KM for AI Readiness Assessment

Knowledge Assessment for LLM Based Systems

Organizations are trying to factor in how ChatGPT, LLMs, Generative and other AI will advance knowledge management and improve both employee and customer experience. Retrieval Augmented Generation (“RAG”) is a differentiator and prioritizes internal knowledge. RAG ensures:

 +  Protection of Intellectual Property

 +  Elimination of hallucinations

 +  Compliance with brand guidelines

 +  Audit trails for answers

KM for AI READINESS ASSESSMENT

1. Educate executives and stakeholders about the key benefits (and limitations) of ChatGPT types of applications
2. Outline critical success factors for achieving business value from LLM based technologies through information architecture 
3. Examine four critical areas of KM for AI readiness
   
   •  Effective selection of scope and use cases
   
   •  Evaluation of knowledge quality including structure, fitness to purpose and meta data enrichment
   
   •  Establishment of baseline metrics to demonstrate measurable success
   
   •  Ongoing governance and decision making
   
   •  Summarize the current state in an executive working sessions designed to identify gaps, set realistic goals, and prioritize actions
   
   

Why Is This Important?

 •  Organizations compete on their knowledge

 •. Generative AI performs most effectively on componentized knowledge

 •. Semantic search is powered by a knowledge architecture

 •. Reduced customer support costs

 •. Improved knowledge access

 •. Increased operational efficiencies

Deliverables

 •. Business value statement

 •. Prioritized use cases

 •. Vision development working sessions

 •. Technology and organizational readiness scorecard

 •. AI Proof of Value (PoV) plan