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.
From conversational AI to augmented reality, these can transform a brand’s customer service.
Yet, as slow as steady seemingly wins the race, first consider these three base capabilities for brands attempting to build out their AI plans.
- A Voicebot for Routing
Decades after contact centers first implemented IVRs, most have failed to iron out all the kinks. Navigating the systems remains cumbersome.
Thankfully, voicebots are now available, which harness sophisticated speech recognition to understand customer intent without pretraining.
Contact centers must simply determine: what cases should feed into which queue?
With that as a baseline, operations may then begin to add conversational AI for contact automation once comfortable with their new routing engine.
- Auto-Summarization
As the contact center captures customer intent with its voicebot, connecting it to the CRM allows for automated call disposition. As a result, businesses may speed up post-call processing.
Yet, supplemental AI models may also summarize the subsequent interaction to automate the entire process – shaving seconds off the end of every customer case. This can save contact centers significant sums of money.
- Desktop Automation
Work with agents to keep an eye on the simple, repeated processes they follow when answering customer queries.
Do they repeatedly launch the same applications? Copy and paste the same text? Fill in the same tedious forms?
Consider this across various intents: how much time across the day are these wasting? Applying desktop automation in these areas will improve agent experience, allow them to focus more on the customer, and cut costs.
Begin Your AI Journey with RingCentral
Whether an SMB is building out its UCaaS solution to handle customer interactions or a large enterprise is migrating its contact center to the cloud, RingCentral is often the ideal tech partner.
Indeed, its team works closely with businesses of all shapes and sizes – thanks partially to its iron-clad partnership with NICE – to deliver best-in-class service solutions.
Many of these are AI-centric, and over the years, its team has developed the expertise to support incremental contact center AI transformation projects that deliver excellent outcomes.
After all, slow and steady frequently wins the race.
To learn more about RingCentral’s portfolio of AI-powered apps, visit: www.ringcentral.com/apps/p/ai
