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AI + IA = Great CX

By Donna Fluss

View this article on the publisher’s website.

When it comes to contact center systems and applications, no topic is generating more buzz and excitement than artificial intelligence. Never mind the technology’s tremendous future potential, AI is enabling advancements now, and a great example is interaction analytics (IA), also known as speech and text analytics. These solutions, built on AI technologies that include natural language processing (NLP) and natural language understanding (NLU), have recently undergone substantial increases in their accuracy, effectiveness, and benefits. And one can’t overlook the impact of generative AI technologies such as OpenAI’s GPT-4, Google Bard, BLOOM, and others, which in their early days have already vastly improved ease of implementation and expanded the use cases and audience for IA solutions.

How AI Makes This Happen

AI is an expanding group of technologies that, when supported by a large and appropriately sourced, targeted, tagged, and curated data repository, can discern patterns and perform some basic humanlike functions. For contact centers, this means that once it recognizes the processing steps for each type of inquiry or transaction, AI will be able to automate their handling and resolution, freeing agents to perform more complex tasks and activities.

One of the latest AI technologies is GPT-4, the fourth generation of OpenAI’s language prediction model. GPT-4 is a neural network machine learning model that uses NLP and natural language generation to understand and produce humanlike natural language text. A neural network “learns” by identifying patterns in massive amounts of digital data, which enables it to predict the next word in a sequence. And with 175 billion parameters, GPT-4 is a powerful language processing AI model. GPT-4 enables users to give the trained AI model a wide range of text-based prompts and can then generate written content in a variety of formats on the given topic. Because GPT-4 and similar generative AI technologies can produce realistic humanlike text, they have a wide range of uses in contact centers as well as other customer experience-oriented functions.

Practical IA-Based Applications

IA vendors are investing in traditional and generative AI technologies to improve the identification of customer intents, automate the summarization of conversations, assess agent soft skills (like professionalism or empathy), and predict customer satisfaction and propensities. The most recent generation of IA offerings comes with transformational tools that leverage AI to passively capture voice-of-the-customer (VoC) findings and insights, minimizing (or even eliminating) the need to survey customers directly. Other AI-based tools deliver real-time feedback and guidance to agents and supervisors, enabling them to enhance their performance while building agent engagement and increase productivity by reducing average handle time. In many cases, these solutions also provide a full transcript of the interaction that can be used to improve many aspects of CX.

IA solutions deliver great benefits on their own due to their ability to identify and surface the underlying reasons that customers interact with a business, along with an appreciation of customer effort, emotion, sentiment, and satisfaction. However, the value and quantifiable contribution of IA technology grows when it is combined with third-party solutions for all kinds of CX uses and applications, including the ones below:

  1. Real-time guidance.This application leverages AI, including NLU and generative AI, to listen to and understand both sides of a live conversation, then advise and guide agents to properly address objections (in a sales or collections environment) or resolve and handle service inquiries, in real time.
  2. Virtual assistants.These real-time “advisers” provide agents the guidance and information they need to accurately address opportunities, and they can fully resolve issues with little or no agent input. These solutions also include self-service capabilities to assist employees with internal activities such as scheduling and HR processes.
  3. Analytics-enabled quality management.This application monitors and evaluates all voice and digital interactions and provides a constant flow of coaching recommendations to help agents improve their performance.
  4. After-interaction summarization.Relevant phrases, metadata, and intents are extracted from the conversation or transcript in real time to create a summary that’s exported to a CRM or other customer tracking system.

Interaction analytics technology, particularly when enriched with generative AI, is providing the foundation for a new group of contact center applications. These solutions, designed to assist and augment the work done by agents, are valuable because they help improve CX, EX, and productivity. It’s time for contact centers to assess their infrastructure and update it with AI-based solutions that will position them to deliver on their mission.