From Tools to Coworkers: How the Rise of Agentic AI is Changing Management Dynamics
- Shefali Korke
- Jan 16
- 3 min read
A recent survey revealed a striking statistic: 76% of executives now see agentic AI as a coworker rather than just a tool. This shift is more than a change in wording. It signals a new way organizations think about AI’s role in the workplace. This change affects how managers lead their teams and interact with AI systems.
The Difference Between Tools and Coworkers
Understanding the difference between tools and coworkers is essential for adapting management styles.
Tool thinking focuses on using AI to speed up tasks. For example, using a spreadsheet to organize data or a hammer to drive nails. The tool does exactly what you tell it to do, nothing more.
Coworker thinking means treating AI as an independent agent. You delegate tasks, provide context and limits, and trust the AI to find the best way to complete the work. Instead of directing every step, you review the AI’s output and provide feedback.
When AI acts as a coworker, managing it requires skills similar to managing people. This means communication, delegation, and evaluation become more important than just technical know-how.
What This Means for Day-to-Day Management
If AI is a coworker, managers must adjust their approach. Managing AI agents involves clear communication and setting expectations, much like managing human team members.
Clear Delegation
Vague instructions lead to poor results, whether for humans or AI. Managers need to give AI agents specific guidance on what success looks like.
Effective delegation includes:
Specific objectives
Instead of saying “help with customer service,” say “respond to customer inquiries about order status within 2 minutes and escalate complaints about damaged products to human agents.”
Defined boundaries
Clarify what the AI can do on its own, what needs approval, and what is off-limits.
Success criteria
Decide how to measure the AI’s performance. For example, response time, accuracy, or customer satisfaction scores.
Escalation protocols
Set rules for when the AI should ask for help or pass tasks to humans.
Building Trust Through Review
Managers must review AI work regularly. This review is not about micromanaging but ensuring the AI meets standards and improves over time. Feedback helps the AI system learn and adapt, just like coaching a team member.
Adapting Leadership Styles
Managing AI agents requires flexibility. Some tasks may need close oversight, while others allow more autonomy. Leaders must balance control with trust, adjusting based on the AI’s capabilities and the task’s complexity.
Practical Examples of AI as Coworkers
Consider a marketing team using an AI agent to draft email campaigns. Instead of telling the AI exactly what to write, the manager sets goals: target audience, tone, key messages, and deadlines. The AI drafts the emails, and the manager reviews and edits them before sending. This approach saves time and lets the manager focus on strategy.
In customer support, an AI agent might handle routine questions independently but escalate complex issues. The manager defines clear rules and monitors performance metrics like resolution time and customer feedback.
Preparing for the Future of Work
As AI becomes a coworker, organizations must rethink training and development. Managers need skills in AI communication, delegation, and performance review. Teams should learn to collaborate with AI agents effectively.
This shift also raises questions about ethics and responsibility. Managers must ensure AI decisions align with company values and legal standards. Transparency about AI roles and limits helps build trust within teams.
The rise of agentic AI changes management from controlling tools to leading partners. This change offers opportunities to improve productivity and innovation but requires new skills and mindsets.
Managers who embrace AI as a coworker will be better positioned to guide their teams through this transformation and unlock AI’s full potential.
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