Apple’s Siri has been around, believe it or not, since 2011. Almost 15 years, hard to believe. But it wasn’t until the advance of AI in the last few years for me to finally claim that voice can and is (finally) good enough to be the interface of the future. Voice technology used to live in consumer assistants. Now, it’s moving into enterprise systems. It has become a foundational layer for interacting with software, data, and workflows. As of 2024, over 8.4 billion voice assistant devices are in use worldwide, surpassing the global population, and usage is projected to rise further by 2025, driven by the widespread adoption of voice AI. 8.4 billion voice assistant devices Platforms like Deepgram and Nuance are delivering real-time, human-like responses. The infrastructure is catching up to the ambition. What matters now is whether your systems are ready to support this shift. Most Systems Aren’t Built for Voice Most Systems Aren’t Built for Voice Many companies still design around screens and clicks. Voice introduces different constraints. Real-time input needs asynchronous processing. Conversations don’t follow menus. APIs need to handle variation, not just precision. Latency is another issue. If a voice interface lags, the entire experience collapses, and voice data adds a layer of sensitivity that most systems aren’t built to handle. Examples of Voice Delivering Real Value Examples of Voice Delivering Real Value Companies that are integrating voice successfully are doing a few things differently. And when they get it right, Voice AI delivers the strongest returns when embedded directly into core workflows. The impact often appears in operational metrics that don’t trend in headlines, yet they drive customer retention, team productivity, and compliance confidence. Banking and Finance: Garanti Bank’s MIA reduced customer service calls by 20 percent and improved client retention. The operational gain comes from automating secure transactions and fraud alerts without increasing staff load. This integration works because it fits into the customer interaction flow rather than creating an extra process to manage. Banking and Finance: Garanti Bank’s MIA Automotive: In-car voice AI helps drivers stay focused. 76% of US drivers already use voice for hands-free tasks. Voice commerce could reach $35 billion annually, with the strongest results coming from designs that are safe, quick, and reliable every time. Automotive: 76% of US drivers Retail and Logistics: Voice-directed warehouse systems have increased productivity by up to 35% and improved picking accuracy to 85%. In retail, adding voice capabilities to apps has helped reduce cart abandonment, addressing a consistent point of revenue loss. These gains increase further when paired with better inventory tracking and order accuracy. Retail and Logistics: Voice-directed warehouse systems Healthcare: Voice tools reduce the time physicians spend on administrative work, improve patient engagement, and automate screenings. Hospitals also use them to strengthen compliance processes. These improvements, while incremental, add up to significant efficiency over time. Healthcare: Voice tools Customer Service and Training: Wyze reached an 88% self-resolution rate by integrating voice AI into its support workflow. Education and training programs now use voice coaching to improve learning outcomes, shifting from static scripts to adaptive, real-time feedback that scales. Customer Service and Training: Wyze reached an 88% A Framework for Building With Voice A Framework for Building With Voice The key is to start with systems architecture. Voice-first design assumes real-time workflows. That requires APIs that don’t choke under latency and fallback logic that doesn’t rely on human intervention. Event-driven systems and asynchronous handlers become the baseline. From there, the best teams are selective about where voice fits. They target use cases where speed and context make a difference, such as operations triage, internal reporting, or compliance checks, and skip the low-value plays like homepage chatbots or branded voice gimmicks. Training is another early investment. Many product and engineering teams have never touched voice UX, and the learning curve can be steep. Skipping this step only slows results later. Finally, measurement is intentional. The goal is to cut resolution times, reduce support load, or shorten task cycles. Where It’s All Headed Where It’s All Headed Voice won’t remain isolated. It will merge with gesture, vision, and location data. These inputs will converge to form adaptive, proactive systems. This trend is already shaping investment priorities. Conversational AI continues to be a fast-growing area of enterprise investment, with organizations rapidly integrating voice agents into workflows and customer operations for 2025, according to McKinsey. according to McKinsey Voice will sit at the center of that growth. If your systems are still optimized for keyboard input, you are building around the future instead of into it. Voice is already viable. When implemented well, it creates real operational leverage. . . . . . . Nick Talwar is a CTO, ex-Microsoft, and a hands-on AI engineer who supports executives in navigating AI adoption. He shares insights on AI-first strategies to drive bottom-line impact. → Follow him on LinkedIn to catch his latest thoughts. Follow him on LinkedIn → Subscribe to his free Substack for in-depth articles delivered straight to your inbox. Subscribe to his free Substack

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