HIGH POTENTIAL (for what?!)

Dispatches from the Wild #2

Dispatches from the Wild #2

Quick reminder: while the current Refractions series explores broader/future themes, Dispatches from the Wild offers a field perspective drawn from my recent client work, conversations with smart people (like you), and research at the intersection of humans, the workplace, and AI.

This essay blends observation, theory, and early evidence to spark new thinking about how we identify and develop leadership potential in an age of intelligent systems. The purpose of this piece is not necessarily to replace legacy hipo prediction routines, but rather to broaden the frame—to see potential as a dynamic pattern of contribution and adaptability in context rather than a fixed/linear trait.

Many of the practices described here are still being tested across organizations and labs. New predictive attributes and measurement methods are also emerging, combining human insight with intelligent systems to show relevant skills and qualities that show up in context and in flow.

Happy reading!

-Cameron

© 2025 Cameron Hedrick

https://www.linkedin.com/in/cameronhedrick1/ www.yieldxyz.com

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Snapshot

Short take: historical notions of “high potential” are aging out. In many companies, the same prediction methods have gone unchanged for years, turning a once-helpful process into one that often misses what matters. The cracks were already visible, but the rise of intelligent systems has widened them.

This piece explores which human capacities are emerging as the strongest signals of adaptability and long-term value as AI reshapes work. While applied technical fluency absolutely matters, readiness is increasingly defined by distinctly human qualities such as curiosity, empathy, contextual judgment, and the ability to learn and reorient in real time. It argues that potential is best understood through contextual mastery —the capacity to learn, adapt, and create value as conditions shift. Mastery itself is becoming more visible and measurable through live work and evolving AI capabilities, enriching human evaluation.

If you’d like the extended version, read on…

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History

Across many organizations, the term ‘high potential’ is generically defined as the ability to succeed in the next, more complex role(s) within a set time frame—an attempt to forecast future success from current signals. Way back when, pedigree, tenure, IQ, relevant technical acumen, and current performance were the dominant indicators of future potential. Elliott Jaques later tied potential to cognitive capacity for managing greater complexity over longer time spans. His stratified systems theory remains influential, albeit controversial, and is often criticized for rigidity and limited cultural relevance. (Still, it’s worth a read for thoughtful leaders.)

Years later, the Corporate Executive Board reframed potential as a mix of ability, aspiration, and engagement, a model still echoing in talent reviews today. Large consultancies routinely publish potential prediction definitions centered on steady performance, learning agility, and motivation for broader roles. These frameworks are essential and helped cement the idea of ‘potential’ in succession systems. Still, in doing so, something vital was lost: it became a label to manage rather than a capacity to cultivate, often anchored to current performance with little regard for future context.

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Now

Uneven Returns.

I’ve spent much of my career building and running these systems. They are beneficial; however, the weight of the process often overshadows the value. At their best, they slow leaders long enough to debate what “potential” really means, a small but meaningful act in organizations perpetually racing the clock. Still, those reflections are brief, their effects uneven, the follow-up uneven, and the return rarely worth the heavy lift.

Division. Complacency. Cynicism.

The purpose of identifying potential is meant to be both developmental and risk mitigating, designed to build a durable leadership bench. Right intent, but the practice often divides. Those who make ‘the list’ may grow entitled, while those who do not may quietly disengage. Some organizations now withhold communicating designations altogether, wary of politics and weakening morale. As a result, what began as a system for growth has, in many cases, become a source of quiet cynicism.

Devolving Signal-to-Noise Ratios.

Legacy indicators of potential, such as tenure, pedigree, IQ, and formal education, lost their sacred predictive status long ago. And now, as AI absorbs even more cognitive and procedural work, the signals that once marked readiness now point mostly backward. Current performance still matters; however, its predictive power is waning in an increasingly unpredictable world.

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Next: Attributes/Skills & Calibration

Prediction advantage is shifting toward distinctly human capabilities—e.g., empathy, presence, humility, self-awareness, ethical judgment, curiosity, collaboration, and resilience. These traits reveal how people learn, adapt, and create value in complex, AI-shaped environments, showing up in how they build trust, navigate conflict, and design systems where people and technology strengthen one another. The next era demands a contextual view of performance, where mastery is measured by how effectively individuals thrive in consequential settings. Potential is only as powerful as its context, so ask the essential question: high potential for what?

Grounding

Calibrations should begin with a shared understanding of the relevance of current performance. Try the following questions to ground the discussion:

Role-based:

• Is the current role stretching their capability, or has it become routine?

• How closely does today’s role/scope reflect the demands of likely future roles?

• Where are the gaps in the necessary leadership and technical proficiency in the incumbent’s current role?

• Where are the gaps in the leadership and technical proficiency needed for the target future role? (and how much of that technical proficiency can/should/will be outsourced to a machine?)

Incumbent-based:

• How do they handle ambiguity, feedback, and shifting conditions?

