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Generative AI has progressed past prompt-based assistance to become interconnected systems capable of autonomous task execution.

Agentic AI systems now offer the ability to work within set boundaries and can carry out multi-step tasks that used to require ongoing manager supervision. Instead of answering one-off questions, AI agents can monitor information and present recommendations tied to business priorities.

When used carefully, these tools can expand a leader’s ability to process information and act with clarity.

Why It Matters: With nearly 40% of organizations experimenting with AI, according to a recent McKinsey survey, leaders face a potential shift in workflow. Agentic AI promises efficiency and improvements to how executives allocate resources for high-stakes decisions. Used thoughtfully, these systems could significantly elevate productivity and clarity despite requiring new forms of oversight and governance.

Continuous Intelligence Gathering and Contextual Evaluation: AI agents can track news, competitor announcements, regulatory updates, and industry developments on a regular basis. The system summarizes what has changed and explains why it may matter to the company instead of sending a general list of articles. For example, if a competitor releases a new product or changes pricing, the agent can compare those moves with internal plans and highlight possible implications.

Review of Executive Time Allocation: Reviewing past communications and transcripts can allow an AI agent to analyze where leadership attention is actually spent. By categorizing discussions with stated priorities, the system can then compare activity with goals or performance targets. If senior leaders say growth is the top focus, but most meetings center on operational issues, the gap becomes visible. From there, the system can suggest adjustments to delegation or scheduling. Over time, this helps align daily activity with company goals.

Stakeholder Modeling and Meeting Preparation: AI agents can review past conversations and feedback to build working profiles of board members or other stakeholders. Before a major presentation, a leader can ask the system to identify likely questions or concerns based on prior interactions. The agent can suggest supporting data and clarify areas that may raise concerns, making preparation more thorough.

Defined Workflow with Ongoing Learning: Effective use of agentic AI begins with clear instructions. Once leaders have defined goals, approved data sources, and success criteria, the agent completes assigned tasks using connected research tools and internal systems. Using new information as it becomes available, the agent is able to update findings. However, human oversight remains necessary before external communication or policy decisions. These regular check-ins ensure the system remains accurate and aligned with company standards.

Governance and Capability Development: Many organizations begin with repetitive tasks such as reporting or compliance checks that provide lower risk environments for testing reliability. Team-based training encourages employees to share use cases and learn from one another as clear policies define which data can be accessed and how outputs should be validated. For executives, support shows institutional commitment and encourages disciplined experimentation, while ongoing evaluation of emerging tools ensures that system design keeps pace with advances in model capability.

Go Deeper -> “What Leadership Looks Like in an Agentic AI World” – Harvard Business Review

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