News and announcements

As someone working in the domain of conversational speech and language AI for over 25 years, from researching speech recognition in smart meeting rooms through to developing commercial products and services, it’s been an incredible journey seeing the technology evolve.

Along the way, I’ve learned that while we often focus on new technological capabilities, real value is only unlocked when we deliver that within a user experience that helps people achieve the actual job that they need to do, or the problem that a business needs to solve.

Another key learning has been that, while technology changes at a rapid pace, the millions of meetings and business calls taking place around the world each day are just conversations between people, and the fundamental nature of conversations never changes. 

Conversations are dynamic interactions that unfold naturally, and so at first glance transcripts lack the readability and structure of authored text documents.  There is, however, an organic structure to all conversations.  They are set within a context, involving people with roles interacting in speaking turns similar to paragraphs in a document. Like chapters in a book, conversations progress through high-level discussion threads or topics, whether imposed by an agenda, or emerging naturally.  

Ultimately, meaningful conversations include key passages worth highlighting – Moments of significance.  Which specific Moments are significant depends on the context of the conversation.  It could be someone saying they will follow up on an item tomorrow, or raising dissatisfaction with a product. 

This perspective led us to develop Moments as a powerful way to deliver practical insights from business conversations.  As our first Moment, we found Complaints have significance to a range of organisations seeking to improve how they serve their customers.  Other Moments are in the pipeline – such as one that detects when staff suffer verbal abuse on a call, helping businesses provide a safer working environment. 

The public awareness of AI skyrocketed with the launch of Chat GPT and now AI is everywhere. These technologies have, however, been steadily evolving over a long period of time.  The Transformer neural network architecture underlying current language models was proposed back in 2017, and models have been steadily growing in capacity since then.  Another big step forward in this field was around 2018 when automatic speech-to-text reached human-level accuracy at transcribing conversations.  

While these things have been brewing for many years, there is no question that in recent months we have witnessed an enormous step forward in the capabilities of Large Language Models.  For me, the biggest change has been the naturalness of the responses these models generate to a broad array of questions or prompts, across industries and languages.  From our perspective, what this means is that we can now distil long call transcripts into readable summaries and key points just like notes a person might have taken.  For an end user this is a game changer – let’s face it, someone rarely has time to read a full transcript, they are mostly interested in just a few key moments.

In my role it’s important to stay at the forefront of this ever-evolving landscape.  One way we do this is by fostering strong relationships with key technology partners and university research groups.  This helps us see ahead to what is coming in the near-term as well as over the horizon. This highlights one of the benefits that customers gain from our cloud-based product – we seamlessly deliver the most up to date in AI technology for them to tap into.  For them, it’s out of the box and scalable, without requiring them to invest in people, development or budget to implement, maintain or upgrade.

So while much of the world woke up to AI when Chat GPT was released, our conversation intelligence platform has been in development at Dubber for some time – particularly over the two years since acquiring Notiv.  This platform allows us to deliver cloud-based AI-powered product experiences in a flexible, responsible way.  This has positioned us to quickly launch the Moments product on a global scale through Service Providers. 

Having done the heavy lifting, we’re now in the process of developing and rolling out a range of new Moments.  How do we do this?  We start with market research to define a Moment with value across a broad set of companies.  We then collaborate closely with early adopter customers to evaluate and refine the Moment on their calls, validating the benefit to their business.  An initial customer took 40,000 of their calls through this process, and for the first time they could see what people were complaining about, both at a macro level as well as being able to drill down into a specific conversation, helping them improve their customer experience and business performance.

Who do these use cases apply to? I honestly think the answer is ‘every business’.  From a small business, to mid-size, up to enterprise, every company wants to hear directly from their customers to improve their business. 

Technology evolves, but humans will always communicate through natural language.  With Dubber in the network we can help service providers and their customers unlock the full value of their conversations. The use cases for ‘Moments’ are seemingly endless.  Personally, my favourite moment is seeing one of our customers gain a new insight into their business and then realise that it was hiding in their call data all this time, just waiting to be uncovered!

Conversations with leading service providers reveal common themes.

• How do we increase margins  at a time when lower yield UC services are an increasing part of our overall sales?
• How do we deliver value from heavy infrastructure investments like 5G?
• How do we differentiate our services and create new sources of value?

Answering these questions often started with new offerings running over the top of the network. Could we offer streaming services? Could we sell applications? Could we bundle tangential services – like contact centre software?

The answers to these questions lead to a new set of issues. Costs and complexity of sales and marketing increased. Margin was small and elusive. Service and maintenance costs escalated as the Service Provider often became the first point of contact for issues related to third-party services. And, because often what was offered elsewhere, differentiation was weak.

The net result was more money was made by those running on the network, with value accruing to them and not the Service Provider – and the Service Provider incurred the costs of running the network.

“There is probably no other industry that has contributed more in the last 25 years to the welfare and productivity and expansion of economies as the telecom sector. … And you are right. We have not been able to capture a significant part of the new value chain.

… Today roughly 56 per cent of the capacity of the network—of the European networks in this case—is being used by only five over-the-top [OTT] players that pay nothing for the use of the network.”

José María Álvarez-Pallete López ,chairman and CEO of Telefónica S.A. and board chairman of GSMA, Mckinsey Journal, 2022

In the network is the answer.

Service Providers are increasingly scaling back non-core over-the-top service offerings, reducing cost and complexity and returning their focus to extracting the value from services in the network.

This shift in focus is increasingly centred on one of the primary reasons networks exist – conversations in all their forms: voice, video, and text. Rather than simply focusing on monetising the connection that makes the conversation possible, the question is how to turn those conversations into a source of value for the customer and Service Provider.

A new generation of network-embedded services unlocks the ability to turn conversations into data and render that data meaningful through AI, machine learning, and natural language processing.

Imagine a Cisco Webex meeting taking place and the actions automagically summarised in a branded widget exclusive to a Service Provider and their customers. Or a crucial moment in a conversation between a tradie and their client being transcribed and summarised in a text for the tradie to share with other workers on the job. How about a customer satisfaction app unique to a Service Provider summarising customer satisfaction ratings from that day’s service calls, keywords, and agent performance?

Before today, doing any of this required heavy-weight enterprise applications, complex integration and a myriad of increasingly complex services.

Today, this is possible thanks to a new generation of network native services, instantly provisioned and integrated with the Service Providers’ billing engines. The workloads and costs associated with legacy transcription services are replaced with new services concentrating on where the conversations’ value occurs. And critically, the resulting data becomes a customer magnet, reducing churn and creating a foundation on which any number of new innovative services can be created.

Unlocking the value of the network starts in the network. It is the first place to begin improving revenue, retention and innovation.

Service Provider Trends Outlook 2022.

Why Enterprises Are Looking to Switch Service Providers in 2022 – And What You Can Do to Stop It. Explore all the findings in Service Provider Trends Outlook 2022.