• How effectively do they use AI to extend insight without outsourcing judgment?

• How do they read context, navigate tension, and build trust?

• What learning and decision-making patterns suggest readiness for greater scope?

Next, consider the following attributes and skills, framed within the Yield XYZ philosophy, that may indicate readiness for more complex roles in an AI-shaped world. Keep the conversation rooted in the future context—what is changing in the broader environment and in the specific roles being evaluated for succession.

Self-Knowing (X Dimension)

Capabilities that govern inner adaptability, self-regulation, and meta-learning.

• Learning Agility: The ability to proactively learn, unlearn, and relearn rapidly; to translate feedback and failure into improved judgment—including learning through interaction with intelligent systems.
  

• Cognitive and Emotional Flexibility: Comfort with ambiguity, paradox, and the blending of human and machine perspectives; the capacity to shift mental and emotional frames without losing coherence.
  

• Humility and Self-Awareness: Grounded confidence that pairs conviction with openness. Capacity to recognize limits, stay curious, and adapt through experience. (Often cultivated through mindful awareness, this requires internal ‘space’ to witness thought and emotion without being consumed by them.)
  

• Resilient Presence: Steadiness under pressure; the ability to manage attention and energy amid complexity, noise, ambiguity, and conflict.

Team Navigation (Y Dimension)

Capabilities that enable collaboration, trust, and higher yield on collective intelligence.

• Relational Maturity: The discipline to build trust, navigate conflict, and surface insight through others (not merely with them).
  

• Contextual and Emotional Intelligence: The instinct to read interpersonal dynamics, constraints, and cultural nuance before acting; judgment tuned to both people and situation.
  

• Ethical Judgment: The capacity to align power and consequence; to make principled decisions when data are silent or conflicting, and to model and teach this to your team.

• Human–Machine Teaming: The ability to reconceptualize the team to include both humans and intelligent systems, integrating all inputs harmoniously, extending capability where needed, and engaging technology as a partner rather than a threat.

Organizational and System Optimization (Z Dimension)

Capabilities that enable synthesis, foresight, and thoughtful shaping of complex systems.

• Adaptive Curiosity: Continuous exploration balanced by discernment as to where to invest attention and resources.
  

• Creative and Systems Thinking: The ability to connect disparate ideas, technologies, AI proxies/agents, and perspectives to generate value and design flow across systems and across time.
  

• Design Intelligence: Understanding how structures, incentives, policies, and technologies interact to create conditions for certain behaviors; intentionally harmonizing them so human and machine capabilities reinforce one another.

EPOCH: Human + Machine Complementarities

The EPOCH capacities—empathy, presence, opinion and judgment, creativity, and hope—serve as a unifying element across these dimensions. Based on MIT Sloan’s research on human–AI complementarities, EPOCH was not designed as a predictive tool for leadership potential but as a perspective for understanding where human strengths are most vital as intelligent systems evolve.

Next: Development & Measurement

Modern development demands real-time observation and smarter measurement. The most meaningful insights come from how people work, not just what they produce. Emerging AI tools are extending our ability to passively detect behavioral patterns in real-time—in context, in flow. The ideas below show how to begin now and where the next wave is headed.

What We Can Do Today

• Use real work as the lab: Utilize live projects as proving grounds for mastery. Observe how individuals navigate ambiguity, feedback, and course correction.

• Create peer observation circles: Invite colleagues to name where they see mastery in others and calibrate their approaches accordingly. This builds shared language and normalizes developmental feedback. Additionally, share learning techniques: “How/where did you learn that?”

• Review decisions, not just results: During project debriefs, 1:1s, or performance discussions, focus on the quality of decisions—how tradeoffs were weighed, information gathered, and alternatives considered. This shifts attention from outcomes to the thinking behind them.

• Narrate your learning in motion: Encourage leaders and teams to verbalize what they’re noticing and learning while doing the work. A short “here’s what I’m learning” exchange can turn ordinary moments into shared reflection and build a culture that values continuous learning over post-mortems.

• Observe human and machine integration: Pay attention to how individuals employ AI tools and systems in their workflow. For example, when they delegate, when they collaborate, and how they use technology to extend rather than replace human judgment.

Evolving Techniques

• Persona-based modeling: AI can generate composite future personas or archetypes—collections of skills and attributes that represent success across broader, more complex role categories. This helps organizations develop talent toward a range of future possibilities instead of focusing on a single role. It guides individuals to understand what’s needed and helps HR design development programs aligned with those archetypes.

• AI-augmented 360s: Continuous feedback models that integrate peer, machine, and environmental inputs into comprehensive behavior and readiness profiles.

• Learning velocity tracking: AI can detect how quickly people acquire and apply new knowledge by analyzing iteration cycles, timelines, and feedback loops, revealing mastery as a rate of adaptive learning rather than a static outcome.

