Is it a bird? Is it a plane? No, it’s contact center innovation that’s soaring at an unprecedented speed – and generative AI (GenAI) is providing the rocket fuel.
Indeed, large language models (LLMs) have become the IT team’s best friend, auto-generating code to accelerate the development of new contact center applications.
Moreover, these models are the backbone of many new natural language interfaces, advancing low-/no-code tooling and adding further fuel to the innovation fire.
CX leaders may use these to build better experiences, faster.
Yet, contact center agents are perhaps the biggest winner of the GenAI revolution – as CCaaS vendors release agent-focused features at a scorching pace, blazing a trail for new, enhanced employee experiences.
Generative AI for Live Agents
GenAI has brought many new use cases for agent-assist to life. Some excellent examples are auto-summarizing calls for wrap-ups and handovers, auto-drafting agent notes, and auto-generating performance feedback.
European CCaaS heavyweight Puzzel is helping to set the pace here. Yet, it’s not neglecting older AI use cases, which can generate even more value with the addition of GenAI.
Sharing an example, Mashud Ahmed, Product Manager at Puzzel, said: “A year ago, we would have been talking about next-best action – tailored with a rudimentary understanding of the customer.
“Next-best action is now much more powerful and relevant, understanding the context of the conversation at hand and personalizing its recommended actions to the customer journey.
“That is equally valid to use cases that save time here and there, such as those summarizations, automated note-taking, and so forth.”
With this next-best action 2.0, the agent remains in control in case the conversation goes off course, yet the assistant guiding them is much more aware and capable.
GenAI also assists agents by augmenting workforce engagement management (WEM) suites.
In doing so, it may add context to automated quality scores for enhanced post-call coaching. In addition, it may strip away live insights – such as customer sentiment – to coach agents in real-time.
Generative AI for Virtual Agents
GenAI supports developers in designing, developing, and optimizing virtual agents.
Mapping customer intents, developing text samples, and validating virtual agent designs are just three examples of how it’s doing so.
The customer intent work is perhaps most significant. After all, an intent model drives every virtual agent, recognizing the contact reason and certifying a response.