- Sat, 27 June 2026
Everyone is busier than they have ever been. Managers move from one meeting to another.
Teams, supercharged by AI, generate more ideas, reports, presentations, and output than ever before. And yet people are exhausted, work is reactive, and the impact we are having as an organisation hasn’t improved.
It is not a new phenomenon; in many organisations, the focus has long been on maximising employee workloads and striving for greater efficiencies. Being busy, or at least being seen as busy. Now the talk has turned to how AI will make our organisations 10X more productive, with even greater efficiencies, leading to fewer people, faster delivery, more projects, and infinite scale. Yet the organisation isn’t going faster; leaders will ask questions: AI was supposed to make us faster, so “why aren’t we doing more?”
AI increases our capacity to generate work faster than our organisations can absorb, evaluate, prioritise, and act on it. As AI lowers the cost of creating work, the volume of work expands to fill the available capacity. Many organisations will find that by using AI, the amount of work actually accelerates.
● AI dramatically lowers the cost of generating options.
● More options require more decisions.
● More decisions increase coordination costs.
● More outputs increase the demand for human judgment, review, and validation.
● More experiments create more learning to process.
● More information increases cognitive load.
The bottleneck shifts from production to sense-making. As AI accelerates output, the constraints move to prioritisation, coordination, decision-making, and attention. The problem is that most organisations are designed to optimise production, not make sense in a complex landscape.
People are exhausted, but any gains from AI are swallowed by the inefficiencies within our organisational design, where the dysfunctions are magnified by the very attempt to go faster. Often, the result is going slower, rather than faster. Early evidence suggests that while individuals often report productivity gains from AI, many organisations struggle to translate that into improved organisational outcomes. Individual productivity does not automatically become organisational productivity. The gains made by one person often create additional work for someone else: more ideas to evaluate, more outputs to
review, more decisions to make, and more information to process.
In manufacturing, where demand is predictable, using repeatable processes and maximising resource utilisation can improve efficiency. In knowledge work, there tends to be more uncertainty, dependencies, and variability are high, high utilisation often creates queues, delays decisions, increases context switching, and slows the flow of value. When every person, team, and function is operating at full capacity, there is no room to absorb variation, respond to
opportunities, or solve unexpected problems.
Some organisations realised long ago that if you need to do creative, strategic, or problem- solving work, then focusing on productivity alone doesn’t solve anything. The problem is not people productivity, but the flow of work through a dysfunctional organisational system.
Productivity matters. But productivity without flow, focus, and adaptability creates overload instead of advantage. In stable environments, efficiency creates an advantage. In uncertain environments, adaptability creates advantage.
What if keeping everyone busy is making us less adaptive and actually slowing us down? When leaders start asking this type of question, that is when things can start to change. We can start limiting work in progress to reduce competing priorities and deliver work faster. Leading to fewer meetings and less need for coordination. Teams can allocate time for experimentation and system improvement. People will be more focused on delivering. Then we can start using AI to help us remove friction and deliver more valuable outcomes, not increase volume. There will be objections: “Why do people have spare capacity?”, “Couldn’t we fit in one more project?” Being busy feels safe, whereas slack in the system feels inefficient. It takes leadership to create space for adaptability and to hold firm on the idea that by doing less, you deliver more value to your customers.
In his article No Silver Bullet – Essence and Accident in Software Engineering, Fred Brooks [1] argues that "there is no single development, in either technology or management technique, which by itself promises even one order of magnitude (x10) improvement”. Now, he wrote this in 1986, and was projecting 10 years into the future. Although Brooks wrote specifically about software engineering, his distinction between accidental and essential complexity has become increasingly relevant to knowledge work more broadly. Brooks’ argument distinguishes between different types of complexity. Accidental complexity (friction, tooling limitations, repetitive work, mechanical effort) and essential complexity (ambiguity, human needs, trade-offs, understanding, coordination, creativity, and decision-making). AI may help organisations significantly reduce accidental complexity, but it cannot eliminate essential complexity. The hard bits to get right remain human: understanding customers, making trade-offs, building trust, navigating uncertainty, and deciding what matters. The danger is assuming that removing accidental complexity also removes essential complexity. It doesn’t, and this becomes the central danger of “10x productivity” thinking. AI may help us produce work faster, but it does not tell us which work matters most. Look at your teams: are your busiest teams the most effective? High utilisation creates bottlenecks, encourages context switching, reduces quality, and harms people’s lives as burnout increases.
Adaptability requires slack; slack is the capacity that allows organisations to absorb variation without breaking down. It is the space required for learning, collaboration, experimentation, strategic thinking, and responding to change. Without slack, organisations become efficient at executing yesterday’s priorities while losing the capacity to adapt to tomorrow’s challenges. With appropriate time and space, work becomes more focused. Teams finish more work. People have time to think. AI removes friction rather than creating more work. The organisation becomes calmer, more adaptive, and paradoxically, faster. Patterns to Support Adaptation and Speed There is no single solution for building an adaptable and fast organisation; it requires experimentation with structures, organisational policies, work execution, and especially changes in how we treat people. Here are some of the first patterns to consider.
Managing flow involves monitoring and optimising the movement of work through the organisation. It’s about focusing on what happens to the work, rather than what people are doing. Effective flow management identifies and addresses bottlenecks, ensuring work progresses smoothly from start to finish and enhancing overall value delivery.
A key pattern for improving flow and delivering value to customers fast is to limit the amount of work undertaken concurrently. This increases focus and reduces context-switching, which can lead to bottlenecks and delays. Limiting WIP improves throughput and ensures a smoother flow of work through the system.
Create stable cross-functional teams organised around customer value, responsible for delivering complete products or features from conception to delivery. This approach encourages shared learning and reduces dependence on specialists; it encourages collaboration to produce better outcomes and reduces dependence on external teams.
Implement short, iterative work cycles that include regular feedback sessions to quickly adapt and refine processes and products based on real-time insights.
Many organisations focus on how much work can be completed by individuals and teams, and how much the system can produce (output). However, much of that work might be a waste and ultimately not deliver value to the customer. By focusing on outcomes, we want to maximise value for our customers with the least possible output. A New Type of Organisation Focus on creating the simplest organisation capable of achieving its mission, and, in doing so, a
different organisational culture emerges. People are not permanently overloaded; they have space to think and to improve how they work continuously. By creating a lean organisation, we can use AI intentionally to focus on meaningful outcomes, rather than just producing more output.
Some questions for you to reflect on
● Where are we measuring busyness instead of value?
● What work are we generating simply because AI makes it easy?
● Where are people overloaded with context switching?
● What would happen if we stopped starting so much work?
● What would we learn if teams had more time to think?
Practical moves:
● Reduce the amount of work in progress.
● Measure flow instead of utilisation.
● Create protected focus time.
● Remove low-value meetings/reporting.
● Use AI to reduce friction, not increase demand.
● Create slack for learning and improvement.
In the age of AI, the organisations that win won’t be the ones that keep people busiest. They’ll be the ones who create the space to think, learn, and adapt faster than everyone else.
[1] Brooks, Frederick P. (1986). "No Silver Bullet—Essence and Accident in Software
Engineering".
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