Prepare Your Business Knowledge for the AI Era (with Cor Schutte)
Most businesses know documentation is crucial—but it's often the first thing to slip through the cracks. What many don't realize is that in the AI era, how you structure your documentation directly determines how effective your AI becomes.
Poor documentation structure = poor AI responses. Well-structured knowledge = dramatically better AI outputs that can actually run your processes.
Join us for an exclusive 90-minute workshop where PYV from 9x teams up with Cor Schutte, who runs a systems automation and process design agency, to demonstrate how restructuring your business knowledge transforms AI from a basic tool into a self-learning system. You'll see an experimental method of using GitHub as your central knowledge store and the incredible impact this can have on getting AI to co-pilot your business operations.
This workshop is perfect for business leaders who understand the value of documentation but want to see how small changes in structure can unlock exponential improvements in AI performance and business automation.
Access conditions: This event is completely free to attend! Simply register to secure your spot and receive the access link before the event. Space may be limited, so early registration is recommended.
Agenda:
Opening: Why most businesses struggle with AI knowledge integration
Live demonstration: Transforming business content into structured intelligence
Systematic approach to organizing your company knowledge for AI
How AI systems can learn and improve from your team's work patterns
Building safeguards to prevent AI from making dangerous business assumptions
Interactive Q&A
Outcomes - Learn how to:
Transform scattered business knowledge into AI-accessible intelligence systems
Implement safety frameworks that protect critical decisions while accelerating insights
Create learning systems that improve systematically rather than requiring constant maintenance
Build behavioral intelligence that adapts to your team's specific working patterns
Develop systematic AI integration that scales across multiple people and processes
Access starter materials and methodology frameworks for immediate experimentation