With over a decade of experience at the forefront of mobile development, Anand Naidu has witnessed the evolution of voice technology from a clunky novelty into an indispensable user interface. He has guided teams across the finance, healthcare, and retail sectors in building applications that don’t just hear users but truly understand them. In our conversation, Anand unpacks the strategic shift toward “voice-first thinking,” exploring how improved accuracy and contextual awareness are fundamentally changing user expectations. He shares insights on navigating the complexities of privacy in personalized apps, the critical necessity of real-world testing, and the profound impact of voice on accessibility, revealing how designing for specific needs often creates a better experience for everyone.
You mentioned voice accuracy improved from around 70% to over 95%. Could you walk us through a specific project where this was key to boosting engagement and describe the challenges you overcame to achieve that reliability in a real-world setting, like a busy street?
Absolutely. I remember a project we worked on about four years ago for a major retail client. The goal was to allow users to add items to their shopping cart hands-free. The initial version was, frankly, a bit of a disaster. It was built on older tech, and its accuracy was hovering around that 70-75% mark in ideal conditions. We’d test it in the quiet office, and it would seem okay, but the moment a user tried it in a real store or walking down the street, it fell apart. The feedback was brutal; people would say something like “add bread and milk,” and the app would hear “red and silk.” It was more frustrating than helpful, and our usage analytics for the feature were abysmal. The breakthrough came when we rebuilt the feature using modern machine learning models and took advantage of the improved microphones in newer smartphones. The new system reached over 95% accuracy, but getting there in a noisy environment was the real challenge. We had to train the model to specifically filter out common urban sounds—sirens, traffic, other people talking. We spent weeks collecting and tagging audio samples from busy streets, train stations, and bustling cafes. It felt less like coding and more like being an audio engineer, fine-tuning the system to distinguish the user’s voice from the cacophony around them. The first time I successfully added three specific grocery items to my cart while a bus loudly hissed past me on the corner, I knew we had cracked it. That shift from a frustrating gimmick to a reliable tool led to a massive spike in engagement; the feature went from being ignored to being one of our most praised conveniences.
The article contrasts “voice-first thinking” with “bolting on” a feature. Can you outline the first few steps your team takes to design a voice-first user flow for a new app, and what common mistakes do developers make when they add voice as an afterthought?
“Voice-first thinking” starts long before a single line of code is written. The very first step for my team is never about the technology; it’s about identifying the user’s context. We ask: when would someone use this app without their hands or eyes? Are they driving, cooking, exercising, holding a child? This helps us pinpoint the specific tasks where voice provides a genuine advantage over tapping. For instance, in a recipe app, the most valuable voice command isn’t searching for a recipe—you do that when you have time to browse—it’s navigating the steps while your hands are covered in flour. So, our second step is to map that specific user journey as a conversation, not a series of screen taps. We literally write a script: “What would the user say? How should the app respond? What if they ask to repeat a step or ask for a substitute ingredient?” We design for interruptions and natural language, not rigid commands.
The most common mistake I see with the “bolted-on” approach is simply adding a microphone icon that transcribes speech into a search bar. It’s lazy and misses the entire point. The developer thinks they’ve “added voice,” but the user quickly learns it’s just a clumsy dictation tool. It doesn’t understand context. For example, in a banking app, a bolted-on feature might let you say “account balance,” but it won’t understand a follow-up like “and transfer two hundred dollars from there to savings.” A voice-first design anticipates that sequence. It treats the interaction as a single, fluid conversation, turning a process that used to take six or eight taps into one seamless verbal exchange. The mistake is treating voice as just another button instead of a fundamentally different way of interacting.
You gave an example of a finance app learning user habits. When implementing such personalization, what are the key steps for ensuring user privacy is protected from day one, and how do you communicate those safeguards within the app to build trust with users?
This is probably the most critical aspect of designing smart voice features, because trust is everything, especially in finance. The very first step, our “day zero” principle, is to design with a hybrid processing model. We decide which data absolutely must stay on the device and which needs cloud processing. Simple, routine commands like “check my balance” can often be handled locally, meaning the audio never leaves the user’s phone. This is not only faster but inherently more private. More complex requests that require natural language processing might need to go to a server, but even then, we anonymize the data and strip it of any personally identifiable information.
