Most conversations about AI focus on its ability to generate outputs such as text, code, or data analysis. These are impressive capabilities, but they may not be AI’s most transformative impact.

The bigger opportunity lies in coordination. According to Asana’s Anatomy of Work Index, in large corporations, 50 to 60 percent of employee time is spent on coordination rather than value creation. Addressing this inefficiency could fundamentally reshape organizational dynamics.

If you’d like to revisit Part I, we explored how enterprises can move beyond coffee-break demos to real productivity.

The Context Window Problem

Humans, like AI models, have limited processing capacity – call it the human context window. To cope, organizations break down complex goals into smaller objectives. The result is fragmented incentives that don’t always add up to the company’s best overall outcome.

Procurement is a classic example. Teams negotiate hard with suppliers to save 10%, but delays in strategic purchases can cost the company far more in missed opportunities. Everyone is optimizing their own metric, while the system as a whole remains suboptimal.

AI agents, with their ability to handle more context and dynamically adjust priorities, could help organizations align toward a single, unified reward function.

The Social Factor

Coordination is also social. Politics, relationship management, and status-seeking often consume more energy than actual decision-making. These dynamics can sometimes spark creativity and culture, but they also create friction and inefficiency.

AI doesn’t care about status or office politics. It simply pursues goals. This neutrality could make coordination dramatically more efficient. The open question is how hybrid human–AI teams will function: will AI’s objectivity enhance collaboration, or will it clash with human habits of hierarchy and influence?

The Principal–Agent Problem

Employees balance company objectives with personal ones such as career advancement, work-life balance, or skill development. These goals naturally shape decisions, but they can also create misalignment.

AI agents, by contrast, have no careers to manage. They can be aligned fully with organizational priorities. That creates efficiency, though it may also cause friction when AI decisions favor company goals over human preferences.

The AI-First Organization

Taken together, these shifts point toward AI-first organizations: companies where AI manages coordination and execution, while humans focus on strategy, creativity, and relationships.

• AI would run workflows, reporting, and routine decision-making.
• Humans would concentrate on vision, innovation, and stakeholder management.
• Middle management layers, whose primary function is coordination, could shrink dramatically.

The result is a leaner, faster, more efficient organization. Less time wasted on alignment; more energy devoted to creating value.

A Challenging but Pivotal Transition

Technical and cultural hurdles remain. Feeding AI with full context, ensuring accuracy, and setting the right guardrails are still hard problems. And organizations are social systems with many dependencies, so transitions won’t be seamless. But the direction is clear. Coordination is one of the biggest bottlenecks in modern work, and AI is uniquely suited to address it. The companies that embrace this shift early may find themselves not just more productive, but fundamentally redesigned for a new era.