Continuing on the last tutorial on why knowledge graphs are an essential block of a sustainable KM practice, this tutorial will focus on how to build a knowledge graph. I am using pre-sales KM practice to show how it works for learning purposes.

A knowledge graph is a powerful tool for pre-sales teams, enabling faster decision-making, better collaboration, and scalable knowledge transfer.

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The goal is to:

• Identify missing skills in the team.
• Recommend training programs.
• Keep the knowledge graph updated dynamically.

1. Define Key Entities & Relationships  

Entities:  

• Employees (Pre-sales engineers, SMEs, Solution Architects, Proposal Managers,  new hires)  
• Skills (AI/ML expertise, competitive analysis, proposal writing)  
• Documents (RFP templates, battle cards, demo scripts)  
• Customer Engagements (Past deals, use cases, objections handled)  

Relationships:  

• Employee A → Knows → AI Model Explainability  
• Document X available in central→ Used in → Deal Y  
• SME B → Mentors → New Hire C  

2. Capture Tacit Knowledge from Outgoing Employees  

• Exit Interviews → Knowledge Management Integration through KM powered exit-onboarding program:  
• Map their expertise (e.g., Senior Engineer → Key Contributor → Healthcare AI Proposals).  
• Link their insights to relevant deals, competitors, and internal best practices.  

3. Enable Direct SME Connections for Upskilling  

• AI-Powered Recommendations:  
• If a new hire struggles with AI pricing strategies, the KG suggests:
  •    Relevant SMEs (e.g., Connect with Priya, who closed 5 AI deals last quarter).  
  •    Training Resources (e.g., Watch Priya’s recorded demo on cost justification).  

4. Reduce Onboarding Time  

Automated Learning Paths:  

•  New hires query the KMS: Show me all docs/SMEs for FinTech AI pre-sales.  
• The KG surfaces:
  •   Top 3 Battle Cards for FinTech objections.  
  •    SME Contacts who specialize in FinTech.  
  •    Recorded Demos from past successful deals.  

5. Make Knowledge Reusable  

• Smart Search & Contextual Suggestions:  
• When working on a manufacturing AI proposal, the KG auto-suggests:
  •    Past winning proposals in manufacturing.  
  •    Competitor comparisons from similar deals.  
  •    SMEs who can review the proposal.  

Expected Outcomes  

• 30% faster onboarding (New hires access curated knowledge instantly).  
• 20% fewer repeat questions (SMEs spend less time on basic queries).  
• Preserved tribal knowledge (Even after employees leave).  
• AI-driven upskilling (Employees get personalized learning paths).  

By implementing a knowledge graph, AI pre-sales teams can transform scattered information into a dynamic, reusable asset—bridging skill gaps, accelerating onboarding, and preserving critical expertise. This structured approach not only empowers employees with AI-driven insights but also ensures that institutional knowledge grows smarter over time, driving faster deals and more competitive wins.

The future of knowledge management isn’t just about storing information—it’s about connecting the right people, skills, and insights at the right time. Start building your knowledge graph today, and turn organizational knowledge into your greatest strategic advantage.

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