Beyond Basic AI: What Are 'Agentic' Systems?
The finding specifically refers to 'agentic AI'. Unlike a simple chatbot or a content generator, an agentic system is an advanced form of AI designed to act autonomously to achieve a goal. It can perform complex, multi-step tasks that would otherwise
require human intervention, such as managing a software development cycle, onboarding a new customer, or even closing the books. These are not just tools that assist humans; they are agents that can execute entire processes. The promise is immense, suggesting a future where human teams supervise fleets of AI agents doing the heavy lifting. This leap in capability is what makes the productivity paradox so jarring—the most powerful AI tools are, in some cases, having a negative impact.
The Productivity Paradox Explained
The core of the issue, as identified by McKinsey's "Technology Trends Outlook 2026," is that companies often introduce these powerful AI agents without fundamentally changing how their teams operate. Simply layering sophisticated technology onto outdated processes creates friction, not efficiency. The report notes that this is particularly evident in software development. In one study, AI tools led to a 180% increase in coding activity, but the number of actual product releases only went up by 30%. This shows that more activity doesn't equal more value. Without a disciplined and systematic approach, this can lead to what the report calls 'vibe coding'—a flurry of AI-generated work that doesn't translate to tangible business outcomes. This misalignment results in operational problems and, ultimately, a decline in overall productivity.
The Missing Link: Strategic Workflow Redesign
The companies that are succeeding with AI are not just buying tools; they are re-architecting work. The research consistently shows that 'high performers'—those attributing at least 5% of their earnings to AI—are far more likely to fundamentally redesign their workflows. Instead of just using AI to automate an isolated task, they reimagine the entire end-to-end process. This involves focusing on the 'handoffs' between teams and systems, where most delays and coordination problems occur. For example, rather than just using AI to write code faster, a redesigned workflow might use AI to automate testing, documentation, and deployment, freeing up human developers to focus entirely on higher-level strategic problems. According to one study, firms that redesigned their processes around AI were more than twice as likely to see significant productivity gains.
Lessons for Indian Businesses
For India's rapidly digitising economy, this finding is a critical lesson. As Indian firms from IT services to banking and manufacturing pour investment into AI, the temptation is to focus on acquiring the latest technology. However, the McKinsey data serves as a strong warning: the technology itself is not a magic bullet. The real return on investment comes from strategic and sometimes difficult organisational change. Leaders must ask not, 'What tasks can we automate?' but rather, 'How can we redesign our entire value chain around what AI makes possible?' This requires moving beyond pilot projects in isolated departments and adopting a holistic view of transformation. The focus must shift from technology implementation to business process re-engineering, ensuring that AI agents are integrated into a system designed for them, not one inherited from a pre-AI era.
