First, What Is Agentic AI?
Before diving into the findings, it’s crucial to understand what ‘agentic AI’ is. Unlike generative AI tools like ChatGPT that respond to prompts, agentic AI systems are designed to be autonomous. They can independently plan, make decisions, and take
actions to achieve a specific goal with minimal human oversight. Think of them not as assistants you command, but as digital employees that can manage complex, multi-step tasks from start to finish. For example, an agentic AI could manage an entire supply chain reordering process, from monitoring inventory levels to placing orders with suppliers and tracking shipments, all on its own.
The Productivity Paradox Explained
According to McKinsey's 2026 Technology Trends Outlook, the rapid adoption of these sophisticated tools has led to an unexpected outcome for many. The report found that productivity declined in nearly 30% of companies after their teams started using agentic AI, particularly in software development. The core issue is that many organisations simply layer this new technology onto their existing processes without fundamentally changing how work gets done. This approach, which McKinsey calls “vibe coding,” can lead to unintended consequences. For instance, one study cited in the report showed that while AI tools boosted coding activity by a staggering 180%, the number of actual product releases only increased by 30%. More activity, in this case, did not translate to more value.
The Workflow Redesign Imperative
The companies that succeeded with agentic AI did one thing differently: they redesigned their workflows first. Simply handing a powerful tool to a team without changing their processes, roles, and goals is a recipe for failure. Effective workflow redesign involves reimagining the entire end-to-end process with AI in mind. This means identifying which tasks are best left to AI agents, which require human oversight, and how they should interact. According to McKinsey, organisations that redesigned their processes before integrating AI were more than twice as likely to see productivity gains exceeding 20%. This often involves creating smaller, more autonomous teams and redefining roles to focus on higher-value strategic work, while AI handles repetitive or routine tasks like documentation and initial drafts.
Hurdles Beyond Implementation
Even with a perfect strategy, significant limits to agentic AI remain. A major obstacle is trust. The McKinsey report highlighted a crucial trust gap, with 46% of developers admitting they actively distrust the accuracy of AI tools. Only a tiny 3% said they highly trust AI-generated outputs. This scepticism can stall adoption and prevent teams from fully leveraging the technology. Beyond trust, there are technical and operational hurdles. Poor data quality can lead to poor AI decisions, as these systems are only as good as the information they are fed. Furthermore, the massive computing power, energy, and network capacity required for agentic AI present significant infrastructure challenges and costs for many companies.
The Path Forward for Indian Firms
For business leaders in India, this report serves as a critical guide. The temptation to rush into AI adoption is strong, but the evidence suggests a more measured, strategic approach is required. The focus should not be on acquiring AI tools, but on building an AI-ready organisation. This starts with investing in change management and upskilling the workforce to collaborate effectively with autonomous systems. It means prioritizing data governance to ensure the AI has clean, reliable data to work with. Most importantly, it requires leadership to champion a fundamental redesign of business processes. The goal isn't just to automate existing tasks, but to reimagine how value is created and delivered in an AI-powered future.
