Since the introduction of ChatGPT by OpenAI, Generative AI has taken the world by storm. Businesses interested in differentiating themselves from other competitors are looking at Generative AI as a way to create a more efficient and productive agent workforce. Gartner estimates that 70% of companies are in exploration mode with Generative AI.
But there are also several pitfalls to investing in technologies purely based on the GPT large language models (LLM). Sharing confidential or PII information with ChatGPT, for example, can run the risk of data breaches, reputational damage, and financial losses.
Observe.AI’s 30 Billion Parameter Contact Center LLM is here
The Observe.AI team started building and using contact center-specific LLMs when they first emerged in 2018, starting with the famous BERT model. However, unlike the traditional method of fine-tuning the out-of-box language models (LM), we trained our own models to understand the characteristics of contact center interactions.
The downside of out-of-box LMs like GPT, is that they are trained on what is called “clean text”, e.g. language that is simplified and distilled to root worlds that are easy for a machine to understand. This clean data looks very different from the natural conversation between agents and customers, where the disfluencies (stuttering, drawing out sounds, or repetitions) and non-grammatical utterances are common.
Observe.AI has built an LLM that is completely customized for contact center data and trained to perform well for contact center use-cases like call summarization, generating coaching tips or responses to customer questions. The LLM is trained with different numbers of parameters (7B, 13B, and 30B parameters) to maximize its performance for contact centers. Based on initial tests, our Contact Center LLM was found to be 35% more accurate than GPT3.5 in automatically summarizing conversations and 33% more accurate in identifying customer or agent sentiment during calls.
So what can this LLM do for you?
The New Generative AI Suite by Observe.AI
Observe.AI’s new Generative AI Suite empowers agents throughout the entire customer interaction process, improving performance and productivity every step of the way.
Knowledge AI: Answer customer questions faster and better than before
Today, when a customer asks a question that isn’t very easy to answer, your agents put them on a “brief hold” to go search knowledge base (KB) articles or ask a supervisor. Industry reports suggest that 46% of customers are put on hold for an average of 55 seconds at a time. The same article also suggests that customers who are put on hold report a 13% lower CSAT and a 16% lower first call resolution (FCR).
With Knowledge AI, Observe.AI has eliminated the need for agents to scour through your knowledge bases.
Agents can simply type the question and get ready-to-use answers. Once you have simply connected Knowledge AI to your KBs or even manually uploaded documents, Knowledge AI analyzes the information sources to deliver the best response to customer questions instantly.
Agents also get citations in the form of links to the original documents if they want to access more details around the answer.
This form of just-in-time knowledge discovery reduces customer hold or wait time and results in better CSAT as well as higher FCR. As a result, your overall average handle time (AHT) also improves.
Auto Summary: Automatically capture the essence of customer interactions
In early 2023, we launched Automated Actions for Real-Time Agent Assist as a unique way to automate parts of note-taking and reduce after-call work.
With our new Generative AI-powered Auto Summary contact centers can now completely eliminate the need for agents to capture notes. Generative AI can create summaries in multiple formats:
- Structured: Select the kind of structure you want your call summary in, e.g. reason for call, customer issue, solutions provided, follow-up or next steps, etc.
- Unstructured: Ask for a free-flowing description of what the essence of the entire conversation was.
- Entities: It identifies key entities mentioned on the call like names, phone numbers, dollar amounts, and so on.
These summaries can be generated as soon as the call ends or also in batches in our post-interaction AI solution for use in QA or coaching.
Additionally, Generative AI-based summaries can be combined with the existing capabilities of moment-based notes capture as well as manually adding notes in Real-Time Agent Assist.
Auto Summary has the potential to completely eliminate after-call work and make your contact center more productive. At the same time, you can save time on training and coaching agents on improving the quality and consistency of their note-taking. Machines can now summarize better and faster than humans.
So if agents save time with features like Knowledge AI and Auto Summary, can they also use Generative AI to make better use of the saved time? Read on to see how.
Auto Coaching: Provide agents with immediate, in-the-moment coaching
QA and manager-driven coaching are critical methods for improving agent performance. But is there an opportunity to complement these workflows with agent self-coaching?
With Auto Coaching, agents will have the opportunity to self-coach and learn what went well and what didn't in the interaction that just ended. Auto-created feedback from Generative AI is served up to agents so they can make quick adjustments on their own to improve performance, without having to wait for QA coaching or supervisor feedback.
This method cuts down time to improvement in agent performance and impacts a wide range of contact center metrics like CSAT and FCR.
Interested in learning more about our new Generative AI Suite?
These three new capabilities are the first step in our Generative AI journey. Over the coming months, we look forward to working with our customers, uncovering new use cases and adding new Generative AI capabilities across our real-time and post-interaction AI solutions.
If you want to learn more about these Generative AI capabilities or what your approach to LLMs should be, here are some additional resources to get you started:
- Website pages: Contact Center LLM | Knowledge AI | Auto Summary
- The Generative AI Manifesto, from CEO Swapnil Jain
- Get the Ebook: The Comprehensive Guide to Generative AI for Contact Centers
- Request a demo: See Generative AI for contact centers in action
