Why Reducing Complexity Is Now A Leader's Most Important Job
AI was expected to simplify work. In reality, teams now have more tools, more output to review and more ways to complete the same task than they had a few years ago.
According to ActivTrak's "2026 State of the Workplace" report, the average company used two AI tools in 2023, and by last year that number had grown to seven, with most companies running at least six.
According to ActivTrak's "2026 State of the Workplace" report, the average company used two AI tools in 2023, and by last year that number had grown to seven, with most companies running at least six.
Yet according to the same report, after adopting AI, employees spent more time on email, messaging and administrative work—not less. AI removed some effort, but it also introduced another layer of decisions: what to automate, what to review, when to trust the output and how to coordinate work done not only by humans but also by agents.
For years, much of management revolved around coordinating work: assigning tasks, reviewing progress and making sure information reached the right people. AI is now taking over more of that coordination. A Harvard Business School study of developers using GitHub Copilot, reported in Harvard Business Review, found that once teams adopted AI, they spent more time on their core work and less on project management and coordination. Stanford's research on changing workplace skills explores how the key human skills are shifting: As AI takes over more information processing, the value of organizing work, communicating clearly and helping people collaborate continues to rise.
This has changed what a leader is for. Teams depend far less on leaders for information and coordination. Now the most important leadership job is to reduce the complexity of the modern workplace.
The catch is that complexity tends to accumulate. When we want to improve something, our instinct is to add. We add a new tool or a new team member to help with the workload or a new project. But with every addition, the level of complexity only increases.
Research published in Nature suggests that this isn't just how organizations behave—it's also how people think. Across eight experiments involving more than 1,500 participants, researchers found that people consistently looked for ways to add rather than remove, even when subtraction produced an equally good solution. The busier people felt, the less likely they were to consider simplifying at all.
Adding is easier to think through, and there is a cultural pull behind it, since we tend to read "more" as ambition and measure ourselves by what we have built rather than by what we have set aside. Reducing complexity means working against that instinct and treating subtraction as a deliberate practice. It means you need to decide what's no longer relevant to make priorities easier to see and give people the space to focus on work that actually matters.
For years, much of management revolved around coordinating work: assigning tasks, reviewing progress and making sure information reached the right people. AI is now taking over more of that coordination. A Harvard Business School study of developers using GitHub Copilot, reported in Harvard Business Review, found that once teams adopted AI, they spent more time on their core work and less on project management and coordination. Stanford's research on changing workplace skills explores how the key human skills are shifting: As AI takes over more information processing, the value of organizing work, communicating clearly and helping people collaborate continues to rise.
This has changed what a leader is for. Teams depend far less on leaders for information and coordination. Now the most important leadership job is to reduce the complexity of the modern workplace.
The catch is that complexity tends to accumulate. When we want to improve something, our instinct is to add. We add a new tool or a new team member to help with the workload or a new project. But with every addition, the level of complexity only increases.
Research published in Nature suggests that this isn't just how organizations behave—it's also how people think. Across eight experiments involving more than 1,500 participants, researchers found that people consistently looked for ways to add rather than remove, even when subtraction produced an equally good solution. The busier people felt, the less likely they were to consider simplifying at all.
Adding is easier to think through, and there is a cultural pull behind it, since we tend to read "more" as ambition and measure ourselves by what we have built rather than by what we have set aside. Reducing complexity means working against that instinct and treating subtraction as a deliberate practice. It means you need to decide what's no longer relevant to make priorities easier to see and give people the space to focus on work that actually matters.
Three Places Leaders Can Reduce Complexity
In practice, there are three places where simplifying the work makes the biggest difference.
The first is decision-making.
Most leaders I coach make too many decisions, and many of those should be handled by their teams. When complexity is high and there's not enough clarity in decision-making, leaders absorb all the gray areas and become the go-to people for making most decisions.
As I've written before, teams move much faster when everyone understands which decisions belong to the leader, which require approval and which are theirs to make.
The second is role clarity.
When you add AI to existing workflows, you only increase busyness. HR leaders I interview on my Built by People Leaders podcast share that success comes only when you redesign the work process and create absolute clarity about what belongs to humans and what belongs to AI. Without that, people hold on to tasks that no longer need them and stay unsure what their job is now for.
The third is making sense of change.
When organizations don't explain why work is changing, people tend to create their own explanations. They wonder what AI means for their role, whether expectations have shifted or whether they're focusing on the right priorities. Those questions don't stay in people's heads for long. They show up as hesitation, second-guessing and unnecessary coordination.
This is one responsibility that becomes more valuable as AI becomes more capable. As I've argued before, leaders who help people make sense of change do that by facilitating meaningful conversations, not by providing information.
If you are leading teams in the AI-driven workplace, don't add automatically. Think about creating clarity through subtraction. Remove unnecessary approval processes, and remove tools or tasks that don't add real value. People can find information on their own. What they can't look up is where the organization is going and how they fit into it.
Follow me on X or LinkedIn. Check out my website.
This article was originally published on Forbes Coaches Council
In practice, there are three places where simplifying the work makes the biggest difference.
The first is decision-making.
Most leaders I coach make too many decisions, and many of those should be handled by their teams. When complexity is high and there's not enough clarity in decision-making, leaders absorb all the gray areas and become the go-to people for making most decisions.
As I've written before, teams move much faster when everyone understands which decisions belong to the leader, which require approval and which are theirs to make.
The second is role clarity.
When you add AI to existing workflows, you only increase busyness. HR leaders I interview on my Built by People Leaders podcast share that success comes only when you redesign the work process and create absolute clarity about what belongs to humans and what belongs to AI. Without that, people hold on to tasks that no longer need them and stay unsure what their job is now for.
The third is making sense of change.
When organizations don't explain why work is changing, people tend to create their own explanations. They wonder what AI means for their role, whether expectations have shifted or whether they're focusing on the right priorities. Those questions don't stay in people's heads for long. They show up as hesitation, second-guessing and unnecessary coordination.
This is one responsibility that becomes more valuable as AI becomes more capable. As I've argued before, leaders who help people make sense of change do that by facilitating meaningful conversations, not by providing information.
If you are leading teams in the AI-driven workplace, don't add automatically. Think about creating clarity through subtraction. Remove unnecessary approval processes, and remove tools or tasks that don't add real value. People can find information on their own. What they can't look up is where the organization is going and how they fit into it.
Follow me on X or LinkedIn. Check out my website.
This article was originally published on Forbes Coaches Council