Explainable AI

The Explainable AI provides clear reasoning behind every recommended action. Instead of simply suggesting what to do next, the system also explains why the recommendation was generated, what data influenced it, and how confident the model is in its suggestion.

This ensures users understand the logic behind each Next Best Action, enabling them to validate recommendations, act with confidence, and maintain transparency in decision-making.

It answers three key questions, helping users understand the importance of each action:

1. What you should do?

   • Specifies the exact next action the user should take, such as sending an email, making a call, or scheduling a meeting.

   • Significance: Provides clear guidance, removing ambiguity and helping users take immediate action. It ensures users know the exact step to move opportunities forward.

   • Example: “Send an email asking the customer for any additional information or clarifications needed to proceed effectively.”

2. Why you should do it?

   • Explains the rationale behind the recommendation, highlighting which signals, events, or patterns triggered it.

   • Significance: Builds trust in the AI by showing reasoning. Users understand the context and can prioritize actions based on urgency, potential impact, and historical success patterns.

   • Example: “The customer has recently engaged by sending documents and scheduling an appointment, showing high interest and a need for timely follow-up.”

3. Goal

   • States the intended outcome or business objective that the recommended action aims to achieve.

   • Significance: Aligns user actions with business objectives. It ensures that users are not just acting reactively but are guided toward measurable results, like faster deal closure, improved customer engagement, or SLA compliance.

   • Example: “Promptly obtaining this information will enable tailored support and help maintain strong customer engagement within the next few hours.”