Many Support organizations want to be more customer-success focused, but few have the capacity to change.  To make this transition, Support resources need to focus on high value activities such as helping customers adopt and succeed with products and resolving new and challenging issues.

Consider the following scenarios for AI in Support:

1.  AI Enabled Self-Help

Known issues are resolved through intelligent automation that will match customer needs to available knowledge.  When new customer issues are identified they are flagged and prioritized based on need for addition to the knowledge base.

2.  Intelligent Resource Allocation

As customer issues are identified during a knowledge base search or new online ticket creation, issues will be triaged and directed to the most appropriate resource for resolution.

3.  Skills Enhancement

Intelligent monitoring of service interactions will result in recommendations for skills development for both customers and service staff.

4.  Relationship Development

Analysis of customer behaviors and sentiment will identify opportunities to sustain and enhance relationships.  Resources can be directed to head-off potential issues that will negatively affect relationships.  Targeted engagements will help to deliver value-added services to expand relationship value.

5.  Product Quality Improvements

Analysis of customer issues provides the foundation for prioritizing corrective actions and product enhancements.  Issues that otherwise may cause customers churn can be addressed before they defect.

6.  Proactive Issue Resolution

Deep data analytics will monitor product performance and usage telemetry to identify potential issues and apply corrective actions.