The second step is radical transparency. You can’t just hide these details in a 50-page privacy policy. We build communication directly into the user experience. For example, the first time the app offers a proactive suggestion—like “I see it’s the first of the month, would you like to pay your credit card bill?”—we include a small, easily tappable link that says “How did I know that?” This leads to a simple, one-screen explanation in plain English: “This app learns your usage patterns locally on your device to offer helpful shortcuts. Your personal voice and financial data are never shared.” The key is to be proactive and clear, framing it as a benefit while providing an immediate, easy way to manage that setting. We always give users granular control—a simple toggle in the settings to turn off learning, and a button to delete any stored pattern data. Building that trust isn’t a one-time thing; it’s about continuously reinforcing that the user is in control.
You stressed that lab testing fails for voice interfaces. Considering the development timeline, what specific real-world testing methods do you use to catch bugs related to accents or background noise, and how does this process differ from testing a typical touch interface?
Lab testing for voice is almost useless; it gives you a false sense of security. The real world is messy, loud, and unpredictable. So, our primary method is what we call “diversified field testing.” Early in the development cycle, long before a public beta, we recruit a small, carefully selected group of testers who represent a wide range of accents, speaking patterns, and ages. We don’t give them a script or bring them into an office. Instead, we give them the app and a list of tasks, then ask them to use it over the course of a normal week and record their screen and audio when they do. This is fundamentally different from touch interface testing, which is often about task completion rates in a controlled setting—can the user find the button? For voice, we’re testing for resilience. We want to see what happens when a user from Scotland tries to order a prescription refill while their TV is on, or when a fast-talking teenager tries to use it on a windy day. We get back these raw, authentic audio files, and that’s where we find the real bugs—the system failing to understand a specific dialect, or a common background noise like a blender completely derailing the command. This process takes more time than a lab session, but it saves us from launching a feature that breaks the moment it encounters the real world. It’s an investment that pays for itself by preventing the massive user frustration that comes from a feature that only works in perfect silence.
You shared an example of a voice-only app for elderly patients. Beyond simplifying commands, what specific design principles did you follow for this audience, and could you share an anecdote where a feature designed for accessibility became a popular convenience for all users?
For the healthcare app for elderly patients, our core design principle was “reassurance through feedback.” Many older users are not digital natives and can feel anxious about whether the technology is working correctly. So, we designed the app to be very conversational and confirmatory. After every command, the app would repeat the request back in a calm, clear voice before executing it. For example, if a user said, “Remind me to take my heart pill at 8 AM,” the app would respond, “Okay, setting a reminder for your heart medication, every day at 8 AM. Is that correct?” This verbal confirmation loop was vital for building confidence. We also designed the system to have a very high “error tolerance.” It didn’t require perfect phrasing. A user could say “next medicine time,” “when is my next dose,” or “what pill do I take now,” and the system was smart enough to understand the intent behind all of them.
A wonderful anecdote came from that exact feature. The spoken medication reminders were, of course, designed for our core audience of elderly patients. However, during a later, broader beta test, we noticed a surprising user group adopting it enthusiastically: young parents. They were using the feature to manage their children’s medication schedules, especially for things like antibiotics that have strict, multi-day timetables. One parent left feedback saying, “I just tell the app ‘remind me about Timmy’s medicine’ and it handles the rest. I don’t have to set a million different alarms on my phone while also trying to get him to eat his lunch.” It was a perfect example of how designing with empathy for a specific group with accessibility needs can create a feature that is so fundamentally convenient and well-designed that it becomes indispensable for everyone.
What is your forecast for voice interaction in apps beyond 2027? Do you see it merging with other technologies, and what is the biggest challenge the industry must solve to make voice truly seamless for everyone?
Looking toward the end of this decade and beyond, I don’t see voice as a standalone feature anymore. I see it becoming part of a multi-modal interface, seamlessly integrated with other technologies like augmented reality and ambient computing. Imagine looking at a piece of furniture in your living room through your phone’s camera and asking, “Would this fit in blue?” and seeing it change color in real-time. Or walking through a grocery store and asking your device, “Where are the gluten-free pastas?” and having the directions subtly overlaid on your vision. Voice will be the conversational thread that connects our physical and digital worlds, acting as the most natural way to command the complex systems around us. It won’t replace touch, but it will become the go-to interface for intent-driven actions.
However, the biggest challenge the industry must solve to make this vision a reality isn’t technological—the accuracy and processing power are already incredible. The real hurdle is overcoming our legacy of screen-based design thinking. We are still designing conversations that feel like navigating a visual menu. To make voice truly seamless, we must get much better at designing for ambiguity, context, and genuine human dialogue. The system needs to be able to understand not just what I said, but what I meant, based on my location, my past behavior, and the subtle tone of my voice. Solving this design challenge—creating experiences that are truly conversational rather than just command-driven—is the final frontier for making voice an invisible, effortless part of our daily lives for absolutely everyone.
