RingCentral is one of the latest companies to delve into generative AI, with a new AI platform: RingSense. This new platform uses voice and natural language processing to draw insights from conversation data by leveraging generative AI, so let’s take a look at what RingSense is, how it works, and what it can bring to businesses.
RingSense Summarized
RingSense is an AI platform from RingCentral that uses generative AI and conversational intelligence to analyze conversations, thus gaining new insights into customers, agent performances, trends, and so on. While it is intended to expand, currently the RingSense AI portfolio consists of RingSense for Sales, designed specifically for sales teams and agents.
RingSense for Sales analyzes interactions between sales representatives and customers or prospects to gain information like performance metrics and customer insights, which can then be used to help train and mentor representatives, help teams improve, and gain more information about what customers are looking for.
There are several AI capabilities built into RingSense for Sales, which we can expect to see used in some form or another in future RingSense solutions:
- AI-generated notes, summaries, and follow-ups that are automatically added to the sales rep’s CRM or productivity suite.
- Rep scores and reporting, designed to help managers quickly search conversations to find customers and reps that need the most attention.
- Keyword and phrase tracking, which can be fine-tuned to focus on entire concepts, products, or competitors.
- Integrations with third-party apps, including CRMs like Salesforce and Hubspot, calendar applications, and a variety of call and video meetings.
These are popular AI features, commonly used to improve productivity and reduce time spent taking notes or searching through recordings, which can prove useful for sales teams using RingCentral.
Along with RingSense for Sales, RingCentral has plans to release RingSense solutions for a wider array of teams and departments, including:
- Revenue Leaders, with custom scorecards, tailored onboarding for new hires, and automated feedback.
- Sales Enablement, featuring custom libraries of sales techniques, sales playbooks based on actual customer data, and insights into individuals and teams.
- Customer Success, through automated summaries of buyer journeys, monitoring for ongoing upsell activities, and reviews of past interactions.
- Marketing, by tracking key moments for customer behavior and customer-driven messaging.
Artificial Intelligence APIs
At the same time as RingSense, RingCentral also introduced AI APIs, designed to let developers access their own data from RingCentral. This is designed to let users extract and analyze their transcripts and interactions across channels to gain a better understanding of their customer interactions and overall sentiment across platforms.
Essentially, this is a way to bring RingSense to RingCentral solutions, APIs, and third-party applications that don’t currently have it. Developers can build private or public apps and integrations using RingCentral’s AI APIs, including such features as:
- Speech-to-text conversions
- Talk-to-listen ratios that identify engagement levels
- Automatic meeting agendas
- Sentiment analysis
This is not RingCentral’s first foray into artificial intelligence, as the company has previously released several AI-powered video meeting capabilities, including AI-powered meeting insights and summaries. Artificial Intelligence APIs are an attempt to extend those capabilities to more solutions and are currently available in an open beta.
The Use of AI in Sales and Communications
Generative AI has seen a massive boost in usage and attention in recent months, and RingSense is another example of how it’s being used in business communications. However, this is the tip of the iceberg, as there are several ways that generative and conversational AI is reshaping business communications across teams and departments.
One major use of generative AI is assisting with calls by accessing information and providing suggestions in real time. AI can be trained on company data, policies, and product information, enabling it to instantly access information when it’s needed. We can see this in play for support calls and internal meetings alike, whether an agent needs assistance finding information for a customer or meeting participants are looking for new data and reports.
Another common use is personalization, for both employees and customers (even when the customer is using self-service features). AI can use customer data to create suggestions and provide assistance specific to each customer’s needs and history, improving both engagement and loyalty. This personalized information can also be used by sales teams to gain new insights and key information specific to each customer, helping them close more deals.
On a similar note, many businesses are using AI for lead generation. AI can help identify potential customers by analyzing their data and identifying patterns that indicate potential interest, which sales reps can then use to connect with the prospect. In some cases, the AI can even be set to automatically draft outreach emails for the prospects (although those should never be sent out without a human proofreading them first).
However, despite the name, artificial intelligence is not entirely “intelligent.” AI is trained on massive amounts of data and programmed to identify patterns, phrases, and key indicators, as well as carry out tasks, but it is still just a program and is not immune to making mistakes. Human oversight remains important for anyone using artificial intelligence for their communications, especially when customers or prospective customers are involved.
In this case, the AI for RingSense is used on the business’ end to gain insights and analyze data, rather than interact directly with customers. This is an effective use, as it provides suggestions and actionable data, which the human users can view and act upon.
As RingSense shows, generative AI can be particularly helpful for gaining insights into both employees and customers by analyzing conversations, automating tasks, and identifying strengths, weaknesses, and opportunities. As the technology continues to develop, we’ll undoubtedly see more applications for it in business communications.
