Customer Managemt

Synthetic Voice Fraud Bringing New Security Challenges to Telecom Networks

API TIT — Tue, 22 Sep 2026 11:27:50 +0000

Voice communications are facing a new security challenge as artificial intelligence makes it easier to reproduce a person’s speech. Traditional voice scams have relied on caller-ID manipulation, impersonation and social engineering, but synthetic audio adds another layer by making a fraudulent call sound like it comes from a trusted person or organisation. Tele Info Today notes that synthetic voice telecom fraud is therefore becoming a telecom cybersecurity issue involving both caller identity and voice authenticity.

Voice Fraud is Moving from Spoofed Numbers Toward Synthetic Identities

Caller-ID spoofing can make a call appear familiar, while a synthetic voice can make the conversation itself more convincing. An attacker can combine these techniques with social engineering to create a compound attack that is harder to assess through conventional caller cues.

INTERPOL’s 2026 Global Financial Fraud Threat Assessment reports that convincing voice clones can be created from as little as 10 seconds of audio in some circumstances. The report also identifies AI-enabled fraud services offering synthetic identity capabilities.

The security challenge is therefore moving beyond whether a number appears trustworthy. synthetic voice telecom fraud can exploit the gap between verified caller information and the apparent authenticity of the speaker.

Caller Authentication Does Not Establish Voice Authenticity

Caller authentication and voice authenticity answer different security questions. Network controls can help establish whether a calling number has been legitimately presented, while voice analysis can assess whether audio has characteristics associated with synthetic generation or manipulation.

A legitimate number can therefore still be used with a convincing cloned voice to persuade a recipient to disclose information or authorise an action. As telecom security expands across network infrastructure and identity controls, caller identity controls, voice security is becoming another layer in the wider security lifecycle.

Synthetic voice telecom fraud consequently creates a need for multiple independent signals rather than relying on a telephone number or voice impression alone.

Key Takeaway: Synthetic voice is making impersonation more convincing, increasing the need for layered caller authentication and network-level voice security.

Voice Security is Moving Toward Layered AI and Caller Verification

The emergence of synthetic audio is pushing telecom security beyond traditional caller screening. A network can establish information about a calling number, but that does not necessarily establish whether the voice itself is authentic. This is making synthetic voice telecom fraud increasingly dependent on multiple security signals, including caller identity, signalling information, call behaviour and AI-based audio analysis.

AI Detection is Moving into the Voice Network

Traditional approaches to unwanted calls have relied on reputation databases, known-number lists and traffic patterns. Synthetic voice creates a more adaptive threat because the audio can be generated or modified for each interaction. Detection systems therefore need to identify suspicious combinations of network and behavioural signals rather than relying on a single indicator.

GSMA’s 2026 case study on network-level voice protection describes AI-based systems analysing billions of call events and adapting to changing scam patterns. Hiya’s current voice-security network analyses approximately 28 billion calls per month across more than 40 countries, illustrating the scale at which automated call-risk analysis is increasingly being performed.

For synthetic voice telecom fraud, this creates a shift from static blocking toward continuous analysis. A suspicious call can potentially be assessed using the calling number, network characteristics, calling behaviour and other available signals before the recipient decides whether to trust the interaction.

Caller Verification is Adding Another Security Layer

AI-based audio detection is not the only response. Telecom and industry initiatives are also working on stronger ways to establish who is authorised to make a call.

Open Verifiable Calling, for example, is designed around cryptographically verifiable caller identity, allowing organisations to demonstrate that a number is associated with an authorised entity before the call reaches the recipient. This approach addresses a different part of the problem from synthetic-voice detection: rather than asking only whether the audio sounds genuine, it establishes whether the caller has a verifiable identity.

These approaches can therefore complement each other, Caller Verification, Who is making the call?, Voice Analysis, Does the audio show signs of manipulation and Network Intelligence, What additional context surrounds the call?

Together, these layers can provide more information for security decisions than any single control.

Synthetic Voice is Increasing Pressure on Detection Systems

Voice-security models also face a moving target. New synthesis techniques can produce different acoustic characteristics, while compression, background noise and network conditions can alter genuine and synthetic audio. Recent research on voice-authentication threats notes that anti-spoofing systems can struggle with previously unseen attack techniques, reinforcing the need for continuous evaluation and adaptation.

This makes synthetic voice telecom fraud a wider network-security problem rather than a problem that can be solved only through an audio classifier. Detection, caller verification and network-level intelligence increasingly need to operate together.

Voice Security is Becoming a Network Responsibility

Telecom voice security is increasingly moving beyond caller-ID protection toward a layered model that combines identity verification, signalling intelligence and analysis of the voice itself. Synthetic voice telecom fraud highlights the limits of relying on any single indicator, particularly when attackers can combine legitimate-looking numbers with AI-generated speech and social engineering.

Protecting voice services therefore requires cooperation between network operators, technology providers and the organisations receiving calls. Cryptographically verifiable caller identity, network-level monitoring and adaptive detection can provide complementary signals while maintaining appropriate controls over sensitive communications data.

Tele Info Today notes that the broader challenge is restoring trust in voice communications as synthetic audio becomes more accessible. As telecom networks strengthen caller verification and real-time detection, voice security is becoming an increasingly integrated part of the wider cybersecurity architecture supporting digital communications.

The post Synthetic Voice Fraud Bringing New Security Challenges to Telecom Networks first appeared on Tele Info Today.

Subscriber Identity Controls Becoming a Greater Telecom Security Priority

API TIT — Tue, 22 Sep 2026 11:18:05 +0000

Mobile networks increasingly depend on digital credentials that determine whether a subscriber, device or service is authorised to connect. As networks become more distributed and digital services rely more heavily on mobile identities, protecting these credentials is becoming a broader security requirement. Tele Info Today notes that the challenge now extends beyond the physical SIM to authentication, eSIM provisioning, subscriber identifiers and the systems responsible for managing them.

5G is Strengthening Subscriber Identity Protection

5G introduced stronger mechanisms for protecting permanent subscriber identity. Instead of transmitting the permanent identifier directly over the radio interface, 5G can use the Subscription Concealed Identifier, or SUCI, to conceal the Subscription Permanent Identifier, or SUPI, using the home network’s public-key infrastructure. This reduces unnecessary exposure of the permanent identity during initial network access.

The change is particularly relevant to roaming because subscriber identity can cross network boundaries. 5G also introduced additional mechanisms designed to strengthen authentication and home-network control when users connect through visited networks.

This makes subscriber identity security more than a question of protecting an identifier in transit. Operators also need to protect the credentials and systems that allow those identities to authenticate against the network.

