to integrate.
The Surprising Productivity Dip
According to McKinsey's 2026 Technology Trends Outlook, the rapid adoption of these sophisticated tools has not been a universal success story. The report highlights that nearly 30% of surveyed companies saw productivity decline after their teams started using agentic AI. This phenomenon, sometimes called the 'AI productivity paradox', occurs when the expected efficiency gains fail to materialize on the bottom line, despite heavy investment and individual employees reporting personal productivity boosts. The report specifically points to software development, where AI tools dramatically increased coding activity by 180% in one study, but actual shipped product releases only rose by 30%. This shows that more activity doesn't always equal more value.
Why Tech Alone Isn't Enough
The core of the issue isn't the AI itself, but how it's being implemented. McKinsey states that without a systematic approach, introducing agentic tools can lead to unintended negative outcomes. Many companies simply layer these powerful new systems onto old, unchanged ways of working. The real bottlenecks in modern work are often not the tasks themselves, but the handoffs, wait times, approval chains, and coordination between teams. If an AI agent produces a report ten times faster, but it still sits in a manager's inbox for two days waiting for approval, the overall workflow hasn't become more efficient. You are simply automating a broken or inefficient process, which can amplify existing problems rather than solve them.
Redesigning Work for the AI Era
The companies succeeding with agentic AI are those that are not just adopting technology, but are fundamentally redesigning their workflows. This involves mapping out how work actually gets done, identifying the friction points between steps, and reimagining the entire process with AI as a core component. Instead of just automating a task, leaders should ask what the entire workflow should look like if an intelligent agent could handle the coordination. It requires a shift from being 'AI-enabled' to becoming 'AI-first', where business processes are built around the capabilities of intelligent systems from the ground up. This is less of a technology challenge and more of a strategic and organizational one, demanding changes to roles, responsibilities, and how teams collaborate.
The Challenge for Indian Firms
For businesses in India, which are rapidly investing in and adopting AI, this report serves as a crucial guidepost. The temptation to deploy the latest tools to gain a competitive edge is immense. However, the McKinsey findings suggest that the biggest returns will come not from who has the best models, but from who best integrates them into their operations. This requires investment in more than just software; it demands a focus on process analysis, employee training, and a willingness to challenge long-standing operational habits. As companies scale their AI initiatives, managing the lifecycle of these systems—from governance and data quality to security and cost management—becomes paramount to avoid pilots becoming expensive science projects that fail to deliver value.
