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Generative AI

Generative AI refers to artificial intelligence models designed to produce original content. There are several use cases for the contact center.
Glossary >G - L

As we witness unprecedented advancements in artificial intelligence (AI), one branch of the technology is emerging as a groundbreaking solution in customer service and support: generative AI. 

The application of generative AI in contact centers has a wide-ranging impact—from enhancing the customer experience, to promoting automation and improving operational efficiency, to boosting the bottom line. 

“When we look back on 2023, it will be remembered as the year Generative AI changed the world,” says Swapnil Jain, CEO of Observe.ai. 

But what exactly is generative AI, and how can it help your contact center?

What is Generative AI?

First things first: What exactly is Generative AI? Generative AI refers to artificial intelligence models designed to produce original content. It involves expertly training large language models (LLMs) on massive datasets, using deep learning and machine learning. The result is human-like text, artwork, music, and other forms of original content. 

Using natural language processing (NLP) and advanced algorithms, generative AI intelligently “learns” patterns and generates coherent, context-specific outputs.

LLMs: The engines behind Generative AI

Large language models are trained to analyze and produce human language. They’re based on massive amounts of available data, giving them a huge vocabulary and capacity for understanding complex language structures, nuances, and context. 

A variety of LLMs have been developed over the years, driven by increasingly large data sets and complexity. But for contact centers, larger is not always better.

That’s because generic LLMs, such as ChatGPT, are not equipped to handle the enterprise-level specificity, detail, and precision needed for contact centers. They are prone to serious inaccuracies and confabulations, making them too risky to use in business settings.

But what’s great about LLMs is that they can be targeted and tailored for specific industries and purposes. You can feed them customized data and create domain-specific LLMs expertly trained for certain use cases. This not only produces significantly more accurate responses and predictions but also offers users unprecedented levels of trust, control, and feedback. Observe.AI uses an expertly trained, contact-center-specific LLM for just this reason.

Benefits of Generative AI in contact centers

In a system traditionally driven by direct human interaction, the introduction of AI in contact centers has automated several routine processes, enhancing efficiency, and elevating customer satisfaction. 

In addition to leveraging generative AI for chatbots and conversational AI to enhance real-time customer interactions by addressing customer needs more effectively, these new technologies can provide effective and efficient ways to support agents in real-time (see use cases below).

Industries from healthcare to e-commerce can benefit from applying Generative AI in their contact centers. Here are some benefits:

  • Personalized customer experience. Generative AI crafts context-specific responses based on customer queries and interactions, providing an enriched, personalized experience.
  • Actionable insights: Generative AI can analyze and convert vast amounts of unstructured data into actionable narratives. These insights help predict customer behavior and enhance customer experiences.
  • Faster call resolution: As the technology makes use of real-time transcription and summaries to provide solutions, customers receive prompt and accurate responses.
  • Improved workflows: By handling routine tasks, Generative AI streamlines workflows, allowing human agents to tackle complex issues and achieve better results.
  • Reduced operational costs: With more tasks resolved by AI technology, contact centers can minimize operational expenses related to human interventions.
  • 24/7 support: AI doesn't sleep. This allows business operations to run around the clock, promising consistent and uninterrupted customer support.

Specific Use Cases of Generative AI in Contact Centers

We've established that the application of Generative AI in contact centers is compelling, but how exactly is it being used? Let's delve into a few practical functionalities.

  1. AI-driven chatbots. With Generative AI, chatbots have become more sophisticated and context-aware. These bots offer personalized customer interactions while providing real-time solutions to customer queries or inquiries, improving the customer experience.
  2. Knowledge AI. Gone are the days of agents putting customers on hold for minutes at a time while they search for answers in company materials. Features like Observe.ai’s Knowlege AI automatically analyzes all company information sources so that agents can simply type in a question and the system will produce the best response to customer questions—instantly.
  3. Automation. Generative AI automates repetitive tasks in contact centers, allowing human agents to focus on more complex issues. This AI-powered automation includes transcription, summaries, and follow-up emails; streamlining workflows; and boosting efficiency. Observe.ai’s Auto Summary capability, for example, can instantly create different kinds of summaries tailored to the contact center’s needs—such as structured based on criteria such as reason for the call or customer issue; unstructured, which is a free-flowing description of the entire conversation; and entity-based, which identifies key entities mentioned on the call, including names, phone numbers, dollar amounts, and so on.
  4. Auto coaching. Thanks to automation and AI-powered analysis, agents have better insight than ever before into their performance on each and every call. Observe.ai’s auto-coaching feature can generate instantaneous feedback for agents while they’re still on the call so that they can make quick adjustments and immediately improve results—without having to wait for a post-call analysis or supervisor feedback. This method cuts down on the ramp time to improve performance and boosts a wide range of contact center metrics like CSAT and FCR.
  5. Enhancing sentiment analysis and predictive capabilities. In addition to identifying customer behavioral patterns, Generative AI can analyze customer data for deeper insights. This helps agents offer tailored solutions and anticipate customer needs. 
  6. Agent assist for seamless customer support. Generative AI assists contact center agents by offering relevant knowledge from various knowledge bases and CRM providers. By understanding natural language, AI models can extract valuable information in real time to support customer interactions. It can also automatically audit customer-agent interactions to gauge the quality of the interactions, suggesting areas of improvement based on customer sentiment and tone of conversation. 
  7. Self-service using messaging platforms. Generative AI systems, such as those offered by Amazon and Salesforce, can integrate with popular messaging platforms. They enable customers to resolve their issues independently by interacting with contact center AI solutions through messaging.
  8. Content generation. Generative AI tools can generate emails, notifications, or reports, which helps to streamline communication and increase productivity in contact centers.

Future-Proofing Contact Centers with Generative AI

Generative AI is revolutionizing contact centers. As it continues to advance and organizations fine-tune their AI capabilities, the potential for AI-driven customer support will only grow stronger. 

That said, it’s imperative to ensure that your organization is experimenting with Generative AI in the right way.

“This is my strong recommendation to you: Experiment early and experiment fast, but experiment responsibly,” Jain says. 

How do you do that?

“Generative AI solutions must have the ability to calibrate and fine tune the system. By nature, generic out-of-the-box models are trained on broad data sets and will not understand the nuances of contact center conversations,” Jain explains. “A black box solution without the means to calibrate it can be risky when things go awry. No machine gets it right the first time, every time. Allowing humans to refine the machine is essential.”

Further, he adds, “for any Generative AI application you’re considering, if it checks [these] two boxes—low barrier to entry and easily reversible—and the value is there, then it’s well worth a test. The downside is minimal, and the upside is limitless. Experiment. Learn. Iterate.”

Harnessing the power of Generative AI today prepares businesses for a future where customer satisfaction and efficiency are the keys to success.