SIM and eSIM Security is Becoming a Lifecycle Issue

The subscriber identity stored within a SIM or eSIM is supported by cryptographic material used for network authentication. A compromise can therefore affect network access and subscriber confidentiality. The UK’s Telecommunications Security Code of Practice 2026 requires public telecom providers to manage SIM-related security risks, review existing profiles and address vulnerabilities against current GSMA recommendations.

The guidance also highlights risks associated with profile-modifiable SIMs and eSIMs. Providers are expected to ensure that only trustworthy services can add, remove or modify eSIM profiles, while profile changes should be logged and monitored. This reflects a shift from treating SIM security as a fixed hardware property toward managing subscriber credentials throughout their lifecycle.

For subscriber identity security, that lifecycle includes provisioning, activation, authentication, profile modification, replacement and deactivation. Each stage creates opportunities for controls that can protect the subscriber and network.

Identity Controls are Expanding with Digital Services

Mobile identity is also increasingly relevant beyond basic network access. SIM and device relationships can influence account recovery, authentication and fraud controls for external digital services. A weakness in subscriber identity management can therefore have consequences beyond the telecom network itself.

As operators strengthen controls around SIM credentials, eSIM provisioning and subscriber identifiers, subscriber identity security is becoming a broader component of telecom cybersecurity. The next development is extending trusted identity information into controlled digital interfaces, allowing other services to use relevant network signals without receiving unrestricted access to subscriber data.

Identity Controls are Expanding Beyond the SIM

Subscriber identity security is increasingly extending beyond the credentials stored on a SIM or eSIM. As digital services use mobile numbers and device relationships for authentication, operators are developing ways to provide trusted identity signals without exposing underlying subscriber information. These signals can help external services determine whether a number is genuinely associated with a device, whether a recent SIM change has occurred or whether a device relationship has changed.

This is making subscriber identity security increasingly relevant beyond network access. The operator’s role can extend from protecting subscriber credentials to providing controlled signals that help banks, fintechs and other digital services assess identity-related risk.

SIM Swaps are Becoming a Security Signal

A SIM swap can change the relationship between a customer’s mobile number and the SIM or eSIM used to access the network. That creates a potential account-takeover risk when the mobile number is also used for authentication or account recovery. Instead of relying only on post-event investigation, digital services can use operator-provided signals to determine whether a recent SIM change should influence a transaction or authentication decision.

Number Verification can similarly confirm whether the mobile number used by a digital service corresponds with the number associated with the device and network session. These capabilities can reduce dependence on SMS-based verification in some scenarios while keeping the operator involved in establishing the relationship between the subscriber, device and number.

For subscriber identity security, this creates a transition from static credentials toward continuously evaluated identity relationships. It also establishes a bridge between telecom security and network based identity verification. The next step is allowing these signals to reach authorised external services through standardised network APIs.

Mobile Identity APIs are Scaling Globally

GSMA Open Gateway provides evidence of this broader development. In March 2026, GSMA reported that 86 operator groups, representing more than 300 networks and 80% of global mobile connections, were aligned around its common API framework. Identity-related services include Number Verification and SIM Swap capabilities, alongside other network APIs.

The scale of the framework indicates that mobile identity signals are becoming part of a wider network-API ecosystem rather than remaining confined to individual operator systems. This can create a more direct connection between telecom infrastructure and external fraud-prevention or authentication services.

However, access to identity signals still requires appropriate authorisation, data protection and clear limits on how information can be used. Subscriber identity security therefore increasingly involves both protecting the underlying credential and controlling how trusted identity signals are exposed to external systems.

Subscriber Identity is Becoming an End-to-End Security Layer

Subscriber identity is increasingly becoming a security layer that connects network authentication with digital services. Protecting SIM and eSIM credentials, concealing permanent identifiers and monitoring changes to subscriber-device relationships can help operators reduce risks that extend beyond network access.

This makes subscriber identity security increasingly dependent on controls that operate across the full identity lifecycle, from provisioning and authentication to profile changes and account recovery. The growing use of network-based identity signals also creates opportunities for external services to incorporate trusted telecom information into their own security processes.

As these capabilities develop, identity protection will increasingly depend on coordination between operators, technology providers and digital-service platforms. Tele Info Today will continue to examine how telecom security is expanding from protecting network infrastructure toward managing the identity signals that connect subscribers with the wider digital ecosystem.

The post Subscriber Identity Controls Becoming a Greater Telecom Security Priority first appeared on Tele Info Today.

Proactive AI Bringing Network Intelligence Into Customer Service

API TIT — Mon, 14 Sep 2026 07:06:18 +0000

Telecom customer service has traditionally been reactive. Customers report a connectivity problem, failed service or performance issue, after which support teams investigate the cause and determine the appropriate response. Network operations may already have information about an incident or degradation, but that information does not always reach the customer-service layer before the customer makes contact.

This is creating a role for proactive telecom AI that connects network intelligence with customer-service processes. Instead of waiting for a complaint, AI systems can potentially identify service conditions that are likely to affect customers, determine which customers or services are exposed and trigger an appropriate response. The shift is therefore from responding to known customer problems toward identifying potential problems before they become support requests.

Network Intelligence is Moving Closer to Customer Care

Telecom networks continuously generate information about coverage, availability, latency, throughput, faults and service performance. Historically, much of this information has remained within network operations environments, while customer-care platforms have worked primarily with account records, interaction histories and support cases.

Connecting these domains can create a more complete view of the customer experience. A network degradation affecting a particular area, for example, can be matched with the customers using services in that location. A customer-care system can then potentially recognise that a reported problem is associated with an existing network condition rather than treating the interaction as an isolated case.

This is where proactive telecom AI differs from conventional customer-service automation. The system is not simply using AI to answer a request more efficiently. It is using network and service intelligence to determine whether an intervention may be appropriate before the customer initiates contact.

AI is Moving Customer Service From Detection Toward Prevention

The broader development is connected to the industry’s movement toward customer-aware autonomous networks. TM Forum’s 2026 work on customer-aware autonomous networks describes AI agents, digital twins and closed-loop automation being used to detect, decide and act on network issues before customers are affected. The approach combines network conditions with customer-impact information so that corrective action can be prioritised according to its effect on service experience.

This introduces a different operating model for customer care. Instead of waiting for contact volumes to increase after an outage or degradation, an operator could potentially identify an affected customer group and provide information about the issue, recommend an alternative or initiate an approved remediation workflow.

The approach can extend beyond major outages. Performance deterioration, recurring service problems, activation failures and other operational signals can potentially be assessed against customer information to identify situations where early intervention could reduce friction.

Proactive telecom AI can therefore connect two traditionally separate activities: understanding what is happening within the network and understanding which customers are likely to experience the consequences.

The technology is still developing, and reliable proactive service depends on accurate network data, timely customer information, well-defined decision rules and controlled automation. Operators also need to avoid unnecessary interventions when network signals do not translate into meaningful customer impact.

