AI in Voice Services: Benefits and Best Practices for Integrating AI into the Voice Path

Mobile World Live’s 2025 Industry Survey found that 38% of surveyed mobile professionals are already investing in customer-facing GenAI apps, while 41% are planning to. 55% of respondents want to target customer service first for customer-facing GenAI. For many telcos, customer service is only the starting point for AI voice implementations. In fact, the bigger opportunity lies in leveraging the voice network itself and building a portfolio of value-added AI voice services – like live transcription and translation or voice assistants – to capture the B2B market.

However, many AI solutions that diversify voice services aren’t built to carrier-grade standards by default. Over-the-top (OTT) integrations that take call audio out of the network to a third-party service can add latency, make call quality depend on someone else’s platform and hand the business case to that third party. What’s the alternative? In this article, you’ll discover the benefits, a framework example and best practices for integrating AI into the voice path.

Adding AI into the voice path vs. over the top

OTT integration might seem initially faster to implement, offering less operational responsibility, limited commitment and lower upfront costs. However, the OTT provider then owns not only updates and maintenance but also the value-generating layer. This means that operators lose new revenue opportunities that come from owning AI-enhanced voice services. An in-path integration enables you to turn voice into an asset and differentiator that generates business growth.

Latency and real-time processing constraints

A pause that might be invisible in a chat interface becomes an audible issue on a live call. The further the AI service sits from the call, the worse potential delays get. When an OTT provider processes audio from a distant data center, round-trip time (RTT) can make transcription, translation or bot responses slow enough to notice. To protect call quality and customer satisfaction, keep media processing inside your network and place each AI engine where the use case allows – on-premises for the most latency-sensitive services or in the cloud where a short delay is acceptable.

Maintaining call quality and reliability at carrier scale

Consumer AI tools are built for best-effort availability. Voice networks are held to service-level agreements (SLAs) that typically target five nines (99.999%) of availability – roughly five minutes of downtime per year. When you route a call through an external AI service, its uptime and call quality are only as reliable as this third-party service. On the other hand, an AI capability that sits in the voice path is managed as part of an operator’s network infrastructure. With its high availability, latency budget enforcement, automatic rerouting to alternate AI engines and traffic prioritization, it meets the same carrier-grade standards as the rest of the voice network.

Turning the voice network into a platform for new services

Most calls still travel over operators’ voice networks, which puts operators in a unique position to bring AI into the call itself. Once AI sits in the voice path, every call can become the starting point for value-added services – from real-time and post-call transcription, noise reduction and interactive AI suggestions to live translation and, where regulation permits, AI support for emergency-service operators.

A voice-native AI layer connects to the existing SIP/IMS voice network rather than replacing any part of it, and the operator keeps ownership of both the call data and the service. With an OTT approach, every new service is only as flexible as the vendor’s roadmap.

Benefits of integrating AI into the voice path

New revenue opportunities. Owning the AI layer in voice lets operators monetize it directly instead of leaving that value to OTT players. On this architecture, operators can build premium add-ons for consumers, such as live call translation or call transcripts, and packaged AI services for business customers – from call summaries for SMBs to multilingual support and opt-in scam call protection. Because the operator controls where call data is processed, these services can also reach regulated sectors like financial services and healthcare.

Flexibility. Once the AI layer is in place, new services reach the market faster: they reuse the existing speech recognition, language models and text-to-speech pipeline instead of requiring a new integration project. Operators can pilot a service with a selected group of subscribers or business customers and roll it out more widely as demand grows. Over time, the same foundation can support capabilities such as sentiment analysis, voice bots and speech enhancement.

Improved customer experience. Low latency and carrier-grade reliability keep conversations natural, without noticeable delays or accidental talk-over. Subscribers can use services like live translation directly in a regular phone call, with no extra app. In customer support, AI voice bots can handle routine calls end to end and shorten queues, while live transcription and generated summaries help agents resolve the remaining issues faster

Lower operational costs. A single voice processing pipeline serves every AI service, so operators run one architecture instead of a separate integration per use case. Fewer components mean lower maintenance costs and fewer points of failure. AI processing capacity can also scale independently of core voice capacity, and each use case can run on the most cost-efficient AI engine – so AI processing costs grow with actual usage.

A practical example of an in-path AI voice framework

The AI voice framework that our team uses to help operators expand their voice service offerings is an example of how you can achieve carrier-grade AI in voice. It’s built on the same proven media engine that powers our other Amplitiv voice solutions for more than 10 operators across Europe (mostly Tier 1) – an engine we keep developing as market needs evolve.

Software Mind’s AI Voice Framework – Infrastructure

When a call connects a customer with a consultant, it’s routed through our Amplitiv AI Media Server, a customizable solution that bridges telco-grade media processing with AI capabilities. The AI layer – including an AI Gateway that combines live call audio with session metadata – launches relevant AI features, such as real-time transcription or a call summary. Call recordings and AI-generated content, such as transcripts and summaries, are stored securely within the operator’s environment, easily accessible for authorized users.

