Everyone hates automated customer support… right? Actually, every customer hates automated customer support. Companies love it because it saves them money that they assume is pure overhead. However, what if automated support could be beneficial to both customers and companies? That’s the promise of AI-powered automated customer support.
“A big part of what we need to be able to do at a call center is to help our clients gain that insight to go away with something they didn’t even know they needed before they came into the situation,” noted Rob High, Chief Technology Officer of IBM Watson. “Creating a conversational system within a call center is not something that you simply drop into place and expect on day one to have everything answered that you are trying to accomplish.”
What Are Businesses Trying to Accomplish with Conversational AI Customer Support?
- Automating tasks better and faster.
- Augmenting experiences in order to orchestrate activities.
- Transform and improve your business.
“Ultimately, what many of us are looking for the ability to transform our businesses, to really achieve something greater than what we are able to do today,” says High. “This is to address our clients needs in ways that perhaps haven’t really been considered in the past or that will result in us being able to differentiate ourselves as a business.” That progression, he says, is one that needs to be taken a step at a time because as each step is taken you are increasing the complexity and the data that are needed to bring value.
Evaluating the Conversational Support Option
It’s a long (and often expensive) process to implement AI-powered conversational support into your business call center. So how does your company justify going down this road? IBM’s Rob High thinks there are five concerns to consider:
- Business Value – Address a clearly recognized business opportunity or pain point. “Do you really have a business value proposition that you are trying to address?” asks High. “Is there value that you can identify out of the results that you are trying to create? Can you measure that both quantitatively as well as qualitatively? Are there quantify values that you can associate with the outcome of this effort, such as reducing the first customer time to resolution or increasing net promoter score? Any of those kinds of qualitative and quantitative measurements that are important to you that you can identify use to measure value will allow you then to determine that this is a worthwhile effort.”
- Viable Data – You will need to analyze the availability, accessibility, and quality of your data. It is absolutely key to the implementation of successful conversational support that you think about your data first.
- Technical Feasibility – IBM’s CTO asks, “Do you have access to the technologies that are necessary to achieve the results you are producing? Are you able to get access to the AI systems, the conversational services, and even the data processing that is necessary in order for you to have a successful project.”
- Speed to Implement – The longer it takes to do something and the longer it takes to generate a result can kill support for a key project like conversation AI support. It not only raises costs but it also drives concerns within the enterprise that it will ultimately be successful and critical to future success. “Finding a way of delivering this project quickly by starting simply and getting those results back so that you can build on the value that you are creating is a key element to success here,” said High.
- Alignment with Corporate Initiatives – This comes down to reviewing existing company initiatives to see if AI-powered conversational support is already in alignment. Can you join an existing initiative? If you can it is often the quickest way to get things started.