Senior Manager Sales Development

Putting ElevenLabs voice AI into production: an operator's playbook. Every vendor demo of a voice agent sounds flawless. The audio is clean, the model answers instantly, and nobody asks what happens on the four-hundredth call of the day - when a customer switches languages mid-sentence, the CRM lookup times out, and the request is one the script never anticipated. That gap, between a convincing demo and a system that survives a live operation, is where most enterprise voice AI quietly stalls. It is also the gap that a June 2026 partnership between TELUS Digital and ElevenLabs was assembled to close. The headline is a vendor deal; the useful part is the operating model underneath it - a template any company can read for how to take a voice agent from a deployment into something customers actually experience. "Deploying AI agents at enterprise scale is harder than it looks - the technology has to hold up in a live operation with real customers, real complexity, and no margin for a bad experience," said Ashish Uchil, Head of Business Development and Partnerships at ElevenLabs. That sentence, not the partnership, is the thing to internalise. The model: platform, implementation, and the layer in between. The arrangement separates three jobs that companies often wrongly treat as one. Enterprises contract with ElevenLabs directly for ElevenAgents, its AI voice agent platform. A separate implementation partner - here, TELUS Digital - owns deployment, integration, governance and the ongoing operations that keep the thing running after launch. The platform connects to the systems a contact centre already lives in: Genesys, Twilio, Amazon Connect, Zendesk and Salesforce. That separation matters because the platform is rarely what fails. The model can sound natural and still produce a terrible experience if it cannot reach the right record, escalate cleanly, or be supervised in production. The hard, unglamorous work sits in the middle layer - solution architecture, conversation and persona design, systems integration, monitoring, and a responsible-AI review before anyone talks to a customer. It is the same lesson behind why so many enterprise AI programs stall: the demo clears in a week, and then the integration and governance work that no one budgeted for takes the next two quarters. What makes the middle layer credible is operator experience, not slideware. TELUS Digital frames its edge as running customer operations at scale itself, with more than 900 AI engineers and a "forward-deployed" model that embeds those engineers inside client operations to build and refine close to the real work. Whether or not a company uses that particular partner, the structural takeaway holds: buy the platform, but rent - or build - the operating capability to run it. A voice agent that has never been pressure-tested against a real queue, real edge cases, and a real compliance team is a prototype wearing a production badge. What a voice agent actually does on the front line. Framed honestly, a voice agent is capacity. It handles high-volume, routine interactions, and routes complex or sensitive ones to human teams - who, in return, receive better-qualified work. ElevenAgents produces speech across 70+ languages at low latency, and the point is not the voice quality but what happens around it: the agent listens, takes action mid-conversation against back-end systems - updating an account, booking a follow-up - and grounds its answers in the company's own data rather than a generic model's guess. The robustness details are where the demo-to-production gap usually shows. When a customer switches languages mid-call, the agent is meant to follow; when they interrupt or hesitate, the conversation is supposed to keep moving rather than collapse into a re-prompt loop. Those behaviours are cheap to claim and expensive to verify, which is exactly why they belong in a structured evaluation against your own traffic before launch - not in a procurement deck. It can also reach out, not just respond. Proactive contact at the moment of onboarding, before a customer hits a problem, turns out to be one of the higher-value patterns. Designed this way, voice AI is not a replacement for AI customer-support agents staffed by people - it is the thing that lets those people spend their hours on the conversations that genuinely need judgment. The evidence companies should actually weight. Two internal proof points are worth more than the marketing copy, because they are measured outcomes inside a demanding operation rather than projections. The first is training: TELUS Digital runs ElevenLabs inside its own Fuel iX Agent Trainer to generate lifelike voice and chat simulations, letting new hires rehearse everything from routine questions to difficult complaints before taking live calls. After more than 90,000 simulations as of June 2026, onboarding time fell by roughly 20%, with early signs of lower agent turnover. The second is a customer-facing proof-of-concept at TELUS Communications. A voice agent proactively called newly activated home-internet customers during their first 90 days - confirming setup, walking them through their first bill, and answering early questions before they turned into support calls. Account changes, troubleshooting and any request for a person stayed with human agents. Customers who received a welcome call were less than half as likely to cancel within their first 30 days as the average new customer, and rated the calls 8.5 out of 10. That mirrors what Axccelerate Pte. Ltd. saw when Definity rebuilt its contact-centre workflow around AI: the gains come from disciplined scope, not from handing the agent everything at once. Designing for trust before the first call. The TELUS Communications pilot is most instructive for what it refused to automate. Transparency was built into the call flow: the agent identified itself as an AI at the start and before any account detail was discussed, and the call simply ended if a customer declined to continue. Sensitive work was kept on the human side by design. That is the right default, and it generalises. Responsible voice AI means clear disclosure, strict limits on what data the agent can touch, verifying details against live records instead of assuming them, and a full governance and privacy review before launch. The platform layer supports it - ElevenAgents ships with SOC 2, HIPAA and GDPR compliance, plus EU data residency and zero-retention modes for stricter requirements - but compliance features are necessary, not sufficient. Wiring them into a supervised, auditable workflow is an AI automation and infrastructure problem, and in regulated sectors it is the precondition for going live at all, not a finishing touch. Where to start. The use-case map is clearer than the hype suggests. The low-risk, high-return entry points are routine servicing, proactive onboarding, and status-check interactions - high volume, well-bounded, and forgiving. The high-risk zone is sensitive advice, complaint handling, and anything that could create an unsuitable or unrecorded customer communication. Start in the first zone, instrument everything, and expand only once the escalation rules and audit trails have held up under real traffic. The sectors where this pays first are the ones with high-volume, repeatable customer engagement - telecommunications, financial services, utilities and retail - which is precisely where the TELUS Digital and ElevenLabs go-to-market is aimed. The common thread is not the industry but the shape of the work: enough routine contact that containment and proactive outreach move real numbers, and enough regulatory weight that the governance has to be right. Pick the first use case where those two pressures meet, measure containment, resolution and escalation rates honestly, and let the data - not the roadmap - decide what gets automated next. For scale context, ElevenAgents reports 4.8 million agents live and 1.3 million conversations a day across enterprises and governments in 80 countries. The technology is past the question of whether it works. What separates a result from a cautionary tale is the operating discipline around it: the integration, the governance, the human boundary, and the patience to prove each use case before widening it. Voice AI is not a model you buy. It is an operation you run - and that, not the demo, is what companies should be evaluating. Related services How Axccelerate Pte. Ltd. work in this space. More articles.

