Customer Experience and AI: from reactive support to continuous relationship

The main critical issues emerge when moving from theory to practice:

• Data privacy and compliance
  personalize without violating GDPR
• Bias in models
  Risk of bias in results
• Integration with legacy systems
  CRMs and ERPs not designed for AI
• Organizational readiness
  Lack of internal expertise
• Hidden costs
  training, governance and maintenance

Not surprisingly, about 70 percent of AI projects do not achieve the expected ROI.

What really changes in the CX

AI enables three main levers:

• Predictive analytics
  anticipate needs and behaviors
• Conversational AI
  fluid and personalized interactions
• Hyper-personalization
  experiences built on real-time data

But the real leap occurs when these capabilities are integrated into a coherent system.

Abacus works on customer experience as an integrated system, not as an additional layer.

Key areas:

• Predictive analytics for customer insights
  to anticipate churn and behavior
• Conversational AI
  To manage interactions in a scalable way
• Omnichannel integration
  To ensure continuity between touchpoints
• AI ethics and data governance
  for compliance and trust
• Proprietary LLMs
  For vertical applications and data control
• AI agentics
  to orchestrate complex processes

Expected Results.

A structured approach to AI-enabled CX enables:

• Increased retention and loyalty
• Reducing the operational load
• Faster response time
• Increased consistency of customer experience
• Increased trust through transparency and compliance