Business
Challenge:

The client, a global technology leader renowned for its software, services, and solutions, had a vast partner ecosystem driving business expansion and customer retention worldwide. 

As part of its Skills Initiative Program, the client faced significant operational challenges in partner support: 

  • The support team received a high volume of daily requests from partners related to access, technical, and payment issues.
  • Managing these requests required considerable manual effort and time every day.
  • The absence of a standardized system for handling requests led to inconsistent guidance and slower response times.

The client needed to reduce human effort, accelerate responses, and maintain compliance, security, and partner satisfaction simultaneously. 

The objective was to create a scalable, AI-driven support solution capable of handling routine requests autonomously while keeping complex queries under human supervision. 

TurnB Approach


Analyzing Support Requests 

  • Consolidated six months of historical partner support data to understand patterns and workload distribution.
  • Conducted intent analysis to group requests into well-defined categories for automated handling.
  • Developed a governed knowledge base containing step-by-step playbooks and user-friendly response templates for all identified categories.

Building an AI Agent 

TurnB developed a Large Language Model (LLM)-powered AI agent using Copilot Studio that: 

  • Understood incoming partner requests in natural language.
  • Accessed the knowledge base and executed necessary actions autonomously.

The agent performed key tasks such as: 

  • Extracting critical information from each request.
  • Classifying requests into categories using confidence scores to determine if they could be handled automatically.
  • Retrieving relevant playbooks via Retrieval-Augmented Generation (RAG) and executing steps through connected systems.
  • Drafting clear, contextual replies within the same communication thread.

Human Oversight and Continuous Learning 

  • Requests falling below the confidence threshold were routed to human agents, along with AI-generated draft replies and recommended next actions.
  • Weekly reviews were conducted to update playbooks and training examples, allowing the system to learn continuously and improve over time.
Approaches background
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Implications

The AI-driven partner support solution delivered immediate and measurable impact: 

  • 90% of partner requests were auto-classified and responded to within the first three months of deployment.
  • Human agents focused only on complex or novel scenarios, significantly reducing manual workload.
  • Saved approximately 1.1 hours per day, amounting to around 286 hours annually.
  • First response times improved dramatically—from hours to minutes—enabling 24/7 global coverage.
  • Ensured consistent guidance across regions and partner types, enhancing partner experience and satisfaction.

 

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