AI Is Not the Strategy: How Leaders Build Moats, Moonshots, and Future Value

Is Artificial Intelligence a magic wand for business, or a tidal wave coming to disrupt your industry? According to Jorge Calvo, Deputy Dean of GLOBIS University and co-author of The AI-Driven Business: Leading, Competing and Thriving in the Age of Artificial Intelligence, and Ivan Bofarull, Chief Innovation Officer at ESADE and author of Moonshot Thinking, AI is neither of those things – but rather a tool for augmentation. 

According to Calvo and Bofarull, AI is best understood as a tool for augmentation, and the real question is where value moves when AI makes parts of work cheap and easy to copy.

In our recent Seminar AI-Driven Enterprises & Moonshot Strategy: How Leaders Compete in the Age of AI, the two speakers offered a practical roadmap for leaders who want to stay competitive. Their message was simple: surviving the AI era is not only about “doing digital transformation.” It requires a shift in how organizations learn, compete, and defend what makes them valuable.

Here are the key takeaways from their session on building an AI-driven, Moonshot-Ready business.

From “Return on Investment” to “Return on Learning”

Jorge opened with a common trap: companies adopt AI mainly to reduce cost and increase efficiency. That can help in the short term, but it often pushes companies into a “red ocean,” where competitors converge quickly because AI lowers barriers and makes capabilities easier to replicate.

To achieve a true competitive advantage, what Calvo calls a 650% performance increase, companies must shift their mindset: 

• The Old KPI: Return on Investment (ROI). 
• The New KPI: Return on Learning (ROL)

The goal is to create an “Augmented Company” where humans and machines learn together.

Key Takeaway: By combining proprietary data with a workforce skilled in AI, you create a “Talent Flywheel” that competitors cannot easily replicate.

Building an AI Moat (What Top AI Performers Do Differently)

Calvo emphasized that AI implementation happens in waves, moving from simple automation to Agentic AI (collaborative systems). However, technology alone isn’t enough. You need a “Moat”, a defensive barrier that protects your business. 

To build this moat, Calvo suggests a framework of five characteristics found in top-performing AI companies: 

• Mindset: an AI-first culture that expects change
• Experimentation: rapid trials, learning, and iteration
• Leadership: commitment, governance, and ethical decision-making
• Data: an end-to-end data supply chain and quality discipline
• Skills: an AI-literate workforce that can adapt as tools evolve

Key Takeaway: do not start with the model. Start with the business outcome, then build the data, skills, and operating system that make AI valuable in your context.

Understanding Disruption: The “Unbundling” Method

Bofarull addressed why disruption feels unpredictable. We often look at industries and jobs as a single “bundle,” but disruption happens at the task level. AI codifies certain tasks, commoditizes them, and forces organizations to rebundle around new sources of value.

A useful way to see this is to separate tasks into two categories:

• Codifiable Tasks: These behave like software (zeros and ones). They will be commoditized by AI in the short-to-medium term (e.g., “Transferring Knowledge” in a university).
• Non-codifiable tasks: tasks rooted in human judgment, trust, aspiration, emotion, and meaning. This is often where value migrates.

Key Takeaway: If parts of your business are being commoditized by AI, you must double down on the scarce, human elements that cannot be coded.

The Future Value Compass

How do you unlock new value when AI is doing the heavy lifting? Bofarull introduced the Future Value Compass, offering four distinct paths:

• Platformization: If a task is fully codifiable, turn it into a platform to achieve massive scale.
• Embedding AI: Integrate AI into your core business to create “superhuman” performance (e.g., an AI “learning co-pilot”).
• Moonshots: Design something that is not possible today. Ask “What if?” to force 10x thinking and focused innovation.
• Purpose (The North Star): Identify what will not change. Trust, human connection, and aspiration are durable assets. 

Key Takeaway: Use the compass to move away from knowledge transfer and toward trust—the newest scarcity in the AI age.

Protecting Your Advantage with Proprietary Context

Following their presentations, the speakers engaged in a dynamic Q&A session with the audience, where they addressed a critical concern: how do we maintain a competitive advantage?

They concluded that while the AI models themselves are becoming commodities (accounting for only 10% of value), while infrastructure and data quality account for 20%, the real differentiator is people and context (70%). If you rely solely on public tools like ChatGPT without your private business context, you have no advantage.

Key Takeaway: You must use proprietary data to fine-tune models; injecting your own business context is the only way to build a sustainable “Data Flywheel.“

Conclusion

The consensus from Calvo and Bofarull is clear: do not try to predict the future perfectly. Position yourself for it. Build learning velocity (Return on Learning), invest in an AI moat (data, skills, leadership), and use unbundling plus the Future Value Compass to see where value is migrating.

If you want a simple next step, try this:

• List three tasks in your business that are becoming codifiable this year
• Identify what customers trust you for that will not change
• Pick one Return on Learning metric to track this quarter (for example, learning cycle time, internal adoption, or upskilling progress)