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In Part I of The Executive Read, we asked a foundational leadership question: What are you really dealing with?
Part II moves from reflection to application. Marie Potempski examines what the answers to that question can reveal about performance, capability, accountability, authority, role clarity, and organizational structure.
A visible performance problem does not automatically reveal its cause. A missed deadline does not prove a lack of discipline. A polished document does not prove comprehension. Strong executive judgment requires leaders to separate observable facts from assumptions, investigate the conditions surrounding the work, and identify the correct point of intervention.
The episode also examines how artificial intelligence is changing the performance picture. Research involving more than 5,000 customer-support agents found that generative AI increased average productivity by nearly 14 percent, with gains of approximately 35 percent among less experienced and lower-performing workers. A separate six-month experiment involving 7,137 knowledge workers across 66 organizations found that active AI users spent approximately two fewer hours per week on email.
These gains are significant—but AI-assisted output and demonstrated human capability are not the same.
As AI becomes embedded in organizational systems, leaders must learn to evaluate two forms of performance: what a person can accomplish with technological assistance and what understanding, attention, judgment, and responsibility the person still retains.
This episode offers an executive-level framework for discerning whether AI is removing unnecessary friction, strengthening human capability, concealing a lack of understanding, or encouraging passive dependence. It also challenges professionals who intend to excel to develop the qualities technology cannot assume for them: sustained attention, sound judgment, emotional steadiness, ethical responsibility, teachability, and disciplined independent thought.
AI can improve speed, access, and presentation. Leadership must still determine whether the final answer is accurate, responsible, and fit for purpose.
IN THIS EPISODE
- Moving from the diagnostic questions in Part I to application
- Separating observation from interpretation
- How role clarity, capability, and working conditions affect performance
- Distinguishing development needs from structural constraints
- Research findings on AI and workplace productivity
- Why polished output does not necessarily demonstrate capability
- Focus, cognitive offloading, and critical engagement
- Assessing AI-assisted performance and retained human capability
- Executive formation in an AI-supported workplace
- Returning to the executive read
EXECUTIVE REFLECTION
Before responding to a performance concern, complete these statements:
What I know as fact is:
What I am currently assuming is:
The level at which the problem appears to be occurring is:
The evidence supporting that conclusion is:
The evidence that could change my conclusion is:
The human capability that must remain visible is:
The appropriate leadership response is:
CLOSING PRINCIPLE
Do not evaluate performance only by the quality of the final output.
Examine what produced it, what the individual understands, what the system permits, what the technology contributed, and who remains capable of carrying responsibility when conditions change.
Stratwell Hub™ develops disciplined decision-makers who value clarity over noise.
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