Leadership has always been a distinctly human capability, rooted in emotional intelligence, communication, and the ability to inspire others toward a common goal. It’s something enterprises have spent decades nurturing through mentorship and growth pathways. These approaches help develop potential leaders, but organizations still struggle when it comes to identifying prospects. Why? Because they’ve been relying on outdated signals in an increasingly dynamic workplace.
Now, the rise of agentic AI is changing the way organizations recognize talent, surpassing the limits of previous artificial intelligence iterations. It’s not just reactive, like chatbots or generative models. It’s proactive—autonomous, goal-oriented, and designed to take action with little to no human oversight. Gartner predicts that by 2028, agentic AI will be embedded in over a third of enterprise software, autonomously making at least 15% of everyday business decisions. According to KPMG, 51% of companies are already exploring agentic AI use cases.
If the promises of agentic AI sound like a dream, here is the return to reality: AI agents can only be as effective as the data they’re fed. When the goal is to identify leadership potential, traditional data sources fall short—and the solution lies in harnessing organizational memory through unstructured data.
The Problem with Legacy Signals
Traditional methods of spotting “high potential” leaders—résumés, performance reviews, and succession plans—were built for a workplace where success follows a straight line and leadership is tied to position rather than influence. They reward hierarchy, tenure, output, and visibility. As a result, the most influential contributors often go unnoticed.
Stretch assignments and mentoring can help develop leadership, but enterprises encounter difficulties when it comes to spotting the next generation of natural leaders. That’s because leadership today appears in informal networks. It shows up in acts of collaboration, creativity, and problem-solving, often in places overlooked by formal systems.
The specialist everyone turns to in a crisis and the collaborator holding teams together often wield more real-world influence than the manager two levels up. These are the people who rarely show up on succession plans, but their impact is undeniable. Agentic AI is uniquely suited to surface these hidden patterns and recommend actions, but unlocking these benefits depends entirely on the data ecosystem surrounding the model.
Agentic AI Needs Human-Centered Data
The power of agentic AI lies in its capacity for autonomous decision-making, which depends on experiential information known as reinforcement learning. This model of AI refines itself through real-time interactions with its environment. If the data environment is fragmented, siloed, or filled with ROT data (Redundant, Obsolete, Trivial), the AI’s decisions will mirror those flaws.
Most AI systems today are trained on static and biased data. Self-assessments, old performance reviews, and even AI-generated data are insufficient to capture the dynamic, human reality of a workforce. Feeding agentic AI with these flawed inputs is like aiming a high-powered telescope at a fogged-up window. The tech is powerful, but the view is limited.
Unstructured data such as files and employee communications contain the content, intent, and dynamics of the workforce. Over 80% of enterprise data today exists in unstructured formats, making upstream data management imperative when it comes to curating agentic AI data feeds. When powered by insights surfaced from unstructured, human data—such as peer-to-peer recognition, collaboration networks, and influence maps—agentic AI becomes a Strategic Talent Advisor.
From Insight to Action: AI as a Strategic Talent Advisor
As a strategic partner to HR and decision makers, agentic AI can:
- Surface emerging leaders based on behavioral patterns.
- Recommend high-potential candidates for critical projects.
- Flag early signs of attrition or disengagement.
- Orchestrate coaching programs and mentorship opportunities.
- Dynamically map skills and match talent to opportunity.
Imagine a system that doesn’t just track performance but understands how people influence and inspire. Agentic AI doesn’t replace human judgment, it enhances it.
The Four Pillars of Data Strategy for Agentic AI
To unlock this potential, organizations must get serious about data readiness. Here’s how:
- Align AI With Business Outcomes
Connect agentic AI initiatives to business value—like improving team collaboration, automating workflows, or predicting and curbing attrition. - Strengthen Data Infrastructure
Break down silos and ensure data is accessible, relevant, and up to date. Agentic AI needs real-time, unified information to make proactive, reliable decisions. - Maintain Data Hygiene and Reduce ROT
Use AI tools to detect anomalies, clean duplicates, and standardize formats. Regular curation prevents outdated or misleading signals from steering decisions. - Implement Governance and Ethics
Protect sensitive data with access controls, ensure compliance with regulations like GDPR and HIPAA, and design AI systems that reflect the organization’s values.
The Human Side of AI Leadership
Ultimately, technology itself won’t be the differentiator. The real advantage will come from how organizations use agentic AI: the quality of their data, the intent behind their strategies, and the ability to align artificial intelligence with human potential.
Leadership will always require human judgment. AI can identify patterns, surface potential, and even recommend action, but it can’t define purpose. That remains uniquely human.
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