One area where we can clearly see this is in conversational intelligence platforms, which use AI to optimize communications and business processes.
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Conversational intelligence solutions are used across sales teams, analytics teams, and more. But how, specifically, are these platforms using AI?
To understand the modern state of AI in conversational intelligence, we can look at how those platforms are using AI technology today, as well as the latest advancements in the technology behind it.
Speech-to-Text
Speech-to-text technology lies at the very core of conversational intelligence – everything else comes from there.
When you feed a conversation to an AI-powered tool, it uses speech-to-text technology to convert the conversation into written word. From there, the transcripts are fed to AI-powered tools, where they can be analyzed and understood.
This means that the speech recognition technology needs to be as accurate as possible. Every word matters, as missing or changing even a single word in a sentence can completely change its meaning. However, speech recognition technology often has difficulty understanding different languages or accents, not to mention dealing with background noise and cross-conversations, so finding an accurate speech-to-text model is essential.
For a good example of accurate and powerful speech-to-text technology, we can look at Universal-1 from AssemblyAI. Universal-1 is trained on 12.5 million hours of multilingual audio data and is designed to account for conditions like background noises, accents, and language switching, making it incredibly accurate. This latest Speech AI model is helping organizations build and improve conversational intelligence platforms.
“The Universal-1 model from Assembly AI is doing a great job at powering some of our core services, including meeting notes, tasks, and Semblian, our AI meeting chatbot. Its high quality multi-language capability aligns well with our global product footprint, supporting over 40 languages including dual language use in meetings. We are also impressed with the speed improvements, which have been especially impactful on our long duration workloads.” – Artem Koren, CPO and Co-founder of Sembly AI
Generative AI
Beyond Speech-to-Text, Generative AI is one of the biggest trends in artificial intelligence technology today. We can see generative AI used to create more natural-sounding conversational AI, such as chatbots and virtual agents, as well as empowering employees for everyday work.
Generative AI is now being used to help employees draft emails and responses, as well as automatically log calls with detailed notes. For example, contact centers will use speech-to-text technology to transcribe conversations. After the transcription is made, generative AI is used to assist contact center agents and sales reps in real time by providing automated coaching and suggestions.
Essentially, generative AI is being used to make conversational intelligence platforms more efficient, intelligent, and natural sounding.
Automated Summaries
One of the most common and helpful features of conversational intelligence platforms is the ability to automatically understand and summarize meetings and conversations. This uses natural language understanding and speech-to-text to not only transcribe the conversation but also to understand it and generate notes and summaries.
These automated summaries can include a wide array of key information, including:
– The conversation topic
– Key points discussed
– Action items
– Important questions
– Points to follow up on
Providing all this information in a concise, easily searchable form helps employees stay organized and on track, improving the efficiency of every meeting.
Sentiment Analysis
Contact center agents and sales reps aren’t mind readers, as much as their supervisors might wish they were. Conversational intelligence platforms may not be able to read callers’ minds either, but they can analyze conversations to gain actionable insights into the conversation.
Sentiment analysis tools help reps and agents by listening in on calls to catch key phrases or tones that indicate the customer’s overall satisfaction. This can then be used to help with agent training or to provide notes and suggestions during the call to steer the conversation and keep the customer satisfied.
Automating Monotonous Tasks
Daily work is filled with monotonous, time-consuming tasks like logging calls and taking notes. Fortunately, conversational intelligence platforms can make those tasks all the easier.
Conversational intelligence platforms use AI to automatically understand calls and conversations, then automatically carry out tasks connected to them. This can include anything from drafting email replies (using generative AI) to logging calls in a CRM, complete with all the pertinent information.
Not only does this save time every day, but it also makes life easier for employees, who no longer need to worry about these tasks themselves.
Conversational intelligence platforms are making work easier for employees and businesses of all shapes and sizes, and it all begins with their speech recognition technology. With a robust and accurate speech-to-text model, like Universal-1 from AssemblyAI, companies can implement powerful conversational intelligence tools to make their jobs more efficient every day.
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