It starts simply. You pick up your phone and talk to it — not in keywords, not in fragments, but in full, natural sentences. You describe your grocery list, refine choices, ask follow-up questions, and complete a purchase. All without typing a single word.
What once felt like a novelty is quickly becoming a shift in how users interact with technology. VoiceAI is the next step in the technology’s operational ubiquity.
At its core, a convergence of artificial intelligence and voice recognition, Voice AI’s evolution lies in what it enables.
Today’s systems detect speech and beyond. They interpret natural language, understand context, and respond in real time. The resulting interaction then is fluid, contextual, and increasingly human.
This shift moves Voice AI beyond basic commands into something far more powerful: systems that can carry conversations, interpret intent, and take action. In doing so, they begin to automate entire user journeys —while also making technology more accessible.Why Voice, Why Now
The momentum behind Voice AI is not incidental. It is being driven by user behavior.
A study by Insider Intelligence (eMarketer Forecast) projects that by 2027, GenZ will become the largest group of voice assistant users, with 64% of the US Gen Z population using voice assistants monthly, up from 51% in 2023.
In India, the shift may be even more pronounced. The Arkam Ventures AI Report predicts that the first consumer AI application to reach 200 million users in the country will likely be voice-led, rather than English text-based. This reflects on both – the linguistic diversity and a mobile-first user base.
Voice, in this context, is steadily becoming the primary interface.Voice AI in Action: Meesho’s Bet on Conversational Commerce
Firms are gearing up to take advantage of this moment in AI evolution. Take for instance, the e-commerce marketplace, Meesho. At a swanky hotel in Bangalore, last week, Meesho’s co-founder and CTO Sanjeev Kumar turned up in a company tshirt to launch “Vaani” – Gen-AI enabled, conversational voice shopping assistant.
Modelled on the inherent conversational tone of real life shopping experience, the assistant allows users to speak in their own words, ask follow-up questions, and refine their choices through an ongoing conversation. It spans the entire shopping journey — from discovery to purchase — understanding intent, surfacing relevant products, and guiding users through decisions with contextual inputs like reviews and product details.
Under the hood, the system is built for scale. It uses edge computing for speech understanding and synthesis, ensuring low latency and cost efficiency. A multi-agent architecture enables it to handle complex, multi-step interactions, while fine-tuned models trained on regional language nuances improve accuracy and contextual understanding.
The system is also multimodal — understanding both what users say and what they see — creating a more integrated experience.
Meesho states early signals as such:
- 79% of users say voice simplifies shopping
- 94% find it intuitive
- 62% already trust it for transactions
Within the first month, over 1.5 million users interacted with the assistant. More importantly, engagement is repeat-led, indicating early habit formation. This is translating into business impact, with users seeing a 22% higher conversion rate.
As Kumar puts it, “Over the years, we have embedded AI and ML across every part of our marketplace, from discovery and pricing to logistics, trust and seller growth. Vaani is a natural extension of this journey. It brings together conversational AI with Meesho’s commerce intelligence to support users from discovery to purchase.”
Building for India: Swiggy’s Multilingual Push
If Meesho is rethinking how users shop, Swiggy is rethinking how they access commerce altogether.
Through its partnership with Sarvam, a full stack sovereign AI platform, Swiggy is enabling multilingual, voice-led commerce across food delivery, Instamart, and Dineout.
India has many languages, but most digital platforms still work mainly in English or just a few regional languages. This means a large number of users are left out.
Swiggy’s partnership with Sarvam aims to fix this by making commerce more language-friendly in two ways. First, it removes the need for an app. Users can place orders on Instamart through a simple phone call without downloading anything or even needing internet access.
Second, Swiggy is now available on AI-first platforms like Indus, Sarvam’s chat application. Here, users can order through conversation, with Razorpay enabling payments to complete the process. This enables end-to-end experience in a single conversation.
Sarvam’s voice models are trained on Indian languages, allowing natural and accurate interactions across 11 languages, including Hindi, Tamil, Telugu, Kannada, Bengali, and Marathi.
From Use Case to Infrastructure
As deployments scale, Voice AI is moving into its “operational era” as per Speechmatics’ The Voice AI reality check report.
It is already being applied across industries:
- Healthcare: ambient notes, faster triage, reduced burnout
- Media and Adtech: tone analysis, localisation, brand safety
- Customer Experience and Contact Centres: multilingual support with high uptime
- Public Sector: real-time emergency response and crisis communication
For enterprises, Voice AI has long moved past adding a new interface. It is now about rethinking interaction models at scale. IBM’s collaboration with ElevenLabs illustrates this shift.
By integrating advanced text-to-speech and speech-to-text capabilities into its WatsonX Orchestrate platform, IBM is enabling organizations to build voice-enabled agents that communicate with nuance, emotion, and clarity across 70 languages.
The focus here is on enterprise requirements of scalability across use cases, consistency in interactions – customer and employee alike; and security and compliance.
The applications are broad – from government services delivering multilingual citizen support to banks, insurance providers, and utilities enhancing customer engagement and internal operations.
Voice, in this context, becomes a critical layer in agentic AI workflows.
The Interface That Adapts
What ties these developments together is a fundamental shift in design philosophy.
For years, users adapted to technology by learning interfaces, navigating menus, and conforming to structured inputs. Voice AI flips the narrative and inverts that model.
It allows technology to adapt to users — their language, their context, their way of expressing intent.
As platforms like Meesho and Swiggy demonstrate, this is now more about expanding access —bringing new users into the digital ecosystem by lowering the barrier to interaction.
The next phase of AI may be defined by how naturally it can communicate.
