What I'd Learn Instead of Automation in 2026

Summary

In this video, the creator warns that learning automation in 2026 could be a poor career move due to rapid advancements in AI. While automation systems have been lucrative, technical skills are quickly becoming obsolete as AI evolves. The key to success in the future is understanding business requirements and knowing how to interface business with AI. Rather than focusing on mastering tools, the new high-leverage skill will be communicating business needs to AI models. The creator urges viewers to focus on the bigger picture: the shape of business, systems thinking, and how to position oneself in the AI-driven economy.

Takeaways

• 🤖 Automation skills, like mastering make.com or APIs, are becoming less valuable as AI can perform these tasks from business requirements.
• 📉 Technical execution skills at the margins are repeatedly devalued with each technological revolution, illustrated by the Sarah the Seamstress example.
• 💡 The new high-leverage skill is identifying business problems worth solving and understanding value creation.
• 📝 Effective communication with AI models is critical; knowing how to prompt AI is more valuable than technical tool mastery.
• 🔍 The CLEAR framework (Clarity, Logic, Examples, Adaptation, Results) is a structured method to prompt AI effectively.
• ⏳ AI will soon generate most workflows from natural language, making traditional automation skills increasingly obsolete.
• 📈 Systems thinking — understanding the 'shape of a business' (marketing → sales → onboarding → delivery → reactivation) — is essential for long-term success.
• 🎯 Focus on abstraction and overarching business patterns rather than specific tools or technical implementations.
• 💰 Mastering AI as a tool for business problem-solving can create significantly higher leverage and income potential.
• 🚀 Businesses and service models share structural similarities; understanding these allows you to adapt and scale across industries efficiently.
• ⚠️ Memorizing features or APIs is less valuable; the focus should be on problem identification, AI communication, and systems-level thinking.
• 📊 Validating outputs against business goals ensures AI-driven workflows provide measurable ROI and consistent results.

Q & A

Why is learning automation in 2026 considered one of the worst career moves?

Learning automation in 2026 is seen as a poor career move because AI is advancing rapidly, and technical skills involved in automation are becoming obsolete. In the future, AI systems will automate many of the tasks that automation professionals currently do, reducing the value of mastering automation tools.

How has AI changed the value of automation skills?

AI has made many technical skills in automation less valuable because tools are improving rapidly and can handle the technical aspects of automation. As a result, the focus is shifting from mastering automation tools to understanding business requirements and effectively interfacing with AI.

What example does the speaker use to explain the obsolescence of technical skills?

The speaker uses the example of Sarah the Seamstress. Over generations, Sarah's technical skills (such as handstitching) became obsolete as new technology emerged, from looms to CAD design and, eventually, AI-driven clothing design. This illustrates how technical skills are phased out as automation improves.

What skills are expected to be more valuable in 2026 and beyond?

In 2026, the most valuable skills will be understanding business problems, identifying valuable solutions, and knowing how to communicate those problems to AI systems. The ability to prompt AI effectively will be a high-level skill, as AI will take over many technical tasks in business automation.

What is the major shift in the role of automation professionals as AI advances?

The role of automation professionals is shifting from being tool experts to business problem solvers. In the future, the value will lie in understanding business requirements and knowing how to communicate them to AI systems, rather than mastering specific automation tools.

What is the 'Clear' framework, and why is it important?

The 'Clear' framework is a method for crafting high-quality prompts for AI. It includes: Clarity (precise problem definition), Logic (structured thinking), Examples (specific scenarios), Adaptation (iterative refinement), and Results (measurable success). This framework is critical for ensuring that AI-generated solutions meet business needs and are effective.

How will AI impact the way business workflows are created by 2026?

By 2026, AI will be capable of building complete business workflows directly from natural language prompts. This means that tasks like creating CRM systems, sales pipelines, and inventory management systems will be automated, reducing the need for manual intervention and technical expertise.

Why is it important to understand the 'shape' of a business?

Understanding the 'shape' of a business is important because it transcends specific technologies or tools. The core structure of any business involves lead generation, sales, onboarding, delivery, and reactivation. If you understand these universal business processes, you can apply them to any business model, making it adaptable to different industries.

What role will AI play in the future of business systems and workflows?

AI will play a central role in designing and executing business systems. By using natural language prompts, businesses will be able to create entire workflows, such as customer relationship management systems, with AI. This reduces the need for deep technical knowledge and allows business leaders to focus on high-level strategy.

How can businesses and individuals prepare for the AI-driven future described in the video?

Businesses and individuals can prepare by focusing on higher-level business skills, such as problem identification, system thinking, and communication with AI models. Instead of focusing on mastering specific tools, people should learn how to leverage AI to solve business problems and scale operations efficiently.