Understand The Difference Between Explicit and Tacit Knowledge. AI Will Automate Both

AI Automates Knowledge Work in Areas Previously Thought Impossible

Many AI experts predict that AI WILL BE BETTER THAN HUMANS AT 95% of cognitive tasks in companies within two years. WE AGREE. (FYI, I didn’t 9 months ago.)

This has profound implications for sustained competitive advantage, company valuation, and the redesign of managerial, governance, compensation, and work processes. The mix of human and AI agents will absolutely transform cognitive work at companies.

Knowledge work performed today by, say, 5,000 employees will be handled by 4,000 employees and 10,000 AI agents within several years. This hypothetical company will need fewer humans in customer support, marketing, finance, legal, compliance, sales, engineering, and IT. In other words, AI massively increases human employee productivity.

For companies facing GenAI competitive disruption pressure, human employment will decline, AI employment will increase, and IT spending as a percentage of revenue will rise as more capital shifts toward labor displacement.

Critical to this digital transformation is understanding the different types of knowledge. Much of what defines your sustained competitive advantage rests with tacit knowledge.

Three Types of Knowledge: Explicit, Tacit, and Emergent Knowledge

Three types of knowledge exist. For the last 30 years, most IT systems automated explicit knowledge. Executives need to understand how AI can automate processes involving tacit and emergent knowledge.

1. Explicit Knowledge: Information that can be easily documented, shared, and applied, such as manuals, databases, or formalized processes.

2. Tacit Knowledge: Deeply ingrained, intuitive knowledge typically gained through experience. It includes insights, techniques, and processes that are not easily codified or transferred.

3. Emergent Knowledge: A new concept arising from the AI era, representing insights and patterns that emerge when vast amounts of data are processed by AI systems, particularly Large Language Models (LLMs).

How LLMs Interact with Different Types of Knowledge

LLMs have a unique ability to process and transform knowledge:

• They can sift through vast amounts of explicit knowledge, finding patterns and relationships that might be invisible to human observers.

• They can capture aspects of tacit knowledge embedded in text, images, and sounds, making previously expert-only insights more widely accessible. They can also do this at a scale and cost point that was unimaginable just 18 months ago (e.g., summarizing every public appearance and written article by every Moderna research scientist and executive regarding their approach to drug development).

• Through this process, LLMs generate emergent knowledge—new insights and capabilities that arise from the combination and analysis of diverse data sources.

Case Study: Healthcare Diagnostics

Consider a health insurance firm implementing an AI system to process doctor-patient conversations. This system could:

• Capture explicit knowledge (patient data) and tacit knowledge (the doctor's diagnostic reasoning)

• Generate emergent knowledge in the form of new diagnostic patterns or efficiency improvements

Conclusion

In this amazing Age of AI, understanding the different types of knowledge and how AI can automate heretofore unimaginable business processes is critical for executives. We are entering the age of AI machines being smarter than humans, which enables automation of vast amounts of knowledge work at companies traditionally performed by humans.

For many industries, winners in this revolution will be the firms that start adapting now. There is no fast catch-up.

“Hope is not a strategy.” - some attribute to this quote to Vince Lombardi

Onward,

Paul

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