Nevertheless, proactive telecom AI is establishing a more preventive model for telecom customer care, in which network intelligence can become an input to customer-service decisions rather than remaining solely within network operations.

Network Intelligence is Enabling More Proactive Customer Care

The value of proactive customer service depends on connecting network intelligence with customer-impact information. Detecting a fault is only the first step. Operators also need to understand which services and customers may be affected, how significant the impact could be and whether an intervention is appropriate. AI can help connect these signals and support decisions before a network problem becomes a customer complaint.

This is making proactive telecom AI increasingly relevant to the evolution of autonomous network operations. TM Forum’s current work describes Level 4 autonomous networks as using predictive analysis and closed-loop management for service- and customer-experience-driven operations. The objective is to move from reacting to degraded service toward anticipating and addressing issues earlier.

Network Events are Becoming Customer-Service Signals

A network event does not affect every customer in the same way. A localised degradation may affect a specific group of mobile users, while a broader outage can create a much larger service impact. Connecting network events with customer and service information allows an AI system to distinguish between these situations and prioritise responses accordingly.

For example, if network monitoring identifies deteriorating performance in a specific area, an AI system could match that information with the customers and services operating there. Customer-care teams could then receive an earlier indication of potential impact, while customers could potentially receive an appropriate notification before contacting support.

This creates a different relationship between network operations and customer care. Proactive telecom AI can act as a bridge between technical signals and customer experience, translating network conditions into information that can support service decisions.

Closed-Loop Automation is Moving Toward Prevention

The next stage is connecting detection with controlled remediation. TM Forum’s CX Optimization via AI-Driven SOC Catalyst is designed around closed-loop automation in which AI agents analyse live network events, formulate remediation plans, validate potential actions through digital twins and deploy approved responses. The stated goal is to predict, prevent and resolve customer-experience issues before customers are impacted.

This model reduces the distance between detecting a network problem and acting on it. Instead of generating an alert that requires a separate team to investigate, the system can potentially determine the likely customer impact, identify an appropriate response and execute a defined remediation workflow.

Human oversight can still remain important, particularly when an action could affect a large customer population or alter network behaviour. The role of AI is therefore not necessarily unrestricted autonomy, but faster interpretation and execution within controlled operational boundaries.

Autonomous Networks are Creating a Larger Role for Customer Experience

The broader investment in autonomous networks also indicates that proactive customer outcomes are becoming part of the industry’s automation agenda. TM Forum reported in June 2026 that 75% of operators planned to increase autonomous-network investment in 2026, while 81% aimed to reach Level 4 or above by 2030. The same initiative links higher levels of network autonomy with improvements in customer experience and service resilience.

Proactive telecom AI fits into this wider shift because customer experience can become an operating input rather than simply an outcome measured after an incident. Network decisions can increasingly consider whether a change will prevent service degradation, reduce customer impact or improve perceived service quality.

Key Takeaway: Growing investment in autonomous networks is creating the technical foundation for more predictive, customer-experience-driven operations.

The development points toward customer care that can respond before a customer has to report a problem. As network intelligence becomes connected to customer and service information, proactive telecom AI can help operators identify potential impact earlier and bring customer experience into the network decision-making process.

Customer Care is Becoming More Proactive

Telecom customer service is beginning to move from a model based on reported problems toward one that can use network and customer intelligence to anticipate potential service issues. Connecting network events with information about affected services and customers can help operators identify where intervention may be useful before complaints begin.

This makes proactive telecom AI an important link between network operations and customer experience. Its effectiveness will depend on accurate network signals, reliable customer data, appropriate decision rules and controlled automation.

As autonomous network capabilities develop, proactive telecom AI could allow operators to identify customer-impacting conditions earlier, communicate more effectively and initiate approved remediation before problems escalate. The broader shift is toward customer care becoming part of a predictive operating model rather than remaining primarily reactive.

The post Proactive AI Bringing Network Intelligence Into Customer Service first appeared on Tele Info Today.

AI Agents Bringing New Capabilities to Telecom Voice Services

API TIT — Mon, 14 Sep 2026 05:17:20 +0000

Telecom voice support is moving beyond traditional interactive voice response systems and scripted voice bots toward systems that can understand natural-language requests and participate in broader service workflows. Conventional IVR generally guides customers through predefined menus, while earlier voice bots were designed around narrower commands and scripted responses. Generative AI is changing that model by allowing voice systems to interpret more varied requests and maintain a more conversational interaction.

This is creating a broader role for AI voice agents in telecom customer service. Instead of simply identifying an intent and directing the caller toward another process, an agent can potentially understand the request, retrieve relevant information, use connected tools and continue through an authorised workflow. The important change is therefore not only more natural speech, but the ability to connect spoken interaction with service execution.

From Scripted IVR to Conversational Voice Support

Traditional IVR remains useful for predictable requests at scale, but its menu-based structure can create friction when a customer’s issue does not fit neatly into predefined options. Natural-language voice systems offer a different interaction model by allowing customers to describe a problem in their own words.

This is where AI voice agents can provide a more capable interface. A customer could explain that a recent bill appears higher than expected, report a connectivity problem or ask about an existing order without navigating through several layers of options. The system can interpret the request and determine which information, tools or workflows are relevant.

The development is also moving beyond speech recognition itself. A capable voice agent needs to combine voice interaction with access to business systems, defined procedures and operational tools. TM Forum’s 2026 Contact Center Voice Agent Implementation Guide identifies four capability areas: voice interaction, task completion, tool-calling and SOP compliance.

Voice Agents are Moving Toward Task Completion

The distinction between a voice bot and an agent becomes clearer when the system is expected to complete work rather than simply provide an answer. A billing interaction, for example, could involve checking the customer’s account, reviewing the relevant charge, explaining the result and initiating an approved follow-up action.

The same principle can apply to technical support. A caller reporting a connectivity problem may require checks against service status, account information and previous troubleshooting steps before the issue can be resolved. A connected voice agent can potentially coordinate those checks within the same conversation rather than transferring the customer between separate processes.

AI voice agents therefore represent an evolution in how telecom operators can use voice as a customer-service interface. The objective is not simply to make automated calls sound more natural. It is to connect spoken interaction with the data, tools and workflows required to deliver a useful outcome.

Key Takeaway: Telecom voice agents are being defined around more than conversational speech, with task completion, tool access and procedure compliance becoming core capabilities.

The direction is therefore toward voice systems that can participate in telecom service workflows rather than simply guide customers through them.

AI Agents are Extending Voice Services into Telecom Workflows

The move toward more capable voice support is closely linked to the ability of AI systems to connect speech with telecom data and operational tools. A customer may begin with a simple spoken request, but resolving it can require information from billing, customer accounts, order management or service-assurance systems. Connecting these systems allows the voice interaction to continue beyond recognition and response.

