First, What Is Agentic AI?
Before diving into the paradox, it’s crucial to understand what makes 'agentic' AI different. Unlike standard AI that responds to a prompt (like ChatGPT), agentic AI can perform multi-step tasks autonomously to achieve a goal. Think of it as the difference
between asking a helper for a recipe and having a chef who can find the recipe, check the pantry for ingredients, go to the store for what's missing, and then cook the meal with minimal supervision. In a business context, these AI agents can manage complex workflows, from qualifying sales leads and booking meetings to re-routing supply chains based on real-time weather data. They are designed to be proactive, adaptable, and goal-driven.
The Productivity Paradox Explained
So, why would such a powerful technology cause productivity to drop? The recent 'Technology Trends Outlook 2026' from McKinsey pinpointed the problem: companies are introducing these advanced tools without changing how their teams actually work. In about 30% of the surveyed companies, the rollout of agentic AI led to a productivity decline because they simply layered new tech onto old, inefficient processes. This creates several issues. First, there's a disconnect between activity and value. One study cited by McKinsey found that while AI tools increased coding activity by 180%, the number of shipped product releases only rose by 30%. More code doesn't automatically mean more value. Second, without new workflows, employees spend more time managing and switching between various AI tools, a phenomenon that can cost up to 40% of productive time. Finally, there is a significant trust issue; another McKinsey finding notes that 46% of developers actively distrust the accuracy of AI tools.
Workflow Redesign: The Non-Negotiable Step
The successful companies aren't just buying AI; they are fundamentally rethinking their processes. Workflow redesign is the structured reshaping of how work gets done—changing the steps, sequence, handoffs, and decision points. It's about asking, "If we were building this process from scratch today with AI, what would it look like?" instead of "How can we use AI in our current process?". This means identifying bottlenecks, eliminating unnecessary approvals, and clarifying what tasks remain human, what AI will support, and when a human needs to intervene. Simply automating a flawed or overly complex process just makes the bad process run faster, embedding its inefficiencies into your new technology stack. True gains come from designing simpler, smarter workflows where AI and human employees collaborate effectively.
A Blueprint for Success
For businesses in India and beyond looking to harness agentic AI without falling into the productivity trap, the path forward is strategic. The first step is to treat AI adoption as a change management challenge, not just a technical one. Instead of a broad rollout, start by targeting one specific, high-priority workflow. Analyse the current process to understand its pain points before sketching a future state that leverages AI's strengths. Crucially, this involves defining clear governance and getting buy-in from the employees who will use the tools. Success isn't measured by how many AI tools are deployed, but by improvements in concrete business outcomes like faster cycle times, higher quality output, and better customer satisfaction. The McKinsey report makes it clear: technology alone is not the differentiator. The competitive advantage comes from redesigning how work flows through the organization.
