Why AI-First Companies Can’t Afford Traditional Hierarchies

Last week, a recent computer science graduate on my team presented an AI workflow directly to me that solved a client pain point. The meeting took 20 minutes. We implemented it the next day. 

At my previous company, where I led a performance marketing agency’s solutions and marketing team of dozens of people, the employee would have presented the idea to their manager, who would have refined it and brought it to a director, who would have packaged it for a VP review. Three weeks later, it might have reached my desk if the momentum hadn’t faded. 

The difference isn’t that we’re working faster. The difference is that we’re structured differently. 

Today, we produce comparable volume with a smaller team because AI handles the coordination, documentation, and information flow that used to require all those middle layers. This frees people at all levels to work directly together on what actually creates value. 

How Traditional Hierarchies Create Friction with AI 

For decades, hierarchies made sense because humans were the only option to synthesize information, coordinate work, and train people. AI handles most of that coordination automatically. Agents schedule meetings, record conversations, surface information in real time, and build searchable institutional memory. When that happens, layers that used to add value can start creating delays instead. 

Our senior team works directly with every level of the organization. We’re pairing decades of strategic experience with tech-native talent who can rapidly implement AI solutions. AI handles the logistics and coordination that would normally require three management layers, freeing senior leaders to share experience and help people develop judgment. 

The Capacity Question Every Leader Is Asking 

Traditional thinking says if you want to double output, you need to double headcount. AI changes the equation completely. 

In the past, publishing one piece of thought leadership required three weeks and four people. Today, my marketing lead and I have sessions where I discuss ideas, she captures them, and AI helps her turn those conversations into content at a scale we couldn’t have imagined. She’s doing work that would have previously required a full content team. 

What this means in practice: 

• Publishing cadence: One piece per three weeks → three pieces per week 

• Team enablement: AI amplifies what each person can accomplish 

• Approval cycles: Three weeks → same-day implementation 

• Learning curve: Years to access senior thinking → immediate mentorship 

I’ve led this transformation from both sides: scaling a performance marketing agency to hundreds of employees with a traditional structure, and now helping established enterprises reorganize for AI. The question is whether AI fundamentally changes the relationship between team structure and output capacity. The answer is yes, at any scale. 

Why Leaders Must Lead This Transformation Actively 

A couple of years ago, I became certified in change management through Prosci. Interestingly, I was the only CEO in my class. 

What I realized during that training was that I had been asking the wrong questions. I was focused on why change wasn’t happening fast enough, rather than understanding what was holding it back. 

Change fails predominantly for two reasons: lack of awareness — if people don’t understand the why behind a change, they’ll resist it — and lack of active, visible sponsorship from leaders. Change cannot be delegated. 

The challenge I’m seeing is that many leaders are removed from the daily work. The prospect of driving AI transformation without making a genuine effort to understand the tools creates barriers that slow everything down. 

I spend significant time every week tracking AI releases, testing new tools, and working directly with our team on implementations. I can’t lead a transformation I don’t understand. 

The Data Problem That Surfaces Faster in Flat Organizations 

When we launched our AI consultancy, we assumed data readiness would be our entry point. But clients found discussions about data architecture overwhelming, so we started instead with: What could AI help you accomplish? 

As soon as clients see what’s possible, they begin imagining agents that solve real pain points and quickly discover they lack the data needed to build them. 

Flatter organizations hit this wall sooner, and that’s an advantage. 

In hierarchical structures, someone in the middle manually fixes data inconsistencies before they reach leadership. Problems stay hidden for years. In flatter organizations, when someone builds an agent that fails because of inconsistent naming conventions or messy records, the issue surfaces immediately and gets addressed at the system level. 

Sequential Steps That Make Organizational Change Stick 

Through Prosci, I learned about the ADKAR framework for change management: Awareness, Desire, Knowledge, Ability, and Reinforcement. These are sequential steps you can’t skip. 

When I talk to clients about AI transformation, I hear: “We don’t have budget,” “We don’t have time,” “Our team doesn’t have the skill sets.” Those aren’t really the problems; those are symptoms of being stuck at the Awareness or Desire stage. 

Here’s how to apply this framework: 

• Awareness: Share specific examples of where approval layers delay implementation. 

• Desire: Connect the change to individual benefit. Will people have more time for strategic work? Will they learn skills that advance their careers? 

• Knowledge: Provide training on AI tools and on new ways of working with fewer layers. 

• Ability: Give people real projects where they can practice new skills. Pair emerging talent with senior leaders on actual client work. 

• Reinforcement: Celebrate wins publicly and track metrics that show capacity improvement relative to team size. 

A Practical Roadmap for Leaders 

If you’re considering how AI might change your organizational structure, here’s what I’m learning works: 

1. Start With One High-Friction Workflow: Find one team where people spend more time coordinating than executing.Identifywhich steps are coordination versus actual value creation. 
2. Pair Senior LeadersWithEmerging Talent Directly: Remove intermediate layers for one project. Use AI tools to handle scheduling, documentation, and progress tracking. 
3. Let AI Replace Coordination Work: Build or adopt agents that handle meeting scheduling, note-taking, progress reporting, and document organization.
4. Measure Capacity, Not Headcount: Stop tracking team size and start tracking output. When you measure capacity instead of headcount, you see where AI creates leverage.
5. Lead VisiblyWithAI Tools: Your team needs to see you working differently before they’ll believe the transformation is real. Demonstrating that experimentation matters creates the psychological safety that allows rapid adaptation. 
6. Surface and Fix Data Problems Early: When agents fail because of data inconsistencies, treat that as valuable information about system-wide issues.
7. Celebrate Learning Over Perfection: Creating a culture that values experimentation over perfection is what allows continuous adaptation.

What This Means for the Future of Work 

Across industries I’ve worked in—trading, digital marketing, and now AI—the organizations that restructured early gained meaningful advantage. Companies that restructure now will likely have 12–18 months of learning advantage before this becomes standard practice. 

The traditional corporate ladder will change. Coordination roles shrink. The new path rewards judgment, adaptability, and the ability to apply AI to business problems. People who thrive will be the ones energized by continuous learning and ambiguity. 

The transformation is happening whether we’re ready or not. The question is whether we’re shaping it intentionally or letting it shape us.