Accentism for profit? What Telus is getting wrong about accents. by Dr. Molly Babel and Amanda Cardoso, department of linguistics May 20, 2026 Telus Digital, the global technology and digital services arm responsible for the telecommunication giant's call centres, has recently deployed an "accent masking" artificial intelligence tool to change the way its offshore agents sound. The technology analyzes agents' pronunciation in real time and reshapes their accents to more closely resemble generalized North American or British English speech patterns. Linguistic profiling or accentism - stereotyping, treating someone unfairly or viewing them negatively based on their accent - can permeate aspects of its society in ways that have real-life consequences. They can affect hiring decisions, outcomes of legal proceedings, assumptions about the crimes one might commit, assessments in education and access to housing. Telus appears to be taking advantage of incredible technological advances in signal processing and resynthesis to reproduce an ugly aspect of human behaviour. As language scholars UBC believe it is demeaning, manipulative and wrong. And, if UBC swap in nearly any other social characteristic, it would be discrimination. In order to see racism, UBC need to see race. So imagine a filter on glasses that changes people's skin tone, for example, homogenizing its ethnic and racial identities. There are several strands of evidence that indicate the accentism Telus is practising is not in the best interest of the call centre agents or Canadian consumers. Some accents are harder. Its discriminatory tastes in speech start early. Children prefer playmates who share their accent, even in linguistically rich cities like Toronto. The credibility and trustworthiness adults attribute to individuals and voice-AI assistants vary with the accent. Telus says it's implementing an accent manipulation AI tool because some Telus customers have expressed difficulty in understanding "heavy foreign accents." This is a leap in logic. Researchers have known for decades that the perception of accent strength is not well-correlated with how well that voice can be understood. In the case of call centres in India or the Philippines, call agents may be first-language speakers of English, albeit a different variety than Canadian English. As of yet, there is no public indication that this tech is being used on agents who speak English with a French Québécois accent or a Newfoundland one or a Cape Briton lilt. It's also worth noting that everyone has an accent; unaccented speech is a myth. An accent is simply a way of speaking that is distinctive to a specific group. If this is genuinely about comprehension, then why would it apply to some accents and not others? In any instance where UBC experience difficulty in understanding someone, it is always beneficial to pause and reflect on whether it is a "them problem" or a "me problem." If there are others who find an accent comprehensible, it is most likely a "me problem." The cost of accent manipulation. Any human interaction is a two-way street, and the success of that communication comes, in large part, from an implicit collaborative building of understanding - an establishment of a common ground. When Canadian customers are being duped about who they are talking to - even when this duping is, according to Telus, "to bridge communication gaps and deliver crystal-clear voice experiences" - a cornerstone of that communicative collaboration is removed, leaving an increased opportunity for misunderstanding. Call centres often already have policies about "regulating identity" of their agents, including strict policies around accents, requirements that agents change their names to something more western-sounding or requirements that agents go through accent modification training. These requirements can lead to workplace anxiety and stress, affecting the quality of the interaction between the consumer and agent. Interestingly, call centre agents have already observed that if customers identify the accent as being fake or not genuine - when words and structures being used don't match the accent - customer relations worsen, and customers become abusive. What this means is that this "accent softening," when identified as fake or if leading a customer to assume they are not speaking to a real person, may lead to more frustration by customers and worse treatment of call centre agents. Exposure to diverse accents. Some might argue that it's natural to have an easier time understanding someone whose accent is closely aligned with your own. This is true, but this benefit does not come on its own; it is a function of having more experience with an accent. Think about it this way. There are probably people in your life that you find very easy to understand, like a spouse, family member or close friend. Indeed, that immense familiarity you have with a spouse's voice makes it both easier to attend to and ignore your loved one. It doesn't matter if their accent is the same as yours; it's the experience that matters. Experience renders both voices and accents more comprehensible. Diverse listening experiences can also make UBC better listeners, facilitating understanding of a wider range of accents. This is to say, as a listener, you stand to gain from exposing yourself to new voices and accents. AI technology that can modify specific features of an acoustic speech signal while preserving the speaker's individual identity is cool science. AI can be an incredibly useful tool, but also comes with a human responsibility. Responsible and human-centric approaches to AI should seek to limit harm. In this case, the "accent softening" of Telus call centre agents is discriminatory to the agents. It's also a morally dubious misrepresentation of identity to Canadian consumers that disrupts the natural and productive friction that comes with human interaction. Molly Babel receives research funding from the Natural Sciences and Engineering Council of Canada (NSERC) and the Social Sciences and Humanities Research Council of Canada (SSHRC). Amanda Cardoso receives research funding from the Social Sciences and Humanities Research Council of Canada (SSHRC). Contact. UBC Media Relations [email protected]