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Challenge

How might we enable EOLO customer operations teams to collaborate with AI agents through a continuously updated Customer Digital Twin so that every customer interaction becomes faster, more contextual, more proactive, and more effective in protecting satisfaction and retention?

EOLO wants to transform customer assistance from reactive case handling into proactive customer experience management, using AI, Digital Twins and human expertise. The challenge aims to prototype a new operating model where human operators and specialized AI agents collaborate around a shared Customer Digital Twin to improve service quality, protect satisfaction, reduce churn risk and support better decision-making.

EOLO

EOLO is a leading Italian telecommunications provider specializing in ultra-broadband connectivity for homes and businesses. Through its Fixed Wireless Access network, the company brings reliable high-speed internet to communities across Italy, with a particular focus on rural areas and smaller towns underserved by traditional infrastructure.

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Challenge framing

Current Situation

Customer support teams currently rely on multiple sources of information, including customer profile data, interaction history, tickets, service status, network indicators, commercial information and internal knowledge. Operators must connect and interpret these signals to decide the right next action.

Although EOLO already consolidates customer, service, network, commercial and interaction data in a unified data platform, the challenge is activating this data in real time. Current pain points include fragmented context, manual interpretation, cognitive load, late detection of dissatisfaction or churn risk, and AI capabilities limited to isolated use cases.

Desired Situation

EOLO wants a Customer Operations model where each relevant interaction is supported by a living Customer Digital Twin that combines service, network, commercial, interaction and satisfaction context.

Specialized AI agents should generate recommendations, risk signals and action proposals across technical, network, commercial, care and retention dimensions, while the human operator remains responsible for empathy, judgment, approval and final decisions.

The ambition is to move from reactive support to proactive customer experience management, with faster, more contextual and more personalized interventions.

Stakeholders

Stakeholders

Internal stakeholders
Customer Operations, Customer Care, Technical Support, Retention/CRM, Network Operations, IT, Data/Analytics, Security, Privacy, Legal, Innovation, Transformation, business owners and management.

External stakeholders
EOLO customers, selected startups or solution providers, technology partners and potentially third-party service providers involved in customer operations.

Primary users
Frontline customer care operators, technical support operators, retention specialists, supervisors and team leaders.

Possible solutions

Possible Solutions

  • Agentic AI and multi-agent orchestration.
  • Customer Digital Twin platforms for service operations.
  • AI decision support for customer care, technical support or retention.
  • Customer experience intelligence and journey analytics.
  • Predictive churn, satisfaction and service quality analytics.
  • AI-native operator desktops or employee experience platforms.
  • Knowledge-grounded AI assistants for enterprise support teams.
  • Network/customer experience correlation and service assurance intelligence.
  • Human-in-the-loop AI governance, explainability and auditability.
  • Pre-deployment simulation and synthetic testing environments.

Technical Requirements

  • Customer Digital Twin based on customer, service, network, interaction, commercial and satisfaction data.
  • Specialized AI agents covering technical, network, commercial, care and retention dimensions.
  • Operator-facing interface showing context, AI reasoning, recommendations and required approvals.
  • Human-in-the-loop approval for sensitive, commercial, contractual or high-impact actions.
  • Integration with existing data platforms, APIs, CRM, ticketing, knowledge bases, network monitoring and reporting tools.
  • Role-based access, audit trails and explainability of AI outputs.
  • GDPR-aligned data handling and alignment with EOLO security, IT, legal and compliance policies.
  • Prototype-ready or pilot-ready maturity, with credible enterprise integration capability and path to scale.
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