AI in Customer Support
AI agents and assistants for customer service, contact centers, help desks, ticket routing, knowledge bases, and customer experience operations.
LOT Turns to ElevenLabs for Multilingual AI Passenger Support
LOT Polish Airlines chief executive Michał Fijoł used an ElevenLabs summit in Warsaw to announce a collaboration that will bring ElevenAgents into the airline’s passenger support. His argument was that customer communication has become an operational challenge for LOT: nearly 200 IT systems, flights across dozens of markets, and routine passenger questions arriving in multiple languages and time zones. Fijoł positioned AI voice support not as a replacement for airline staff, but as a way to handle language, timing, and information access at a scale a Warsaw-centered contact model cannot easily cover.
TELUS Digital Cuts Contact-Center Onboarding Time 20% With AI Voice Simulations
TELUS Digital’s vice president of product, Mitch Lieberman, presents the company’s Agent Trainer as a response to a high-volume contact-center onboarding problem: 70,000 associates, 20,000 to 30,000 hires a year, and industry churn of 30% to 50%. Built on ElevenAgents, the voice and chat simulation platform is intended to get new agents ready for customer interactions faster, with TELUS Digital reporting a 20% reduction in time to proficiency, more than 50,000 completed simulations, and early signs of lower churn.
ElevenLabs Unveils Dubbing v2 and Previews More Controllable Eleven v4
ElevenLabs co-founder Mati Staniszewski used a Warsaw summit keynote to argue that AI’s next constraint is not intelligence but communication people can trust. He presented two new models — Dubbing v2, designed to preserve an original performance across languages, and a preview of Eleven v4, aimed at finer control over speech, emotion, accent, whispering and song — as evidence of that thesis. The broader case was that voice AI becomes commercially useful only when models are tied to agents, integrations, authentication, memory and deployment systems that let companies put spoken interfaces into production.
AI Voice Agents Are Beating the Average Customer-Service Rep
Tom Chen, chief product officer at Aircall, argues that AI voice agents should be judged against the average customer-service interaction, not the best human rep. In his account, the technology is already good enough for many routine calls, can handle far more concurrency at lower cost, and may improve satisfaction when customers are given a clear choice between faster AI service and a human agent. The main constraint, Chen says, is often not the model but the undocumented company knowledge the agent needs to resolve issues.
Alphabet’s $80 Billion Raise Shows Public Markets Regaining AI Power
John Coogan used Diet TBPN’s discussion of Alphabet’s reported $80 billion equity raise to argue that AI has made access to public-market capital strategically important again. Coogan, with Jordi Hays, framed the same pressure across OpenAI’s gigawatt data-center plans, confidential IPO filings and other market moves: AI companies are no longer just competing on products and models, but on their ability to finance infrastructure, absorb risk and time their access to public investors.
Travelers Deploys AI Claims Assistant Nationwide After Eight-State Pilot
Travelers’ claims CIO Erik Roen argues that putting an AI assistant into first notice of loss required changing the operating model around claims, not just adding a model to a call flow. In a conversation with OpenAI chief revenue officer Denise Dresser, Roen says the insurer moved from an eight-state pilot to countrywide deployment by pairing OpenAI’s technology with cross-functional business ownership, continuous evaluations, near-real-time monitoring and fail-safes for a workflow that helps customers decide whether and how to file a claim.
Giga Says Product Velocity Beat a 400-Person Rival at DoorDash
Giga co-founder Varun Vummadi argues that enterprise AI companies win less by selling a vision than by proving, in paid deployments, that their product can move a customer’s operating metrics. In a Startup School India interview with YC general partner Ankit Gupta, Vummadi traces how Giga abandoned its original edtech idea, followed customer demand into support automation, and used a small engineering team to win accounts including DoorDash. His broader case is that AI startups should charge early, iterate against real business KPIs, and treat product performance as their strongest sales tool.
Voice Will Become the Default Interface for Enterprise AI
Luiz Domingos, chief technology officer of Mitel, argues that enterprise AI has moved past pilots and into communications workflows where latency, compliance, auditability and human oversight determine whether systems can be deployed. In a conversation with Craig Smith, Domingos says cloud-only AI will not meet the needs of real-time voice and regulated industries, and that edge and hybrid deployments will become central. His larger prediction is that enterprise AI will increasingly be accessed by voice rather than screens, especially for frontline workers whose jobs do not fit a desktop interface.
Enterprise AI Agents Need Sandboxed Runtimes and Deny-By-Default Governance
In a ServiceNow-sponsored interview, ServiceNow AI engineering executive Joe Davis and Nvidia agentic AI product chief Adel Hallak argue that enterprise AI agents should be built as governed systems, not as single models with broad autonomy. They describe agents as layered architectures of models, harnesses, tools, sandboxed runtimes, permissions and control towers, with default-deny access replacing trust in the model’s judgment. Davis points to ServiceNow’s internal automation of 90% of some IT support requests as the practical proof point; Hallak frames Nvidia’s OpenShell and model stack as infrastructure for making that kind of autonomy enforceable.
ServiceNow Says Agentic AI Lifted HR Capacity and Automated Support Work
ServiceNow executives Jacqui Canney and Kellie Romack argue that agentic AI is already changing workplace operations by creating measurable capacity rather than simply replacing jobs. In a ServiceNow-sponsored interview, they point to the company’s internal deployments — including faster commission answers, autonomous IT service-desk resolution, and large-scale support automation — as evidence that AI’s value depends on redesigning workflows, tracking the capacity created, and redeploying employees into higher-value work. Their case is that managers now have to govern both people and agents, with visibility, skills assessment, and explicit choices about what work should be automated.
GPT-Realtime-2 Turns Voice Agents Into Tool-Using Reasoning Systems
OpenAI’s Build Hour on GPT-Realtime-2 presented the new realtime voice release as a shift from conversational voice interfaces toward tool-using, stateful agents. Teri Yu and Erika Kettleson argued that GPT-realtime-2’s larger context window, stronger instruction following, parallel tool calling and controllable speech behavior let developers build voice systems that can operate apps, reason across workflows and know when not to speak. Sierra’s Ken Murphy and Soham Ray added that production voice agents still depend on the surrounding system: guardrails, tuned turn-taking, tracing, redaction, evaluations and customer-specific workflows.
Agent Workflows Route Conversations Through Specialized Subagents
ElevenLabs is introducing Workflows, a visual editor for its Agents Platform that lets builders design routed conversation flows instead of placing all business logic inside one agent prompt. The company argues that specialized subagents, each with their own instructions, tools, knowledge bases and model choices, give teams more control over cost, latency and accuracy. The product is positioned as a way to combine AI interpretation with predefined actions, verification steps and human handoffs on the same design surface.
ElevenLabs Voice Engine Wraps Existing Chat Agents Without Rebuilding Them
Luke Harries of ElevenLabs argues that the next step for chat agents is not a new orchestration stack but a voice layer around the agents companies have already built. His case for ElevenLabs’ Voice Engine is that teams can keep their existing LLM logic, RAG, tools and business rules, while offloading speech-to-text, text-to-speech, turn-taking and interruption handling to a wrapper. The product is positioned for companies that want voice interfaces across web, phone and meeting channels without rebuilding their chat agents inside a fully managed platform.