In the telecommunications industry, the implementation of AI voicebots in contact centers has become essential for optimizing operational processes. The growth in 5G subscriptions and network expansion, as projected by the Ericsson Mobility Report November 2025, presents new opportunities for integrating AI solutions. Simultaneously, increasing global losses from telecom fraud, estimated by the CFCA Global Fraud Loss Survey 2025 at approximately $41.82 billion, highlights the need for enhanced security and authentication measures. This creates an architectural challenge where AI voicebots can boost efficiency, but their deployment demands a careful balance between automation and maintaining customer trust.
AI voicebots in telecom: new opportunities and risks for operators
The proliferation of mobile broadband, used by approximately two-thirds of the world’s population according to ITU Facts and Figures 2025, makes AI services more accessible. In the telecom sector, this translates to rising customer expectations for service speed. AI voicebots can handle a significant portion of routine inquiries, freeing up human agents to address more complex issues. This not only increases productivity but also improves customer satisfaction through rapid access to information.
Alongside these opportunities come specific risks. The primary one is fraud. Subscription fraud, based on a genuine or stolen identity, accounts for about $5.31 billion in annual losses, as per the CFCA Global Fraud Loss Survey 2025. Phishing remains a leading initial access vector, as noted by ENISA Threat Landscape 2025. The exploitation of legacy signaling protocols like SS7 and Diameter is also identified by ENISA as a risk for mobile networks. In this context, AI voicebots, which process sensitive information and perform transactions, become potential targets for attackers if not implemented with adequate security measures.
Where automation helps: improving efficiency and service speed
AI voicebots are transforming contact center operations by providing agents with tools to enhance efficiency. They can:
- Handle typical inquiries: Automatically provide information on tariffs, balances, service status, reducing wait times.
- Route calls: Intelligently determine the purpose of the inquiry and redirect the customer to the most competent agent, minimizing transfers.
- Collect initial information: Gather data (account number, nature of the problem) before connecting with an agent, allowing the agent to immediately focus on resolving the issue.
- Support agents in real-time: Provide agents with prompts, scripts, and access to knowledge bases during conversations.
- Operate 24/7: Ensure continuous service, regardless of the time of day or agent workload.
In practice, Softengi develops AI systems that integrate into telecom infrastructure to automate communications and optimize agent workload.
Risks to trust: how AI voicebots can undermine security and customer loyalty
The implementation of AI voicebots carries significant risks, particularly concerning trust and security. Key among these are:
- Fraud and phishing: Malicious actors may attempt to exploit voicebots to obtain confidential information or conduct fraudulent activities by impersonating customers. The risk increases if the voicebot lacks robust authentication mechanisms.
- Loss of trust due to errors: Incorrect responses, misunderstanding context, or a lack of empathy from the AI voicebot can lead to customer frustration and erode their loyalty to the brand.
- Signaling protocol vulnerabilities: If an AI voicebot is integrated into a network using legacy protocols like SS7 or Diameter, it can become an entry point for attacks aimed at intercepting calls or manipulating data.
- Call authentication issues: Without proper verification mechanisms, a voicebot can be used to bypass identification systems, complicating efforts to combat Caller ID spoofing.
To counter these risks, implementing call authentication and verification mechanisms is crucial. The STIR/SHAKEN standard, described by the FCC in its First Caller ID Authentication Report and Order, and the use of the Identity header in SIP to carry cryptographically signed information about the call’s origin, as per RFC 8224, form the basis for ensuring trust in the call source. DooxSwitch Platform (DooxSwitch‘s VoIP platform for softswitch, SIP routing, billing, and LCR) supports such standards, enabling telecom operators to integrate these mechanisms into their infrastructure.
A common mistake: attempting universal data cleansing for AI without prioritization
One of the most frequent errors in implementing AI voicebots is attempting a complete, universal cleansing of all available data before starting the project. Telecom operators handle vast amounts of data: Call Detail Records (CDR), billing information, call recordings, network traffic data, and customer profiles. Trying to ‘cleanse’ all of this simultaneously without a clear understanding of which data is critical for specific voicebot scenarios leads to project delays, significant costs, and low effectiveness.
In practice, an iterative approach is more effective. Instead of global cleansing, focus should be on data that directly impacts the chosen voicebot use cases. For example, for a voicebot answering balance inquiries, up-to-date and accurate billing data is paramount. For call routing, data on interaction history is key. Data governance should be structured to ensure data quality and consistency for specific AI models, rather than all corporate information at once. This allows for quicker achievement of initial results and gradual expansion of voicebot functionality.
Architectural example: integrating AI voicebots into the telecom ecosystem
In a typical architecture, an AI voicebot is not an isolated solution but is integrated into the existing telecom ecosystem. This includes:
- Contact center platform: The voicebot interacts with the existing Automatic Call Distributor (ACD) and Interactive Voice Response (IVR) systems.
- VoIP infrastructure: Integration occurs at the SIP protocol level, where the voicebot acts as a SIP agent. This may involve using Softswitch platforms like DooxSwitch Platform, which handle call routing and signaling.
- CRM/ERP systems: For access to customer profiles, interaction history, and billing information.
- Knowledge bases and NLU models: The core functionality of the voicebot relies on Natural Language Understanding (NLU) models and knowledge bases.
- Authentication and security systems: Implementing mechanisms like STIR/SHAKEN for call verification, and integrating with corporate Identity and Access Management (IAM) systems.
- Analytics platforms: For monitoring voicebot performance, analyzing customer interactions, and identifying anomalies.
System integration of these components for seamless AI solution deployment into complex telecom infrastructures is performed by companies such as Softline.
Key success factors: balancing technology, security, and customer experience
Successful implementation of AI voicebots in telecom operator contact centers requires a comprehensive approach that considers not only technological aspects but also security and customer trust. Key factors include:
- Clear scenario definition: Start with simple, well-defined scenarios where the voicebot can provide maximum value.
- Data quality: Invest in data governance and iterative cleansing, focusing on data critical for specific AI models.
- Robust security and authentication: Implement call verification mechanisms (STIR/SHAKEN, RFC 8224) and multi-factor authentication.
- Human-in-the-loop: Ensure the ability for a quick handover to a live agent in complex or sensitive situations.
- Monitoring and analytics: Continuously track voicebot performance, customer satisfaction, and identify potential threats.
- Transparency: Clearly inform customers about their interaction with the AI voicebot to build trust.
Adhering to these principles will enable telecom operators to effectively leverage the potential of AI voicebots while simultaneously protecting customers and maintaining their trust.
Contact center readiness checklist for AI voicebot implementation
- 2-3 pilot scenarios for automation have been defined (e.g., balance check, service status).
- An audit of data quality required for pilot scenarios (CDR, billing, CRM records) has been conducted.
- The existing VoIP infrastructure supports SIP and can be integrated with an external SIP agent.
- A process for transferring calls from the voicebot to an agent (human-in-the-loop) has been developed.
- Compliance of the architecture with STIR/SHAKEN requirements for Caller ID authentication has been analyzed.
- Metrics for pilot evaluation have been defined: FCR (First Call Resolution), AHT (Average Handle Time), CSAT (Customer Satisfaction).
- A transparency policy informing the customer about interaction with AI has been formulated.