This is making AI voice agents more dependent on the same underlying data and system integrations that support agentic customer service across other channels. Voice becomes the interaction layer, while the systems behind it provide the information and actions required to complete the request.

Voice Interactions are Becoming More Action-Oriented

A customer contacting an operator about an unpaid bill may previously have been guided through an automated menu before being transferred to another service process. A more capable voice agent could potentially identify the account, retrieve the relevant billing information, explain the issue and initiate an authorised payment or follow-up workflow during the same interaction.

Technical support offers another example. A caller experiencing poor connectivity may need more than a generic troubleshooting response. The system could potentially check service status, identify a known incident, review previous support activity and determine whether additional troubleshooting is necessary.

This makes AI voice agents different from voice systems built primarily around intent recognition. Their effectiveness increasingly depends on whether they can access the right tools and use the information returned by those tools to determine the next step.

Telecom Voice Automation is Expanding Beyond the Call

There are already examples of telecom operators applying AI to outbound and inbound voice processes. In an Airtel and IBM deployment, an AI voice bot used for bill-payment reminder calls increased payment completion from 60% to 64%, while the team supporting the process could be reassigned to other activities. The example illustrates how voice automation can be connected to a defined business objective rather than being evaluated only through conversation volume.

The broader opportunity extends across customer-service workflows such as payment reminders, plan information, order updates, service troubleshooting and account-related requests. Different use cases require different levels of system access, which makes permissions and workflow controls important components of the architecture.

A voice agent that can retrieve information should not automatically have the authority to modify an account. Operators therefore need to distinguish between reading customer data, recommending an action and executing that action. This creates a controlled path between conversational AI and telecom operational systems.

Voice Agents are Becoming Part of the Service Architecture

The development also changes how voice services can fit into the wider customer-service environment. Instead of treating voice automation as a standalone IVR replacement, operators can increasingly position it as another interface to shared AI capabilities, customer data and business processes.

A customer could begin an interaction through voice, receive a response based on account and service context, and then be transferred to a human representative with the relevant information already available. This continuity can reduce the need to restart the interaction when automation reaches a point where human involvement is required.

The same architecture can potentially support other customer channels, allowing the underlying agent to work across voice, chat and digital interfaces while drawing on common data and tools. AI voice agents therefore have the potential to extend agentic customer service into situations where speaking remains a more natural or accessible way for customers to interact with their telecom provider.

Key Takeaway: A telecom voice-AI deployment demonstrated measurable improvement in payment completion while reducing the operational effort required for the calling process.

The significance of these developments is that voice automation is becoming connected to measurable service outcomes. As telecom operators give voice systems access to more data and controlled tools, AI voice agents can move from answering spoken questions toward completing increasingly defined customer-service workflows.

Voice is Becoming a More Capable Telecom Service Interface

Telecom voice services are moving beyond scripted menus and basic voice bots toward systems that can understand natural-language requests, access relevant information and participate in defined service workflows. The change is therefore not only about making automated conversations sound more natural, but about connecting voice interaction with the systems needed to resolve customer requests.

This makes AI voice agents increasingly relevant as another interface for agentic customer service. Their effectiveness will depend on access to accurate data, connected tools, clear permissions and reliable escalation paths when automated handling is not appropriate.

As these capabilities mature, AI voice agents could make routine telecom interactions more direct by bringing conversation, information retrieval and approved service actions into the same workflow. Voice could consequently become an increasingly important channel for task-oriented customer care rather than simply a front end for traditional IVR.

The post AI Agents Bringing New Capabilities to Telecom Voice Services first appeared on Tele Info Today.

Interoperability Shaping the Emerging Telecom Agent Ecosystem

API TIT — Mon, 14 Sep 2026 05:03:26 +0000

Telecom operators are moving from individual AI deployments toward environments in which multiple agents may need to work across customer service, billing, service management and network operations. A customer-service agent may identify an issue but require information from a network agent, while a service agent may need to coordinate with billing or order-management systems before a request can be completed. This creates a new requirement beyond the capabilities of any individual AI system: the agents must be able to work together.

This is making telecom agent interoperability increasingly important as agentic systems expand across telecom operations. Interoperability can allow agents to exchange requests, share relevant context and coordinate tasks without requiring every agent to use the same underlying model, application or vendor platform.

Telecom Agents are Expanding Across Multiple Domains

A customer interaction can involve several operational domains even when the initial request appears simple. A connectivity complaint, for example, may require a customer-service agent to obtain service information, determine whether a network incident exists and then coordinate with a technical workflow. Keeping each function inside an isolated AI application can limit how far automation can extend.

Agent-to-agent frameworks are emerging to address this problem. TM Forum’s Agent to Agent Protocol for Telecoms, or A2A-T, is designed to support communication and collaboration between agents operating across different telecom domains. Its 2026 version focuses on enabling agents to exchange information and coordinate activities across services, network functions and business processes.

This gives telecom agent interoperability a practical role in multi-step customer-service workflows. Instead of one agent attempting to access every system directly, it can potentially request a specific task from another specialised agent. The receiving agent can then use its own tools and permissions to complete that part of the workflow and return the relevant result.

Agent Collaboration is Becoming an Architectural Requirement

The need for interoperability increases as operators deploy more specialised agents. Billing, customer experience, network operations and service assurance may each require different capabilities, data access and operating controls. A common interaction layer can allow these agents to collaborate while preserving their individual roles.

TM Forum’s broader AI-Native ODA work similarly identifies fragmented AI platforms and incompatible agent frameworks as barriers to scaling agentic operations. The objective is to create a more modular and composable environment in which different AI capabilities can participate in shared workflows.

Telecom agent interoperability therefore is not simply about connecting two AI applications. It is about creating a framework through which specialised agents can discover capabilities, exchange structured information and coordinate actions across telecom domains.

Key Takeaway: Telecom agent ecosystems require interoperable mechanisms that allow specialised agents to coordinate tasks across customer, service, network and business domains.

The emergence of these interconnected workflows marks a shift from isolated AI applications toward collaborative agent environments. Telecom agent interoperability will increasingly determine whether individual agents can operate as parts of a wider telecom service ecosystem.

Shared Semantics are Becoming Critical to Telecom Agent Collaboration

Communication between agents is only useful when the systems can interpret the information being exchanged in a consistent way. Telecom environments contain specialised terminology, service relationships and operational states that may be represented differently across platforms. An agent may be able to send a request to another agent, but differences in data structures or meaning can still prevent the receiving system from acting on it correctly.

This makes telecom agent interoperability dependent on more than a common communication protocol. Agents also need shared definitions for customers, services, network conditions, incidents, orders and actions. Without this semantic layer, interoperability can remain technically connected while operational workflows continue to break down.

