The Infrastructure of Agency: Why Learning is the Only Durable Skill

In an era of rapid technological change, your most valuable asset is not a specific technical skill, but how fast you can learn. Ginny Cartwright Ziegler, CMO of Pearson, points out that as AI handles routine tasks, the human competitive advantage becomes agency: the ability to think critically, learn constantly, and work alongside intelligent systems. Many organizations use AI as a shortcut to get answers, but this creates a learning deficit by skipping the mental effort needed to actually retain information. Leaders and educators who prioritize AI readiness over mere AI literacy will gain a lasting advantage. By focusing on human agency rather than tool proficiency, they build resilience for a future where today's job descriptions will eventually disappear.

The Hidden Cost of Teleporting to the Answer

Conventional wisdom says the best learning tools are the ones that provide information the fastest. However, Ziegler identifies a major flaw in this approach: when you use AI to teleport a learner straight to the answer, you remove the mental friction required for true mastery.

> We can not just teleport them straight to the answer. There is a gap between not knowing something and knowing something, and that gap in between is a struggle and it is called learning.
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> -- Ginny Cartwright Ziegler

This struggle is a feature, not a bug. Systems that value speed over engagement fail to build the neuroplasticity needed for long-term memory. Pearson data shows a 25 percent increase in engagement among students using AI tools with pedagogical guardrails, which force the student to work through the logic instead of just consuming the output. The takeaway is clear: if your training or educational systems prioritize speed of delivery, you are likely wearing down the cognitive muscle your team needs to solve complex problems later.

The Shift from Credentials to Verified Agency

Because AI can now generate professional-grade content, the traditional resume is becoming a less reliable indicator of capability. Ziegler notes a structural shift in the labor market: employers are moving away from static education markers and toward a demand for verified human skills.

The market is responding by moving toward micro-credentials, which provide granular, verifiable proof of what a person can actually do. This creates a feedback loop: as the demand for verified skills grows, the institution providing the verification becomes a critical trust broker. For organizations, this means competitive advantage no longer comes from hiring based on pedigree, but from finding candidates with learning velocity, or the ability to apply new knowledge immediately in the flow of work.

Why AI Readiness Outperforms AI Literacy

Most organizations focus on AI literacy, which usually means learning how to use specific software. Ziegler argues this is a tactical error. The more durable strategy is AI readiness, defined by the AIL framework: Agency, Intelligence (learning velocity), and emotional quotient.

> The goal, it is not about AI literacy. I think what gives you agency is this idea of AI readiness and really being able to work effectively alongside intelligent systems in the flow of your work.
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> -- Ginny Cartwright Ziegler

The non-obvious consequence is that while technical tools change, the ability to collaborate with them is constant. When teams focus on the mechanics of a specific tool, they are forced into constant retraining as technology evolves. When they focus on AIL capabilities, they build a system that works regardless of the specific AI model. This creates a lasting moat: while competitors are busy updating their software training, your team is already iterating on how to solve new problems with whatever tools are available.

Key Action Items

• Shift from fast to sticky learning: Evaluate your current training protocols. If they prioritize speed of completion, add friction points that require learners to apply knowledge immediately. (Immediate implementation)
• Audit for agency, not just skill: When hiring or promoting, weigh learning velocity higher than specific current technical knowledge. Look for evidence of a candidate’s ability to unlearn and adapt to new systems. (Ongoing)
• Implement micro-credentialing: If you are a leader, stop relying solely on broad certifications. Create internal micro-badges for specific, high-value skills that require verification of application, not just theory. (Next 3-6 months)
• Focus on the flow of work: Move away from batch-based training. Integrate learning opportunities into the daily workflow where the knowledge is immediately applicable, ensuring the 70 percent forgetting rate mentioned by Ziegler is mitigated by practice. (Next 6-12 months)
• Prioritize human-in-the-loop design: When adopting AI tools, ensure they are configured to augment human judgment rather than replace it. Use AI to handle the data, but mandate human intervention for the final decision-making process to maintain team agency. (12-18 months)