AI (Artificial Intelligence) and ML (Machine Learning) tools are some of the most requested tech by contact centers, but what are the actual use cases of these technologies? Is contact center AI a trend that’s here to stay, and are companies really ready to deploy it?
Our 2022 Tech Summit Panel, “AI, Machine Learning, Automation: Hype or Hope?” brought together some of the biggest leaders in the contact center market. They talked about whether or not these tools are just a fad or essential pieces of tech that drive demonstratable results to the bottom line.
Our speakers were:
- Tony Poer, Channel SE Leader at 8×8
- Geoff Reimer, VP Global Sales Engineering at Dialpad
- Jason Wyant, VP Contact Center Sales at Ringcentral
- Alain Mowad, Director, Product Marketing at Talkdesk
- Karim Zourih, Director of Strategic Innovation at Vonage
Read on to learn more about what the experts are saying about what’s really happening with AI tools and contact center. Check out the video above to watch the full panel.
Are Contact Center AI Tools Needed?
Artificial intelligence (AI) is everywhere right now. During this session, our panelists noted that many clients ask about contact center AI without understanding exactly what that means.
The panelists noted that the use of the term “AI” in the market is similar to how “the cloud” was used five to 10 years ago. Every technology solution was called “cloud”. Our experts considered whether or not the same thing is happening with AI today. Is every bit of tech that has just a little extra intelligence in it called “AI”?
Geoff Reimer of Dialpad agrees that the word AI is overused, but that today’s contact center AI tools are real and useful. Whereas AI was simply a buzzword three years ago, today there are very real use cases. He said it’s really a matter of identifying the client’s actual need.
What should IT leaders do when clients ask vague questions like, “do you have AI?”
The panelists agree that the first step is to understand what the client means by AI and what they hope automation can do for them.
One of the problems in asking “what can you do with AI?” is that it’s like asking “What can you do with the internet?” The applications are potentially limitless, so it’s about identifying the client’s needs and matching them with the tools that will give them the best outcomes and ROI.
Tony Poer of 8×8 noted that one of the first places contact center AI, machine learning and robotic process automation can have the biggest impact is in limiting or replacing time-consuming transactions. The most prevalent transaction in the contact center is probably a huge pain point for agents — a transaction that they do hundreds of times a day. Automating a transaction like that can free up a lot of time and drive efficiency.
Chatbots are a frequently requested contact center AI tool because they save time and improve the customer experience.
Jason Wyant of RingCentral warns, however, that if contact centers want to implement chatbots, they need a knowledge management system that informs the bot, as the bot can’t learn based on nothing. The more in-depth the knowledge management system, the better the bot will be able to help customers.
Understanding Customer Intent
Another aspect of AI for contact center is that the panelists discussed was the importance of understanding customer intent. Many AI tools scan for keywords and give recommendations for the next steps. The best AI tools, however, scan for phrases that hint at the deeper intent of the customer.
As an example, if a customer calls up looking for “drill bits”, a standard contact center AI tool will scan the call for the keyword “drill bits” and give the agent recommendations on different drill bits to sell the customer.
However, with a deep knowledge base that understands that customers looking for “drill bits” tend to want to drill a hole in the wall, the AI tool can then give recommendations for other tools needed to drill holes in the wall.
A deep knowledge base gives context to the request, which then triggers better customer interaction. The panelists all agreed that context is critical with these tools. That said, this is a great example of how these tools can generate value and revenue if they address pain points for the contact center.
Use Cases for Contact Center AI
What are some of the most interesting contact center AI use cases?
Alain Mowad of Talkdesk shared that AI can quickly add value to some of the most common challenges contact centers face. Unlike a few years ago when implementing AI tools required data scientists, contact center AI is now relatively simple to get going.
Some of the challenges that AI can address for the contact center are staffing shortages, skill shortage, higher call volumes, more complex customer calls, and optimizing the customer experience.
Karim Zourih from Vonage made the point that enhancing the customer experience is really about optimizing the agent experience. Voice bot technology does both — it helps customers get to the right agent and it helps agents know a bit about the customer’s problem.
Also, Karim talked about the value of contact center AI tools in sales teams, not just customer experience teams. In one example that he gives, an AI tool analyzed sales pitches and saw a 20% increase in sales that used the word “invest” instead of “buy.” This recommendation led the insurance company to change its sales script accordingly, and they saw an increase in sales as a result.
Tony of 8×8 says that AI has the capability of monitoring every interaction to determine when a customer sounds upset or when an interaction is escalating and needs further intervention.
These examples show the potential ROI of contact center AI on both sides of the balance sheet — cost containment and revenue.
AI tools continue to develop, and more use cases are emerging. The key to applying contact center AI is understanding how AI can solve the contact center’s biggest pain points.
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