Rethinking Leadership in the Age of AI Agents

“AI isn’t a tool. It’s a new kind of teammate.” — Candice Faktor, CEO, Disco | ASU+GSV 2025

If there was one unmistakable theme at this year’s ASU+GSV Summit, it was this: AI agents are not coming—they’re already here. And they’re reshaping how we work, learn, and lead.

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Candice Faktor, CEO of Disco (an AI-first learning platform), delivered one of the most compelling calls to action for leaders navigating this transformation. Her message was both practical and profound: AI isn’t just about automation—it’s about human flourishing, and the organizations that win will be the ones that reimagine their workforce, culture, and learning systems from the ground up.

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In this article, I unpack the most powerful ideas from Candice’s panel and what they mean for leaders tasked with building the next generation of high-impact teams.

1. AI Agents Are Not Tools—They Are Workforce Multipliers

For decades, digital tools have been designed to support humans. But with the rise of autonomous agents, we're now onboarding a new kind of “employee”—one that doesn't sleep, doesn't complain, and doesn’t think like us. Candice calls this a “hybrid workforce.” It’s no longer just human capital; it’s human-machine capital. Leaders must now ask:

• How do we manage agents?
  
• Where do we place trust?
  
• What does supervision look like?
  

The answer? Trial, experimentation, and radical openness. We don’t yet know what the best workflows look like with AI agents—and that’s okay. But we must start exploring now.

2. AI Fluency Is the New Literacy—and It Must Be Mandated

Referencing a bold internal memo from Shopify CEO Tobi Lütke, Candice highlighted a watershed moment: Shopify now includes AI fluency in employee performance reviews. It’s no longer a “nice-to-have”—it’s expected. AI fluency isn’t about prompt engineering alone. It’s about:

• Conversational design
• Prototyping with AI
• Embedding AI in decision loops
• Collaborating with non-human agents
  

It’s a mindset and a muscle. And it must be developed across every level of the organization, not just in innovation or engineering.

3. We Must Shift from Efficiency to Differentiated Value

Many companies adopt AI for one reason: cost-cutting. But that’s a short-sighted move. As Candice put it:

“Efficiency is table stakes. Differentiated value creation is the true opportunity.”

This means using AI not just to speed up what we already do—but to create new kinds of value. New business models. New customer experiences. New ways of upskilling people. The future belongs to companies that ask: How can AI amplify the parts of work that are most human—creativity, insight, empathy—not just replace the rote?

4. Cognitive Rot Is Real—And Leaders Must Design Against It

In a moment of candor, the panel’s moderator shared a concern:

“After outsourcing so much of my job to GPT, I felt my brain start to rot.”

It’s not just a joke. When humans offload too much of their cognitive effort to machines, we risk losing the very capabilities that differentiate us—judgment, abstraction, pattern recognition. Candice’s counterargument is powerful: AI, used correctly, can enhance learning—by making us better questioners, better thinkers, better experimenters. She describes this as a shift from “content consumption” to “conversational learning.” When learners use AI not just to get answers, but to ask better questions, they spark curiosity, insight, and growth.

5. Learning in the Age of AI Must Be Social, Iterative, and Conversational

Candice’s company, Disco, is built on a foundational belief: in a world of infinite content, conversation—not information—is the real catalyst for transformation.

AI learning isn’t just about efficiency. It’s about scale, personalization, and co-creation. Instead of one-size-fits-all training modules, the most forward-looking companies will:

• Empower employees to explore and question
  
• Build organizational knowledge through shared use cases
  
• Encourage rapid experimentation with AI across functions
  

In short: learning becomes a collective intelligence exercise.

6. Leading in This Moment Requires a Scientific Mindset

We are in the early innings of a paradigm shift. And as Candice reminds us, no one has all the answers.

The leadership mindset required today looks less like strategic planning and more like scientific inquiry:

• Experiment boldly
  
• Observe results
  
• Reflect deeply
  
• Adapt continuously
  

Leaders who embrace this model will build cultures that are resilient, curious, and future-ready.

7. This Is a Shared Responsibility—and a Moral One

Perhaps the most powerful moment in the discussion came near the end. When asked whose job it is to ensure that AI doesn’t erode the meaning of work, Candice responded:

“It’s a shared responsibility—across government, companies, teams, and individuals. This is a human responsibility.”

We are not just designing tools. We are shaping the conditions under which future generations will live, work, and grow. AI is dualistic—it can elevate or erode, empower or oppress. It’s up to us to decide which.

Final Thought: Don’t Just Adopt AI—Reimagine What Work Can Be

As leaders, our job is not to simply implement AI into our workflows. It’s to rethink the very purpose of work in an AI-augmented world. The organizations that thrive won’t be the ones that automate the fastest. They’ll be the ones that:

• Empower humans to do more meaningful work
• Build a culture of continuous learning and experimentation
• Lead with curiosity, courage, and conscience

The question is not “How fast can we implement AI?"

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It’s “What kind of future are we building with it?”