Agents Need a Common Understanding of Telecom Context

Consider a customer-service agent identifying a network-related issue. It may send information about the affected service to a network agent, which then needs to understand the service identifier, location, fault condition and requested action in the same way as the originating system.

This is why TM Forum’s 2026 work on the Internet of Agents places significant emphasis on ontology-driven semantics. The initiative is designed to support interoperable collaboration by giving agents a common understanding of the entities and relationships involved in digital-service interactions. TM Forum’s related work on operationalising ontologies for AI-native autonomous networks similarly focuses on semantic interoperability and contextual reasoning across network operations.

For telecom agent interoperability, this semantic consistency can be as important as the communication mechanism itself. A shared meaning allows one agent to interpret another agent’s request without relying on bespoke translations for every connection.

Open Architectures are Reducing Agent Fragmentation

The challenge becomes larger as operators deploy agents from multiple vendors and across different operational domains. A proprietary customer-service agent may need to work with a network agent supplied by another technology provider, while both interact with existing telecom systems.

TM Forum’s AI-Native ODA roadmap addresses this fragmentation by promoting a more modular architecture in which AI capabilities and agents can be composed across domains rather than remaining isolated inside proprietary platforms. The approach brings together agentic AI, data architecture, security and governance as parts of the same operating environment.

This also creates an important distinction between interoperability and standardisation. Operators do not necessarily need every agent to use the same model or implementation. They need common mechanisms for discovering capabilities, exchanging relevant information and maintaining consistent meanings across those implementations.

Key Takeaway: Telecom interoperability is developing across multiple layers, from agent communication and architecture to shared semantics and operational context.

The emerging model is therefore broader than simply allowing agents to communicate. Telecom agent interoperability requires a combination of protocols, architectural standards and shared semantics so that different agents can collaborate without creating new integration silos.

Interoperability Could Turn Individual Agents into a Telecom Ecosystem

Telecom operators are moving toward environments where specialised AI agents can collaborate across customer service, billing, service management and network operations. Making that collaboration reliable requires more than connecting systems. Agents need common ways to communicate, interpret telecom context and operate within defined permissions.

This makes telecom agent interoperability central to the transition from isolated AI applications toward coordinated agent ecosystems. Shared protocols, common semantics and open architectural frameworks can allow agents from different domains and technology providers to participate in the same workflow without requiring every system to use identical models or platforms.

As these standards and frameworks mature, telecom agent interoperability could allow operators to build more flexible agent ecosystems in which customer, service and network capabilities can work together. The longer-term opportunity is to move from individual AI agents performing separate tasks toward coordinated systems capable of supporting end-to-end telecom workflows.

The post Interoperability Shaping the Emerging Telecom Agent Ecosystem first appeared on Tele Info Today.

Telecom Data Architecture Supporting the Rise of AI Agents

API TIT — Mon, 14 Sep 2026 05:02:07 +0000

The expansion of AI agents in telecom is exposing a fundamental requirement: agents need reliable access to the data that allows them to understand a customer request, make a decision and take an appropriate action. Telecom operators hold this information across customer relationship management, billing, network, service and operational platforms, often with different data structures and access mechanisms.

This makes telecom AI data architecture increasingly important to the development of agentic customer service. An AI agent may need to combine account information with interaction history, service status and operational data before it can determine what a customer needs. Without a consistent way to discover, access and govern those sources, adding more AI agents can simply create more isolated applications rather than a connected customer-service environment.

AI Agents Need More Than a Model

A large language model can interpret a customer’s request, but it does not automatically know the current state of an account, whether a service is active or whether a network incident is affecting the customer. Those answers have to come from connected enterprise systems.

This creates a distinction between the AI model and the data architecture supporting it. A customer asking why a bill has changed could require access to billing records, plan information, previous interactions and relevant service data. A technical-support interaction could additionally require network or device information.

Telecom AI data architecture therefore needs to make different information sources accessible to AI systems while preserving the controls associated with each source. TM Forum’s AI-Native Blueprint identifies Data Architecture as one of four core workstreams alongside Agentic AI, Security & Governance and AIOps, reflecting the need to develop these capabilities together rather than treating data as a separate infrastructure problem.

Breaking Down the Data Silos

Fragmented data is a particular challenge in telecom because operators commonly run multi-vendor environments with information distributed across legacy and modern systems. TM Forum notes that siloed data and fragmented architectures remain structural barriers to scaling AI, while its AI-Native ODA roadmap argues that isolated AI deployments cannot coordinate decisions across customer, service and network domains without a common architectural foundation.

For customer service, this means an agent should not need to operate within a single application or rely on manually transferred information. A connected architecture can allow customer, service and operational data to be accessed according to the requirements of a particular workflow.

This also changes the role of data architecture. The objective is not simply to store more information, but to make relevant data discoverable, accessible and usable by AI systems while maintaining data quality, permissions and governance.

Data Quality is Becoming an Agent Capability

An agent can only be as reliable as the information it retrieves. An outdated account record, inconsistent service status or incomplete interaction history can lead to an incorrect recommendation or an inappropriate action. This makes data freshness, consistency and lineage important considerations for agentic customer service.

TM Forum’s Modern Data Architecture work specifically highlights the need for telecom data environments to support collaboration and reuse while ensuring that data consumers only access information they are authorised to process. It also identifies the increasing complexity of data applications as a reason for combining easier data access with governance and policy enforcement.

For AI agents, this means the data layer becomes part of the operational control system. Telecom AI data architecture has to determine not only where information resides, but how an agent can discover it, whether it is current enough to use and what actions the agent is permitted to take based on it.

Key Takeaway: AI agents require a governed data foundation that can connect customer, service and network information without removing the controls around access and use.

The development of agentic customer service is therefore becoming as much an architectural challenge as an AI challenge. As operators move beyond isolated pilots, telecom AI data architecture will increasingly determine whether agents can work across multiple telecom domains with the information and controls required for reliable customer interactions.

Telecom Data Architecture is Connecting AI Agents to Operational Systems

The usefulness of an AI agent depends on whether it can access the information and systems required to act on a customer request. In telecom, those resources are distributed across multiple operational environments, including CRM, billing, order management, service assurance and network platforms. An architecture that connects these sources can give agents access to a broader operational view without requiring every application to be rebuilt around AI.

This is making telecom AI data architecture an important layer between telecom systems and agentic applications. Rather than giving an agent unrestricted access to every data source, operators can create controlled pathways through which the agent retrieves the information required for a specific task.

Data Access is Becoming more Structured

A connected data architecture can allow agents to discover relevant information without depending on a single database or application. An account-related request might require billing and subscription data, while a connectivity problem could require service and network information. The architecture therefore needs to support different combinations of data depending on the workflow.

APIs, data services and common information models can help create these connections. They allow an AI agent to request specific information from an underlying system while keeping the operational system itself separate from the conversational interface.

