The App of the Future Is Voice — Not a Screen. Mitel's CTO Luiz Domingos Explains Why.
Episode
54 min
Read time
2 min
Topics
Artificial Intelligence, Software Development, Product & Tech Trends
AI-Generated Summary
Key Takeaways
- ✓Edge AI for Latency: Deploy AI inference at local data centers or on-premises rather than public cloud for real-time voice applications. In contact center environments, a two-second delay from cloud round-trips makes AI assistance unusable during live calls. Edge deployment eliminates network dependency and keeps sensitive conversation data within enterprise boundaries, addressing both performance and sovereignty requirements simultaneously.
- ✓RAG Over Generic AI: Enterprises should implement retrieval-augmented generation rather than plain generative AI to extract real business value. Generic models lack enterprise-specific context, making outputs too broad to be actionable. Connecting language models to internal knowledge bases, product documentation, and workflow data produces measurable ROI — Mitel applies this to support routing, agent assistance, and intelligent call handling across 70 million users.
- ✓Agentic Governance Before Deployment: Before deploying agentic AI workflows, enterprises must define accountability frameworks specifying where human-in-the-loop approval is required. CIOs at industry events are prioritizing governance and auditability over capability features. Agentic systems that autonomously create tickets, update CRMs, or trigger workflows carry reputational and legal liability if unchecked — enterprises need explicit decision-point documentation and audit trails built into architecture.
- ✓Modernization Prerequisite: Organizations cannot layer AI onto legacy fragmented architectures and expect transformation. Effective AI adoption requires becoming API-first, decoupling the communications layer from the workflow layer, building modular AI services for data ingestion and orchestration, and establishing clean data pipelines with clear governance — gradual hybrid integration outperforms full system replacement while avoiding structural bottlenecks that cap AI value extraction.
- ✓Voice as the Default Interface: Enterprise applications will shift from screen-based GUIs to voice-first interactions within the near-term horizon. Frontline workers — nurses, field technicians — represent the most underserved segment of digital transformation and benefit most from voice AI that eliminates screen dependency. Transcription and summarization, currently premium features, will commoditize into standard subscriptions the same way call recording transitioned from paid add-on to baseline capability.
What It Covers
Mitel CTO Luiz Domingos outlines how enterprise communications is being transformed by AI across contact centers, unified communications, and agentic workflows, arguing that voice will replace traditional screen-based interfaces and that hybrid edge architectures are becoming essential for regulated industries managing latency and data sovereignty.
Key Questions Answered
- •Edge AI for Latency: Deploy AI inference at local data centers or on-premises rather than public cloud for real-time voice applications. In contact center environments, a two-second delay from cloud round-trips makes AI assistance unusable during live calls. Edge deployment eliminates network dependency and keeps sensitive conversation data within enterprise boundaries, addressing both performance and sovereignty requirements simultaneously.
- •RAG Over Generic AI: Enterprises should implement retrieval-augmented generation rather than plain generative AI to extract real business value. Generic models lack enterprise-specific context, making outputs too broad to be actionable. Connecting language models to internal knowledge bases, product documentation, and workflow data produces measurable ROI — Mitel applies this to support routing, agent assistance, and intelligent call handling across 70 million users.
- •Agentic Governance Before Deployment: Before deploying agentic AI workflows, enterprises must define accountability frameworks specifying where human-in-the-loop approval is required. CIOs at industry events are prioritizing governance and auditability over capability features. Agentic systems that autonomously create tickets, update CRMs, or trigger workflows carry reputational and legal liability if unchecked — enterprises need explicit decision-point documentation and audit trails built into architecture.
- •Modernization Prerequisite: Organizations cannot layer AI onto legacy fragmented architectures and expect transformation. Effective AI adoption requires becoming API-first, decoupling the communications layer from the workflow layer, building modular AI services for data ingestion and orchestration, and establishing clean data pipelines with clear governance — gradual hybrid integration outperforms full system replacement while avoiding structural bottlenecks that cap AI value extraction.
- •Voice as the Default Interface: Enterprise applications will shift from screen-based GUIs to voice-first interactions within the near-term horizon. Frontline workers — nurses, field technicians — represent the most underserved segment of digital transformation and benefit most from voice AI that eliminates screen dependency. Transcription and summarization, currently premium features, will commoditize into standard subscriptions the same way call recording transitioned from paid add-on to baseline capability.
Notable Moment
Domingos noted that at a recent industry conference, the dominant conversation among CIOs was not about AI agent capabilities at all — it was entirely focused on governance, liability, and auditability. Enterprises are deploying agentic systems without clear answers on who bears responsibility when automated decisions cause harm.
Episode Transcript
A lot of organizations want AI driven automation without properly updating their systems. What modernization do organizations need to undertake to adopt AI smoothly and avoid bottlenecks? What's changed most in how organizations are actually using AI today versus how they talked about it two or three years ago? Since 2022, everybody's now really way more active active in the day to day of AI solutions. Voice is becoming now again the natural interface for AI. You're gonna be talking to AI. AI will be your friend. Enterprise are really freaking out about how I manage accountability and what's the governance in the enterprise. How you address the responsibility at the end of the day when something goes wrong. Okay. Well, I usually, start by having you introduce yourself. Okay. Give us as much of your background as is relevant. Mattel is a Canadian telecommunications company as far as I know. So you can talk about how you, got to Mattel and and what you guys are doing. So why don't you go ahead and, introduce yourself, and, and then we'll, we'll start asking questions. Certainly. Thanks for having me, Craig. So my name is Luis Domingos. I am Mitel's CTO. I am in the business of enterprise communication and collaboration solutions for quite some time. That's my whole career in general. And, I'm with Mitel for two and a half years now. Mitel acquired a company called Unify, also another big vendor in the enterprise communication markets two and a half years ago. So I'm with Mitel for this period of time, and I was previously CTO for for the previous company for Unify. I am in this business for more than twenty five years, and, I've been following the the whole evolution of communications from the old times of IP telephony through cloud and now with AI, of course, AI communications as part of it. So my whole career is here for sure. Yeah. And and Mitel I'm sorry I was pronouncing it wrong. Mitel builds on prem or cloud and hybrid platforms that integrate voice, video, messaging, and contact center capabilities. So employees and customers are using a cohesive system. But how has AI impacted that, industry? Yes. Mitel is really broadly providing solutions for communication and collaborations. So we have we are more than fifty years in the market. We have a very large installed base, 70,000,000 users. And as as you said, communication, collaborations, contact centers, virtual applications. We are not new with AI. So correct? So we started AI at Mitel in 02/1718 with the first investigations about natural language process. We brought dialogue flow solutions for contact centers. But certainly, we are evolving along with AI. And since 2022, everybody's now really way more active in the day to day of AI solutions. And we've been applying AI to communications. And communications is a segment of the industry where AI can be easily justified to the business. Correct? You can gain significant value …
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