The AI Productivity Paradox
A recent McKinsey report, the 'Technology Trends Outlook 2026', has sent a ripple of concern through the business world. It found that nearly one-third of companies saw productivity actually decrease after implementing agentic AI tools. This counterintuitive
finding highlights a growing paradox: simply acquiring powerful technology does not guarantee a positive result. The report specifies that operational problems arise when these advanced AI systems are introduced without fundamentally changing how teams operate. In essence, companies are trying to fit a revolutionary tool into an old-fashioned process, leading to friction, confusion, and a decline in output.
What Is Agentic AI?
Unlike the generative AI copilots many office workers are now familiar with, which assist with tasks like drafting emails or summarizing documents, agentic AI is a step beyond. These are autonomous or semi-autonomous systems designed to reason, plan, and execute multi-step tasks with minimal human intervention. In fields like software development, this could mean an AI agent that not only writes code but also tests it, debugs it, and prepares it for release. The problem, as McKinsey’s research points out, is that more activity does not always equal more value. One cited study found that while AI tools boosted coding activity by a massive 180%, the number of actual product releases only increased by 30%.
Automating a Broken Process
The primary culprit for this productivity drop is the failure to redesign workflows. Many organisations are simply layering expensive AI onto existing, often inefficient, processes. If a workflow relies on a series of manual handoffs, email approvals, and fragmented data sources, an AI agent cannot magically fix it; in many cases, it can make the chaos worse. Experts call this 'automating a broken process'. The AI may perform its specific task faster, but it then runs into the same bottlenecks and communication gaps that slowed down human workers. True gains come not from improving isolated tasks, but from reimagining the entire end-to-end workflow around what the new technology makes possible.
The Critical Human Element
Technology adoption is also a human challenge. The same McKinsey report highlighted a significant trust gap among the very people expected to use these new tools. Globally, 46% of developers said they actively distrust the accuracy of AI tools, compared to only 33% who trust them. A mere 3% reported having high trust in AI-generated outputs. Without trust and buy-in from employees, even the most sophisticated AI system will fail. If developers spend more time verifying and correcting AI output than it would have taken to do the work themselves, productivity inevitably suffers. This resistance is a clear signal that implementation requires more than just a software license; it demands training, clear communication, and a new organizational mindset.
The Path to a Real AI Payoff
So, how can companies avoid this trap? The answer lies in putting strategy before technology. According to multiple McKinsey studies, workflow redesign has the single strongest correlation with achieving financial impact from AI. High-performing organizations are over three times more likely than their peers to fundamentally redesign workflows. This means mapping out processes to be machine-readable, eliminating human workarounds, and establishing clear protocols for how AI agents and human employees interact. The focus should shift from 'How can we use this AI tool?' to 'What is the business problem we are trying to solve, and how can we redesign our process with AI at its core to solve it?'. This approach requires upfront investment in process analysis and change management, but it is the only reliable path to unlocking the true value of agentic AI.