This is particularly relevant as operators deploy multiple agents for different functions. An agent handling billing enquiries should not need the same access as one supporting technical troubleshooting, while both may need some shared customer information. Telecom AI data architecture can provide the common foundation while allowing access to be determined by the role and purpose of each agent.

Real-Time Data is Becoming more Important

Agentic customer service also increases the importance of data freshness. A response based on an outdated service status can be misleading, while an old account record can result in an incorrect recommendation. Data architectures therefore need to distinguish between information that can be retrieved from relatively stable records and information that needs to be obtained in real time.

Network information provides a clear example. If a customer reports poor connectivity, the agent may need the current service state rather than a historical network record. Similarly, an order-related enquiry may depend on the latest fulfilment status rather than information from an earlier system update.

This makes telecom AI data architecture more than a storage or integration layer. It becomes part of the mechanism that determines which information is available to an agent, when that information was last updated and whether it is appropriate to use for a particular decision.

Governance is Becoming Part of the Architecture

Greater access to data also introduces stronger governance requirements. Telecom customer information can include account, billing, usage and service records, meaning that agents cannot simply be given broad visibility across all enterprise systems.

Access policies can restrict an agent to the information required for its task, while authentication and authorisation controls can determine which actions are available after information has been retrieved. Logging and audit mechanisms can also provide visibility into what data an agent accessed and which workflows it initiated.

This becomes increasingly important as agents move from answering questions toward taking action. Retrieving an invoice and changing a customer’s service are fundamentally different operations and should not carry the same level of authority.

The resulting architecture needs to balance accessibility with control. Telecom AI data architecture can support more capable agents by bringing fragmented information together, while governance determines how that information can be accessed and used.

As telecom operators move toward larger agent ecosystems, the data layer will increasingly need to support reuse, real-time access and controlled interaction across multiple domains. That foundation will determine how effectively AI agents can move between customer, service and network contexts without creating new silos.

Data Architecture is Becoming a Foundation for Agentic Telecom Services

The expansion of AI agents in telecom is increasingly dependent on how customer, service and network information is organised and made accessible. Connecting these sources can allow agents to understand requests using current operational context rather than relying on isolated application data.

This makes telecom AI data architecture a foundational element of agentic customer service. Its role is not only to connect systems, but also to manage data quality, access permissions, freshness and governance so that agents can use information reliably.

As operators move from individual AI deployments toward broader agent ecosystems, telecom AI data architecture will determine how effectively agents can work across customer, service and network domains. A stronger data foundation can help prevent new AI silos while giving agents the controlled access they need to support increasingly complex telecom workflows.

The post Telecom Data Architecture Supporting the Rise of AI Agents first appeared on Tele Info Today.

Context Aware AI Bringing More Personalisation to Telecom Customer Care

API TIT — Mon, 14 Sep 2026 04:57:39 +0000

Telecom customer care is moving toward a model in which artificial intelligence can understand more than the immediate question being asked. Traditional customer-service systems often rely on predefined customer profiles and scripted workflows, while newer AI systems can combine information from previous interactions, active services and current service conditions to build a more complete picture of the customer’s situation.

This is making context aware telecom AI increasingly relevant to personalised customer care. Instead of treating every interaction as a new request, an AI system can potentially understand what has already happened, what services the customer uses and what information is relevant to the current problem. The result can be a more targeted response that reflects the customer’s actual circumstances.

Personalisation is Becoming More Contextual

Traditional personalisation often relies on relatively fixed attributes such as customer segments, tariff plans or account history. These inputs can be useful, but they may not explain what a customer needs at a particular moment. A customer experiencing a service problem, for example, may require a very different response from the same customer asking about an upgrade.

Context-aware systems can combine information around the specific interaction. This can include previous support conversations, unresolved cases, current products, billing information, device details and service conditions. Bringing these elements together allows an AI system to interpret the request within a broader operational context.

This is where context aware telecom AI differs from basic recommendation or segmentation tools. The objective is not simply to identify what type of customer is making the request. It is to understand the circumstances surrounding the request and use that information to determine a more relevant response.

Customer History is Becoming Part of the Interaction

Interaction history can be particularly valuable when customers contact support repeatedly. Without access to previous information, a new conversation may begin with the same questions and troubleshooting steps that the customer has already completed. A context-aware system can potentially carry that history into the next interaction and avoid unnecessary repetition.

For example, a customer reporting a broadband issue could be connected to information showing that the problem has already been reported, a troubleshooting step was previously attempted and a known service incident is affecting the area. The response can then begin with the actual state of the case rather than a generic troubleshooting script.

Context aware telecom AI can also support more useful handoffs to human representatives. Instead of transferring only the customer’s latest message, the system can provide relevant interaction history, account context, actions already attempted and information gathered during the automated conversation.

From Customer Profiles to Situational Understanding

The broader shift is therefore from static personalisation toward situational understanding. AI systems can potentially combine customer information with operational context and the immediate purpose of the interaction, creating a more complete basis for decision-making.

This approach still depends on reliable data access, appropriate permissions and accurate information. More context does not automatically produce better service if the information is outdated or irrelevant.

As telecom operators connect more customer and service information, context aware telecom AI is becoming a potential foundation for more relevant customer interactions. The development is moving personalisation beyond customer profiles toward systems capable of understanding the circumstances surrounding each service request.

Context-Aware AI is Connecting Customer and Service Data

Personalisation becomes more useful when customer context can be combined with information about the service itself. Telecom interactions often involve more than account details: a customer may be affected by a network incident, using a particular device, waiting for an unresolved order or contacting support after previous troubleshooting attempts. Bringing these signals together allows an AI system to interpret the request against the current state of the customer and the service.

This makes context aware telecom AI dependent on access to multiple sources of operational information. Customer relationship management systems can provide interaction history, while billing, order management, device and service-assurance systems can add the information needed to understand what is happening at the time of the interaction.

Customer Context is Expanding Beyond Account History

A customer profile provides only part of the information needed for relevant support. A mobile customer might have a long interaction history but still require a different response depending on whether they are reporting poor coverage, questioning a charge or considering a plan change.

Context-aware systems can combine these different signals rather than treating them independently. Previous conversations can show what has already been discussed, account information can establish which services are active, and network or service data can indicate whether an issue is broader than the individual customer.

This approach can also improve continuity between automated and human support. An AI system that retains the relevant history can pass the customer representative a clearer picture of the issue, including the original request, information already collected and troubleshooting steps that have been completed. TM Forum’s work on context-aware agentic AI similarly focuses on combining real-time data access with conversation history and telecom systems to support more relevant decisions.

Context is Becoming a Data Integration Challenge

The quality of personalisation therefore depends on how effectively different information sources can be connected. Telecom environments often contain fragmented data across CRM, OSS and BSS platforms, making it difficult for AI systems to build a consistent view of the customer.

