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Within technology, there’s no bigger story today than generative artificial intelligence (AI). While AI itself has been around for years, recent advancements have sparked a ferocious response. Generative tools such as OpenAI’s ChatGPT are causing us to rethink what we can do with automation by enabling anyone to use this technology — not just specialists with a team of data scientists behind them.
The business world is taking notice. According to recent research, 78% of companies plan to use AI to improve their customer experience, and by 2025, it’s estimated that 85% of customer interactions will be impacted by AI. This means that the right time to start deploying generative AI — through a purpose-built CX solution like NICE Enlighten — is now.
Improving Virtual Agents
Intelligent virtual agents (IVAs) have been around for a while, but they’ve traditionally been met with everything from skepticism to downright hatred.
The problem?
Without the power of large language models, they’re not very smart, and more akin to an interactive voice response (IVR) system. As a result, customers don’t feel understood or well-served by these bots. According to research from Frost & Sullivan, only 44% of retail customers and 31% of hospitality customers say they’re satisfied with virtual agent interactions.
Further, despite the growth in the use of virtual agents, frustration among customers is growing, with the top three complaints being slow resolution times, inadequately trained bots and an inability to be understood.
In addition to lowering customer satisfaction and hurting your brand reputation, a poor virtual agent experience also means that live agents have to do more work in order to solve customer frustrations.
The Impact of Large Language Models
To keep customers from abandoning self-service options altogether, automated interactions need to be just as good as live ones.
Here’s where generative AI comes in.
Through the large language models that power them, an AI-infused virtual agent can converse more naturally with customers. This means that customers no longer have to worry about prompting chatbots with exact words. They can speak however they wish and still be understood, which gives them a stronger sense of control and likely, a faster resolution time.
However, none of these advancements will make human agents replaceable. While generative AI has raised the bar in terms of how complex a task it can solve, AI-powered bots still work particularly well for high-frequency, low-complexity interactions. The AI bot handles issues such as refund requests or other basic questions so a customer can fully self-serve without the need to escalate the interaction to a human agent.
By handing these off, agents can focus on serving customers with more complex, less common issues, or who might not know exactly what kind of help they need.
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Action Items
Building trust and ensuring customer loyalty is key when automating interactions, according to Frost and Sullivan research. Some tips to help include:
Reconsider how to measure success: Average Handle Time (AHT) and First Call Resolution (FCR) are typically used to measure how efficient an agent is at handling interactions. However, routing algorithms are now more sophisticated thanks to AI, and a lot can get done on one call. This may increase AHT but improve overall outcomes. With AI handling low-level tasks, contact centers can focus even more on becoming revenue generators.
Ensure the security of your data: Because AI models will likely be handling proprietary data, including information on customers or even employees, it needs to be housed in a secure environment.
Create a collaboration, not a competition: One way businesses have failed is by trying to take these technologies and apply it to everything without taking into account the areas where the human touch is necessary. Companies need to understand how and when to apply AI to create the optimal customer experience. The technology shouldn’t compete with your human agents, but rather work in harmony so the customer experience is optimized for customers.
Conclusion
Although generative AI represents a significant opportunity for CX organizations, they can’t do it on their own. Solutions like Enlighten from NICE leverage conventional, brand-aligned chatbots together with the data of billions of customer interactions. This allows Enlighten to create optimized flows for customers to efficiently self-serve and provide insights and coaching to agents in real time. This collaboration between humans and technology improves interactions by empowering agents to deliver optimized customer experiences.
Ultimately, the winners of the AI revolution will be those who can move quickly but decisively and aim for solutions built with CX top of mind.
Learn how Enlighten can help your organization drive extraordinary customer experiences at nice.com.
