Artificial Intelligence in Customer Service
Comments on Gartner’s three important ways Artificial intelligence (AI) can benefit Customer Service Operations.
Gartner has recently published an engaging article on Artificial Intelligence (AI) in Customer Service operations (Dated 19 January 2022). This article is available online on Gartner’s website.
Indeed, we are all witnessing the growth in popularity of AI solutions in recent years. Driven by the rising adoption of natural machine learning tools and cloud services, AI in customer service is increasingly playing a vital role in transforming service operations.
So, it is particularly insightful to read Gartner’s point of view on this matter.
The use cases
We all agree there exist many use cases and compelling benefits that can be associated to the technology. Probably the list of potential use cases is even too large and make it difficult for organisations to decide where to invest.
To be fair, some vendors did not really help for a very long time. They were mainly interested in starting AI projects as quickly as possible, whatever they were – beneficial or not to the customer – with the aim of being the first on the market. Early positioning was key.
Thanksfully, Gartner is limiting the potential use cases to “three main ways”, somewhat high-level, but still a good first step.
Unsurprisingly the author argues that customer service and support executives should use AI for customer insights, User Experience improvements, and for optimizing and automating business processes.
This is a pragmatic statement to start with.
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The challenges
What the article does not really address however, are two very important elements: Firstly, the challenges most organisations are facing today in regards to data and analytics in general, and, secondly, how to actually move forward with AI-enabled digital transformation.
My own experience of AI projects recalls the utmost importance of those points for our customers. In most cases, their current situation shows significant operational inefficiencies and often a low maturity level.
To mention a few… the lack of single view of the customer relationship, inconsistent service levels, the high volume of handoffs across channels and units, the servicing through multiple disconnected systems, the lack of automation, the limited self service capability, the lack of accountability for end-to-end processes, and so on.
Too often, organisations rely on their employees’ skills in order to compensate for those process or system inefficiencies. This is costly and risky. Obviously.
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The way forward
It feels like I am drafting a dark picture here, but we should not be pessimistic and over-emphasize those issues. Most firms are actively addressing them. This is extremely positive.
In our daily work, we are helping our customers understand how to leverage AI technology to reflect the priorities of the board and drive value.
- How can we reduce service costs?
- How can we drive more revenue through our contact centers?
- How can we leverage technological advances in running our customer service operations?
- How can we provide differentiated service?
Moving forward, we help them develop their capabilities, all supported with platforms that are more cognitive and insight driven, like Salesforce Einstein, Pega, or IBM Watson for instance.
A first sign for me is the large number of our customers who have already adopted AI and machine learning to turn prospects into customers, drive product purchases, improve retention rates, and improve customer experience.
Another evidence is that voice recognition systems, conversational-AI / intelligent customer support robots are being widely adopted to automate customer services and to provide self-service functions to respond to routine interactions and customer queries.
Obviously, the adoption of AI technology is facilitated by the growing adoption of natural language processing (NLP) capabilities, improvements in data storage capacity, and processing capabilities. We observe this trend in many industries such as healthcare, financial services or automotive.
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A final word regarding the Gartner’s article is that it is true to say the search for cost reduction, through automation and efficient processing of service requests, is indeed fueling the demand for AI solutions. The COVID-19 pandemic has probably pushed the rate of adoption even further.
But it is also accurate that, to attain the promises – Improving productivity without compromising service quality – most organizations have specific challenges to address in parallel, one of them being how to efficiently and wisely include AI into their digital strategy and operating model.
The Gartner’s paper clear-cut is to bring the AI discussion onto the table of most steering boards for reflection. Undoubtedly
About the Author
Didier Dessens
CRM and Digital Experience Advisory