This is one reason telecom-specific agentic architectures are placing greater emphasis on real-time access and system integration. In one TM Forum Catalyst, an agentic system was designed to access CRM and operational systems while using conversation history to support decisions during live customer interactions. The architecture was intended to combine customer records, network states and workflow actions rather than treating the AI layer as a standalone chatbot.

A broader industry challenge is that telecom AI deployments still face fragmented data and legacy infrastructure. GSMA and TM Forum have identified these structural issues as barriers to scaling AI beyond individual use cases.

Personalisation Still Requires Data Boundaries

More context does not automatically mean better customer care. AI systems need to distinguish between information that is necessary for the interaction and information that should remain restricted. Customer data can include sensitive account details, payment information and service records, making permissions, data minimisation and auditability important parts of the architecture.

This creates a balance between relevance and control. A system may need current network information to explain a service problem, but it does not necessarily need unrestricted access to unrelated customer records. Context aware telecom AI therefore needs structured access to relevant information rather than unrestricted visibility across every telecom platform.

The direction of travel is toward customer interactions that are informed by a much wider operational context. Context aware telecom AI can combine customer history, service information and real-time conditions to make support more relevant, while controlled data access determines how safely that personalisation can be delivered.

Key Takeaway: Personalised telecom support increasingly depends on combining customer history with account, service and real-time operational context rather than relying on static customer profiles alone.

Context is Becoming Central to Telecom Customer Care

Telecom personalisation is moving beyond static customer profiles toward systems that can understand the circumstances surrounding each interaction. Combining customer history with account, service and real-time network information can help AI distinguish between routine enquiries and issues that require a more specific response.

This makes context aware telecom AI increasingly relevant as operators connect customer-service platforms with broader operational data. The value comes from giving AI access to the information needed to understand the situation, while maintaining clear controls over what data can be accessed and how it can be used.

As these integrations develop, context aware telecom AI could make customer interactions more continuous, relevant and responsive. The next step will be extending this contextual understanding into proactive service, where network intelligence can help identify and address customer issues before they become direct support requests.

The post Context Aware AI Bringing More Personalisation to Telecom Customer Care first appeared on Tele Info Today.

AI Agents Moving Telecom Customer Service Beyond Chatbots

API TIT — Mon, 14 Sep 2026 04:55:39 +0000

Telecom customer service is moving from systems designed primarily to answer questions toward AI that can understand customer requests, access relevant information and perform actions across service workflows. Traditional chatbots have improved automated support by handling frequently asked questions and predefined requests, while generative AI has made conversations more flexible. The next development is the use of agents that can connect those conversational capabilities with the systems needed to actually complete customer tasks.

This is giving AI customer service agents a broader role in telecom support. Instead of responding with information and leaving the customer to complete the next step, an agent can potentially retrieve account details, check service information, troubleshoot an issue and initiate an approved workflow within the same interaction. The difference is therefore less about how naturally the system talks and more about what it can do after understanding the customer’s objective.

AI Agents are Moving Telecom Customer Service Beyond Chatbots

The distinction becomes clearer when customer service is viewed as a sequence of tasks. A conventional chatbot may identify an intent, retrieve an answer and direct a customer toward the appropriate process. An agentic system can potentially break a request into multiple steps, access different tools, evaluate the information returned and continue until the task is completed or requires human intervention.

This model is already being explored within the telecom industry. TM Forum’s agentic AI work has examined systems in which multiple AI agents can interact across business domains and access different business systems, moving beyond isolated applications such as chatbots and ticket triage. One of its documented initiatives brought together 10 communications service providers and technology participants to examine more connected agentic customer experiences.

The significance of AI customer service agents therefore lies in their ability to connect conversation with execution. A customer asking why a bill has changed, for example, could potentially receive an explanation based on current account information and have an authorised corrective action initiated without beginning a separate support workflow.

From Conversational Support to Task Completion

This shift also changes how customer-service performance should be measured. A chatbot can appear effective when it handles a large number of conversations, but conversation volume does not show whether the underlying customer problem was resolved. Agentic systems create the possibility of evaluating customer service through task completion, first-contact resolution, escalation rates, handling time and repeat contacts.

The technology is still developing, and the ability of an agent to take action depends on permissions, data access, system integration and human oversight. Not every interaction can or should be automated.

AI customer service agents are nevertheless establishing a new direction for telecom support by linking natural-language interaction with systems capable of retrieving information and executing approved actions. This moves customer service beyond the traditional chatbot model and toward a more action-oriented operating framework.

Key Takeaway: Telecom AI experimentation is moving beyond isolated conversational tasks toward connected agentic workflows that can interact across business systems and domains.

The transition is still at an emerging stage, but the direction is clear. AI customer service agents are beginning to connect customer conversations with the operational systems required to complete service tasks, creating a foundation for more context-aware, proactive and integrated customer care.

AI Agents are Connecting Customer Conversations with Telecom Systems

The ability of an AI agent to resolve a customer request depends on more than conversational intelligence. Telecom support involves information spread across billing platforms, customer relationship systems, order management, service assurance and network operations. Connecting an agent to these systems can allow it to move from understanding a request to gathering the information and completing the actions required to resolve it.

This is making AI customer service agents increasingly dependent on secure access to telecom data and operational systems. A customer interaction may require several separate checks before a resolution is possible, particularly when the issue involves both account information and network conditions.

AI Agents are Connecting Customer Conversations with Telecom Systems

A billing enquiry illustrates the difference. An agent may need to retrieve the current invoice, compare it with previous charges, identify a change in the customer’s plan or usage and determine whether the charge is valid. A conventional chatbot can explain billing policies, but an agent connected to the relevant systems can potentially work through the individual case and initiate an authorised action.

The same principle applies to technical support. A customer reporting poor connectivity may require checks against service status, known incidents, account information and device or network data. Bringing those sources together can reduce the need for customers to repeat information across separate support channels.

TM Forum’s agentic AI work is exploring this kind of system integration, with agents operating across business domains rather than remaining confined to a single conversational interface. This makes AI customer service agents increasingly dependent on APIs, tool access and clearly defined permissions.

Data Access is Becoming a Core Agent Capability

System integration also changes the importance of data quality. An agent can only make a reliable decision when the information it retrieves is current, relevant and presented in a form it can interpret correctly. Outdated account records, inconsistent service information or incomplete case histories can therefore affect the quality of the agent’s response or action.

This is particularly important when an agent is given permission to modify customer services. Access to billing or account information is different from authority to issue a credit, change a plan or cancel a service. Agentic customer care consequently requires controls that separate information access from action authority.

Human oversight can remain important for higher-risk decisions. An agent might gather the necessary information and prepare a recommended action while a human representative approves the final step. This allows automation to handle routine work without assuming that every customer-service decision should be fully autonomous.

