Voice technology is a broad field that has been around for decades and is rapidly evolving, thanks in large part to the advent of AI.
This field is no longer primarily about speech recognition and speech-to-text transcription accuracy. With the help of AI, today's speech-to-text has been automated to a degree that real-time transcription is sufficient for most business use cases. Speech-to-text conversion is never 100% accurate, but it is comparable to human transcription, much faster, and at a fraction of the cost.
For some, this may be the only interesting use case for AI-based voice technology, but in the field of workplace communication and collaboration, this is really just the beginning. For the past six years, I've presented an annual update on this topic at Enterprise Connect. Take a look at his three key voice technology trends discussed at this year's conference that IT leaders should consider.
1. AI is based on voice technology
Today, AI goes far beyond basic transcription. Many AI-powered applications have become standard features in leading unified communications as a service (UCaaS) products, such as real-time transcription, real-time translation, meeting summaries, and post-meeting action items. Note that some use cases apply only to voice, while others are voice-based activities related to other applications, such as calendars.
Modern applications rely on generative AI to automatically create consistent email responses, notes, and blog posts from voice or text prompts (most employees would prefer to use voice) .
Today's games are built on traditional voice technology. But with AI, the use cases are much broader and integrated into the entire workflow, rather than just being used for speech recognition.
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IT leaders should expect these capabilities to be key factors when evaluating potential UCaaS offerings or considering how to stay up-to-date within an existing deployment. All of these AI-based applications are still in development and will need to continue to improve, both in terms of voice accuracy and integration with other workplace and productivity tools.
2. New applications
Even as IT leaders evaluate these new capabilities, they must not lose sight of the big picture. These applications are primarily applied to the way people work today and tend to be seen as point products that perform a specific set of tasks very well. But AI is advancing faster than ever before. While many of these tasks are now largely mastered, the next wave of innovation based on AI voice technology will work at a higher, organization-wide scale.
A great example of this is conversational AI. This makes chatbots more conversational and human-like, making self-service options more comfortable in contact centers. Today's chatbots are far from perfect, but they are now much more widely deployed within companies, with employees using them as digital assistants.
Large-scale language models (LLM) are the next big phase in AI. The main idea here is that businesses see value in capturing all forms of digital communication to make AI applications more effective. Although text and video have long been digitized, many audio formats have not. Since the majority of everyday communication is voice-based, there is a growing interest in capturing this information. dark dataThis is because it represents a valuable data input set for AI.
The development and management of LLMs is rapidly evolving, not only because of the nature of AI, but also because executives recognize the potential of LLMs as a competitive differentiator. (In reality, there are many types of language models in AI, so mentioning an LLM here is an oversimplification. Most IT leaders are not data scientists, so this field requires outside expertise.) ) This trend requires IT leaders to think more strategically about voice technology.
More importantly, AI recognizes how voice applications are connected to everything else and integrated with workflow, project management, personal productivity, and team-based outcomes.
3. Strategic implications for IT
It's clear that IT needs to move beyond the legacy model of voice technology, especially as AI is driving much of the innovation around voice and other communications. As a result, trends in audio technology can no longer be captured in isolation, and success is determined by transcription accuracy.
More importantly, AI recognizes how voice applications are connected to everything else and integrated with workflow, project management, personal productivity, and team-based outcomes. Everyday conversations still have inherent value no matter where they occur, but AI is likely to make them even more valuable as digital streams blended with other digital streams.
This is what makes voice technology so strategic in the enterprise. While these applications will continue to play a key role in helping employees communicate and collaborate more effectively, primarily using UCaaS, the bigger picture is where the business value of AI actually lies. There is a need to accurately identify what is there.
Data is the oxygen that gives AI life, and the more data a model has, the greater its benefits. Most organizations only capture a small portion of their dark data, so this is where voice technology really comes into play when considering AI plans.
John Arnold is a principal at J Arnold & Associates and an independent analyst providing thought leadership and go-to-market advice focused on the business-level impact of communications technology on digital transformation.
