Contact center AI excitement is reaching fever pitch, with many buoyed by the breakthrough of ChatGPT and Bard.
Indeed, online demos highlight just how far AI technology has come since the early days of chatbots and simplistic desktop automation.
As such, many businesses are opening the door to augmenting their customer operations with AI – exploring the applications of conversational AI, NLP, RPA, and more.
Yet, even in recent years, some large-scale contact center AI projects have missed the mark, according to Robin Gareiss, CEO and Principal Analyst at Metrigy.
In a 2022 study exploring the state of customer experience technology, Gareiss writes:
“Part of the problem is that many companies (particularly large ones) have tried to boil the ocean, adding too many AI-based applications at once, resulting in failed implementations.”
Of course, vendors should also take a fair portion of the blame, with many end-users unhappy with the usability and sophistication of many offerings.
Indeed, when scoring elements of their CCaaS platforms, participants in the Metrigy study gave the native AI capabilities the lowest sentiment score – as the chart below highlights. Ouch.
Nevertheless, when done well, contact center AI can dramatically improve agent, business, and customer outcomes.
Noting this, Gareiss states:
“We recommend organizations adopt AI carefully and methodically, addressing a specific problem or opportunity. After each deployment, measure success, learn from the implementation, and move to the next project.”
No more ambitious moon shots. Instead, as AI matures, assess customer success stories, identify the low-hanging fruit, and kickstart an incremental AI transformation process.
A Cautious Step Forwards
Blair Pleasant, President & Principal Analyst at COMMfusion, also advocates for a more cautious, thoughtful approach to contact center AI implementation.
As marketing teams throw the terms “ChatGPT” and “Bard” at them – as many inevitably will throughout 2023 – such attentiveness becomes paramount.
“In 2023, I’m expecting to see more focus on the best ways to deploy various AI capabilities, ensuring a good user experience,” says Pleasant in a thought-provoking predictions piece.
Indeed, those who network and engage with fellow industry professionals will undoubtedly uncover many more AI best practices, learning what works and doesn’t.
Nothing this, Pleasant adds:
“Businesses will be more thoughtful about how they roll out AI, identifying the best use cases, and making it a better experience for both employees and customers.”
Yet, what are these best use cases? The following three may offer some excellent guidance for those taking their first anxious steps into the big, promise-heavy world of AI.
3 Beginner Contact Center AI Use Cases
Many CCaaS platforms embed AI into stalwart contact center tools. For instance, a knowledge base will have a search function – typically laced with NLU – to make it easier for agents to access insights.
Another example is an agent desktop, which likely harnesses RPA to trigger multiple tasks within a single click. Such a task could include sending customer information to an integrated CRM.
A CCaaS vendor like RingCentral will provide all this to streamline customer operations. Yet, they will also make additional AI capabilities available to the contact center.