First, What is Agentic AI?
Before diving into the findings, it's important to understand what makes 'agentic' AI different. Unlike a simple chatbot that answers questions, agentic AI is a system designed to act autonomously. It can understand a high-level goal, create a plan with
multiple steps, and execute those steps using various tools and systems with minimal human supervision. Think of it less as a tool you command and more as a proactive digital team member that can manage complex tasks, such as coordinating a supply chain, analyzing market data to execute trades, or even managing aspects of software development. These systems are designed to be adaptable and learn from feedback, continuously improving their performance.
The Productivity Paradox in Action
The McKinsey Technology Trends Outlook 2026 delivered a reality check to the AI hype. It found that while many companies are racing to deploy these powerful new systems, almost one in three saw team productivity decline after implementation. The report highlighted a crucial disconnect: a rise in activity does not always equal a rise in valuable output. For instance, one study noted that while AI tools boosted coding activity by a massive 180%, the number of actual product releases only increased by 30%. This suggests that teams were busier but not necessarily more effective, producing more code that didn't always translate to business value.
The Pitfall of 'Plug-and-Play' AI
So, why does productivity fall? The core issue is that companies often treat advanced AI as a simple plug-and-play solution. They layer powerful new technology on top of old, inefficient, and often fragmented workflows. This approach doesn't fix underlying problems; it often amplifies them. When you automate a broken process, you get a faster broken process. Adding an AI agent can create new bottlenecks, increase coordination overhead, and add friction if the surrounding human roles and processes are not adapted. The problem isn't the AI itself, but the failure to change the organisational structure and processes that the AI is meant to support. The McKinsey report points out this lack of a 'systematic approach' is what leads to these unintended negative outcomes.
Workflow Redesign: The Real Key to Success
The same McKinsey research that identified the problem also pointed to the solution. Companies that successfully leveraged agentic AI for major productivity gains were more than twice as likely to have redesigned their processes before implementing the technology. The most successful AI adopters don't just automate tasks; they reinvent entire workflows. This means moving from simply asking 'where can we use AI?' to asking 'what is the outcome we want, and how can we design the best possible process to achieve it with both humans and AI?' It involves breaking down a process into its smallest parts and thoughtfully deciding which tasks are best for AI (speed, scale, data reconciliation) and which require human judgment (ambiguity, strategy, high-consequence decisions).
What Redesigned Workflows Look Like
In practice, redesigning a workflow means fundamentally changing how work gets done. Instead of a sales team member manually pulling data from three systems and then asking an AI to summarize it, a redesigned workflow might have an AI agent autonomously gather, synthesize, and present the data based on a single trigger. This shifts the human role from a doer to an orchestrator and quality controller. It requires mapping out processes to identify bottlenecks, clarifying decision rights, and defining clear roles for both humans and their new AI coworkers. Companies that succeed don't just give their employees AI tools; they provide clear operating procedures, establish measurable performance indicators, and foster a culture where employees trust and know how to work alongside their digital counterparts.
