Voice AI Success: Latency Trumps Human Sound, Says Expert Danylov

The future of voice AI lies not in mimicking human speech perfectly, but in achieving response speeds that make interactions feel natural and instantaneous. This is the perspective of Vitaliy Danylov, a voice AI researcher and cross-disciplinary engineer, who argues that latency, rather than linguistic nuance, will drive the interface revolution.

The voice assistant market is experiencing significant growth, projected to expand from $3.54 billion in 2024 to $4.66 billion in 2025, with an estimated 8.4 billion voice assistant devices in use globally by 2025. Despite this expansion, voice technology remains underutilized in enterprise settings and business automation. Danylov, co-founder of a U.S.-based voice AI startup specializing in cross-border communication, believes this is poised to change. His background, combining financial analytics, political science, and computer science, offers a unique lens through which to assess the technology’s potential.

“People tolerate a robotic tone more than they tolerate a five-second delay,” Danylov notes. His diverse expertise provides a comprehensive understanding of business logic, human behavior, and technological feasibility, enabling him to discern genuine innovation from hype. He emphasizes that voice is at least three times faster than typing, and recent advancements in speech recognition have made it accurate enough to handle real-world noise and accents. This technological tipping point, he asserts, will lead to voice replacing text in many human-machine interactions, particularly as voice AI merges with the rise of AI-powered digital workers. What was once a simple chatbot is evolving into a sophisticated digital agent capable of listening, reasoning, and responding in natural speech.

From a financial perspective, the rationale for replacing human office workers with voice-enabled digital employees is compelling. White-collar roles often involve high salaries and bonuses, making their automation highly attractive for immediate return on investment. Businesses evaluate this using a straightforward equation: weighing the present value of expected gains (reduced expenses, increased revenue) against the predicted risk (cost and likelihood of failure). Digital employees are expected to first enter high-cost, low-variance, and low-risk office roles where the financial exposure from errors is minimal. For instance, a mistake in customer support might mildly frustrate a client, but an error in a legal consultation or vendor payment could lead to substantial financial or legal repercussions, altering the automation calculus.

Integrating voice interfaces into corporate environments is driven by their ability to either cut costs or increase revenue. Voice AI can augment or replace human agents in expensive regions, offer 24/7 support without wait times, and eliminate the need for call rerouting during holidays. On the revenue side, Danylov points to car dealerships, where over half of inbound calls go unanswered, representing significant lost sales. A voice agent handling these calls, even with a modest conversion rate, can demonstrably boost revenue. He highlights that technologies become widely adopted when they are fast, cheap, and stable, a threshold voice is now reaching. However, scaling voice-based digital employees requires robust cloud infrastructure.

Danylov’s startup focuses on developing scalable cloud technologies for cross-border communication using AI voice systems. He explains that voice technology, being lighter than video streaming but heavier than typing, demands substantial cloud processing power for real-time audio. Latency quickly becomes an issue if services are distributed across different locations or clouds. The most effective systems integrate automatic speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) within the same physical instance or data center. Leading cloud providers like AWS, Azure, and Google Cloud are facilitating adoption by offering integrated services, including sentiment analysis and translation, under one roof, minimizing friction for developers.

Regarding business models for digital employees, Danylov anticipates subscriptions and performance-based transactions will dominate, mirroring human employment. The subscription model, akin to a monthly salary, will likely be standard for internal support roles such as customer service, reporting, and task automation. This model offers predictability and aligns with existing budgeting practices. For performance-driven functions, like sales bots, a transactional model where payment is a percentage of revenue generated—similar to contingency-based legal fees—is expected to gain traction. This approach, while riskier for vendors, is highly appealing to buyers. Danylov believes framing digital employee costs in terms of payroll or commissions will ease their integration into existing business mental models.

Drawing on his experience migrating financial systems for 25 global automotive plants, Danylov emphasizes key lessons for deploying digital employees. Crucially, “you can’t automate what isn’t documented.” Unlike humans who can infer and adapt, digital employees require fully mapped-out workflows, including all inputs, outputs, exceptions, and failure cases, to prevent errors and breakdowns. If instructions are unclear or business logic is undocumented, automation is premature. Trust is also paramount; digital employees, like new human hires, must earn their place. Deployment should start small, with close observation, before scaling across geographies or business units—a mindset of “slow onboarding, fast scaling.”

Despite the massive potential, Danylov observes that voice technology still receives limited attention, even among cutting-edge startups. As a judge for the 20th Annual Globee Awards for Technology in 2025, he noted that only a handful of the 50 submissions focused on voice, with most centered on text and LLM-based workflows. He attributes this to venture capital’s tendency to fund trendy areas, considering voice a niche. However, he believes the next significant advancements will emerge from overlooked areas like voice and vision. Humans are inherently wired for speech, and widespread adoption is merely a matter of infrastructure catching up. This shift from text to voice is not just technical but cultural and generational.

Danylov, also a mentor at the NYU Alumni in Tech Club, advises young professionals to remain curious and flexible early in their careers, learning broadly and exploring rapidly. More experienced individuals should specialize and deepen their expertise. He clarifies that preparing for voice technology dominance isn’t about acquiring specific “voice skills,” but understanding voice as another input method for underlying AI intelligence. The true transformation is cultural: a move towards machines interacting with humans as humans interact with each other. This shift will create new job categories and displace others. Globally, voice technology will also democratize access to services, education, and work, extending beyond just human-machine interaction.

His work is dedicated to simplifying cross-lingual communication for remote communities. Voice technologies, he predicts, will eliminate the need for intermediaries like interpreters, enabling direct communication across dozens of languages for business, education, and interaction with AI agents worldwide. While voice offers speed advantages over text, it won’t fundamentally change how humans communicate. However, these systems are resource-intensive and will not be cheap to operate. Access will expand dramatically, primarily for those who can afford the services. As with many digital economy offerings, free services will exist, but often come with the caveat that the user, or their data, becomes the product.