The operational objective is therefore not simply to give AI access to more systems. It is to create controlled connections between the conversational layer and the systems that contain the information and tools needed to resolve requests.

Context is Turning Conversations into Workflows

As these integrations mature, customer interactions can increasingly become multi-step workflows. The agent can retrieve information, interpret it, call another system, confirm the result and continue until the request is completed or escalated.

This creates a more direct relationship between customer conversation and telecom operations. AI customer service agents can potentially reduce the number of separate steps required to resolve routine issues, while maintaining escalation paths where automation reaches the limits of its authority.

The broader development is therefore about integration rather than conversation alone. AI customer service agents are becoming a layer through which customers can interact with multiple telecom systems without needing to understand the underlying architecture. That shift creates the foundation for the more context-aware and proactive customer-service models explored elsewhere in this series.

Customer Service is Moving from Answers Toward Actions

Telecom customer care is increasingly moving beyond systems that simply provide information toward AI that can interpret requests, retrieve relevant data and execute authorised service workflows. The shift depends on integrating customer-service interfaces with billing, account, order and network systems.

This makes AI customer service agents increasingly relevant to the next stage of telecom support. Their value will depend on how reliably they can use current information, follow defined permissions and complete tasks without creating additional work for customers or human service teams.

As these capabilities mature, AI customer service agents could make routine support more direct by connecting customer conversations with the operational systems required for resolution. The transition is therefore less about replacing chatbots and more about turning automated customer interactions into increasingly capable, system-connected service workflows.

The post AI Agents Moving Telecom Customer Service Beyond Chatbots first appeared on Tele Info Today.

Digital Island and RingCentral Bring Agentic Voice AI to NZ

API TIT — Tue, 01 Sep 2026 13:24:18 +0000

Digital Island and RingCentral have partnered to bring Agentic Voice AI products to businesses in New Zealand. Under the agreement, Digital Island becomes RingCentral’s first partner in the country and will design, sell, implement and support the company’s unified communications, contact centre and customer engagement products for organisations across the New Zealand market.

The partnership connects a local cloud communications and IT services provider with an international communications software company that has been expanding through regional partnerships. Digital Island, a New Zealand-based provider focused on business collaboration and customer engagement services, will now offer RingCentral’s platform alongside its own local implementation and support capabilities.

New Zealand customers will gain access to RingCentral’s Agentic Voice AI offering through a provider that can manage deployment and support on the ground. The two companies are already working with early adopter customers ahead of wider availability later in 2026.

Agentic Voice AI Expands Through Local Partner

For Digital Island, the partnership adds an international software platform to its portfolio at a time when many businesses are reviewing their communications systems and customer service operations. The company will provide end-to-end services spanning initial design, implementation and ongoing support.

Leon Sheehan, Chief Executive Officer of Digital Island, said: “As RingCentral’s first partner in New Zealand, Digital Island is proud to bring Kiwi businesses access to AI-powered unified communications and contact centre solutions that modern organisations require. We are committed to helping businesses transform both customer and employee experiences with our New Zealand-based team of experts.”

RingCentral sells business telephony, messaging, video, contact centre and related software. Homayoun Razavi, Executive Vice President and General Manager of Global Strategic Providers at RingCentral, said: “We built our global strategy around partnering with local market leaders who understand what their customers need on the ground, and Digital Island is exactly that partner. Together, we’re giving New Zealand businesses a direct path to Agentic Voice AI, running on a cloud platform built for reliability, data privacy, and enterprise-grade security.”

RingCentral Services Target New Zealand Businesses

The partnership model reflects a common route to market for overseas software providers entering smaller national markets. Local sales, migration work and support are often critical to winning contracts in cloud communications, particularly for customers replacing older phone systems or moving contact centre operations to cloud-based platforms.

Krishna Baidya, Head of Connected Work and Customer Contact Research, Asia-Pacific, at Frost & Sullivan, pointed to the importance of local execution. “Moving off legacy communications takes more than software licences. It takes a local partner who can own strategy, deployment, and ongoing support, end to end. Pairing RingCentral’s cloud platform with Digital Island’s local service model gives New Zealand businesses a practical, lower-risk path to modernisation,” said Baidya.

The agreement arrives as competition in cloud communications and AI-assisted customer engagement continues to intensify across Asia-Pacific. Vendors are seeking local partners that can translate broad software offerings into sector-specific projects, while customers weigh the benefits of automation against integration costs and the risks of disrupting day-to-day service. Digital Island will now handle not only sales for RingCentral but also the deployment and support work that often determines whether a unified communications overhaul succeeds.

The post Digital Island and RingCentral Bring Agentic Voice AI to NZ first appeared on Tele Info Today.

UK Urges Operators to Strengthen Telecom Consumer Protection

API TIT — Fri, 05 Dec 2025 07:26:03 +0000

The UK government has called on major operators to strengthen their approach to telecom consumer protection, urging companies to ensure customers are treated fairly and shielded from price changes they did not agree to. Chancellor Rachel Reeves and Technology Secretary Liz Kendall have written to BT/EE, Virgin Media O2, VodafoneThree, Sky, and TalkTalk, asking them to confirm that any customer currently under contract will not face increases beyond the terms they originally signed. The ministers also asked operators to accelerate efforts to move legacy customers to clearer pounds-and-pence pricing, with no change to the timing of planned adjustments.

Reeves and Kendall said these steps form part of a broader push to make billing practices more transparent, especially for households and businesses that rely on mobile and broadband services daily. Their letter follows previous correspondence from Kendall to Ofcom earlier in the month, raising concerns about how pricing structures are communicated to consumers. The officials emphasized that telecom consumer protection must remain central to operators’ commitments as the industry manages widespread changes in tariffs and service offerings.

The government will convene a roundtable with senior industry leaders to discuss additional actions that could support telecoms customers. The session will also explore areas where the government can help the sector accelerate investment in the UK’s digital infrastructure. Officials believe this cooperation is essential to balancing consumer safeguards with the industry’s long-term development needs. The discussion is expected to build on continuing scrutiny of billing communication, contract clarity, and the treatment of long-standing customers.

Kendall said, “Mobile and broadband bills are an essential, everyday cost for millions of us across the country. But it is clear to me that companies need to do more to protect their consumers – loyal customers who rely on these services to run businesses and stay in touch with loved ones. When we meet them shortly, I expect company bosses to put forward clear plans to shield Brits from unexpected price rises and improve their customer communications. But we know this must be a collaborative effort. Working together, we want to support industry to invest in the infrastructure we all rely on and ensure even more people across the country can enjoy improved connectivity and access to digital services.” Her remarks underline the government’s expectation that telecom consumer protection should remain at the forefront of operator policy in the months ahead.

The post UK Urges Operators to Strengthen Telecom Consumer Protection first appeared on Tele Info Today.