• Decision-pattern inference: Natural language models can reveal how people think under uncertainty by analyzing reasoning patterns in reflections, communications, and meeting summaries.

• Relational flow analytics (ONA): Network-aware systems can now map how trust, creativity, and influence move through teams, demonstrating mastery not only in performance but also in how someone strengthens the ecosystem around them.

• Future-context modeling: AI will be able to simulate upcoming roles or market conditions, testing whether current patterns translate effectively to new contexts. This dynamic lens helps leaders gauge readiness and guide targeted development.

• Cognitive twin modeling: AI can create evolving digital profiles—a digital you—that simulate how individuals might respond in future scenarios.

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Outro

All of this calls for a shift from identifying static cohorts of “high potentials” to cultivating relevant contextual mastery. Everyone holds potential for more, but that potential depends on the interplay between innate qualities, developed skills, and the environment in which they are expressed. This has always been true, but the contours are changing as we become increasingly augmented by new, distributed, and improving cognitive tools.

The challenge, of course, is that technical skills are far easier to assess than human behaviors and capacities, which are more opaque. Intelligent systems will increasingly help us detect signals of these capacities passively, in context and in flow, but their cultivation will remain an intrinsically human endeavor.

Potential reveals itself in how we meet the moment, how we listen, adapt, and begin again. It lives in the space between knowledge and curiosity, certainty and doubt, comfort and change. Machines may calculate more quickly, but they do not search for meaning or rise stronger from failure. That act of renewal is what makes us human, and what ultimately shapes the best of what humans and machines can achieve together.

© 2025 Cameron Hedrick

https://www.linkedin.com/in/cameronhedrick1/ www.yieldxyz.com

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Good Reads

• Jaques, E. (1996). Requisite Organization: A Total System for Effective Managerial Organization and Managerial Leadership for the 21st Century (2nd ed.). Arlington, VA: Cason Hall & Co. Publishers.
  

• Wagner, T., & Christensen, U. J. (2025). Mastery: Why Deeper Learning Is Essential in an Age of Distraction. New York, NY: Basic Books.
  

• Oakes, K. (2021). Culture Renovation: 18 Leadership Actions to Build an Unshakeable Company. New York, NY: McGraw-Hill Education.
  

• Hougaard, R., & Carter, J. (2024). More Human: How Great Leaders Use Connection to Power Their Organizations and Lift People Higher. Boston, MA: Harvard Business Review Press.
  

• Drucker, P. F. (1982). On the Profession of Management. Boston, MA: Harvard Business Review Press.
  

• Sun Tzu. (2005). The Art of War (L. Giles, Trans.). New York, NY: Barnes & Noble Classics. (Original work published ca. 5th century BCE).
  

• Schmidt, F. L., & Oh, I-S. (2016). The validity and utility of selection methods in personnel: 100 years of research. University of Iowa. https://home.ubalt.edu/tmitch/645/session%204/Schmidt%20%26%20Oh%20validity%20and%20util%20100%20yrs%20of%20research%20Wk%20PPR%202016.pdf
  

• Cross, R. (n.d.). What is Organizational Network Analysis? Retrieved October 18, 2025, from https://www.robcross.org/what-is-organizational-network-analysis/
  

• Coleman, R., et al. (2018). Improving Company Performance with Organizational Network Analysis (ONA). Harvard People Analytics White Paper. https://scholar.harvard.edu/files/people_analytics/files/improving-company-performance-with-organizational-network-analysis-ona-whitepaper-2018-2.pdf
  

• Visible Network Labs. (2020). What is Organizational Network Analysis and Why is it Useful? https://visiblenetworklabs.com/2020/11/05/what-is-organizational-network-analysis/
  

• Iliescu, D. (2023). The incremental validity of personality over time in occupational settings. Personality and Individual Differences. https://www.sciencedirect.com/science/article/abs/pii/S0191886923002118
  

• Berry, C. M., et al. (2024). Insights from an updated personnel selection meta-analytic review: Excluding GMA tests and impact on validity. SMU LKCSB Research Paper. https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=8567&context=lkcsb_research
  

• MIT Sloan School of Management (2025, October 16). New MIT Sloan research suggests AI is more likely to complement, not replace, human workers.
  

• Patel, H. (2020). Organizational Network Analysis: Overcoming Common Pitfalls. Harvard People Analytics Blog. https://d3.harvard.edu/platform-peopleanalytics/submission/organizational-network-analysis-overcoming-common-pitfalls/
  

• McKinsey & Company. (2025). The State of AI: How Organizations Are Rewiring to Capture Value. QuantumBlack, McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
  

• MIT Sloan School of Management. (2025). Workforce Development in the Age of AI. MIT Sloan Ideas Made to Matter. https://mitsloan.mit.edu/ideas-made-to-matter/download-workforce-development-age-ai
  

• PwC. (2025). AI Business Predictions 2025: Competing in the Age of AI. PwC US. https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.htm