The framework protects operators from AI vendor lock-in as it supports pluggable AI services. This means it works with a wide range of AI tools, from cloud services such as Azure Speech, OpenAI real-time audio models or ElevenLabs’ ElevenAgents to self-hosted models and a client’s own AI solutions.

The framework supports a growing set of AI-driven capabilities, including:

• real-time and post-call transcription,
• call summaries,
• live translation,
• sentiment analysis and fraud detection,
• dynamic voice prompts (text-to-speech),
• voice bots,
• noise reduction and speech enhancement.

Best practices for implementing AI in the voice path

Prioritize in-path, SIP-native integration

An AI media server that speaks SIP and standard media protocols plugs into the voice network like any other network element. The application server simply routes selected calls through it – no changes to handsets, apps or the voice core are needed. It also keeps the data under your control, so a bank, for example, can connect its own AI model to your in-path infrastructure and handle sensitive customer information safely.

Choose carrier-grade infrastructure built for reliability and scale

Make sure your AI infrastructure meets telecom software standards and SLA levels. The media path should be held to the same reliability standards as the rest of your voice network – including redundancy, monitoring and capacity planning.

Add a dedicated AI media server alongside your voice core

In our conversations with operators, one pattern keeps coming up: they prefer not to build AI services on the general-purpose media servers already running in their voice core, such as the Media Resource Function (MRF) in an IMS network. Those nodes are dimensioned to serve base voice services for the entire subscriber base – at high volume and with maximum stability – and they lack the flexibility that fast-evolving value-added services need. Any change to them also has to be validated against the services they already carry.

A dedicated, AI-enabled media server added alongside the core is a simpler path. Base voice services and the core network stay untouched, while AI workloads run on an elastic, cost-effective component that scales with demand – without adding load to the core and even taking value-added media processing off it. The same component then becomes the foundation for further value-added services as AI technology and customer needs evolve.

Keep AI services pluggable to avoid vendor lock-in

AI models change fast, and the AI tools you implement today might not meet your and your customers’ needs in a year. If you’re looking for service flexibility, choose a solution that can connect to any AI service. Even when formats and query structures change, reconfiguration should remain straightforward – and much faster than re-establishing the entire integration. This gives your business the freedom to switch AI providers or run several AI tools in parallel to cover more use cases.

Match deployment to compliance and data-residency needs

A media server can run on-premises or in a private or public cloud environment, as long as the operator controls it. Depending on data-residency rules, sector-specific compliance and internal security policies, place workloads in the cloud where it’s permitted and efficient, and on-premises where data needs to stay in your environment.

Start with high-value use cases, then expand

When kicking off AI implementation in your voice service, aim for the greatest value at the lowest effort. Real-time transcription is a strong entry point: it enriches calls that already happen and needs relatively small changes. AI voice bots, on the other hand, are far more complex: they need dialogue design, integration with business systems and accurate domain knowledge to deliver a good customer experience. They are better added once the foundation is in place.

Building AI into voice services with confidence

Bringing AI into voice services is less about which model you choose and more about where you run it. The in-path approach protects call quality, ensures carrier-grade availability, lowers latency and keeps the revenue with you rather than a third party. The pragmatic path is to build on a pluggable infrastructure tailored to telecom standards, deploy it where compliance dictates and start with high-value use cases you can expand from. This way, you’re free to change AI providers, when needed.

If you want to explore how to effectively enhance your voice services with AI, get in touch with our experts.

FAQ

What does adding AI into the voice path entail?

This integration enables the processing of live call audio inside the operator’s own voice infrastructure – typically through an in-path media server – rather than routing the call out to an external, OTT service.

What’s the difference between in-path and OTT integration?

In-path integration improves latency and availability, while keeping call data within the operator’s environment and opening up new business opportunities. OTT integration might be quicker to deploy in some cases, but service reliability depends on a third party and the business case sits with the third party.

Which AI voice use case should operators start with?

Real-time transcription usually offers the best value for the least effort. More complex use cases such as voice bots, which require accurate domain knowledge to be reliable, are better added later.

How does in-path voice AI support regulated industries like banking?

Processing calls within the operator’s infrastructure enables AI services for highly regulated sectors. A bank’s own AI model can be integrated with the media server without exposing sensitive data to an outside network.

Why not run AI services on the existing MRF in the core network?

The MRF in the core network is dimensioned to deliver base voice services – such as announcements, tones and conferencing – efficiently for the entire subscriber base. It’s optimized for high volume and stability, not for AI integration or the frequent changes that value-added services require. A dedicated AI-enabled media server added alongside it lets operators build and evolve AI services without touching the core or adding load to it.