Generative AI has taken the world by storm. Affecting the way we work across industries, departments and teams for better or for worse.
Let me take a step back. What is Generative AI and how is this different from the traditional AI we’ve become familiar with?
Generative AI is a type of artificial intelligence capable of generating new content such as audio, text, code, images etc based on training data. This differs from traditional AI which tends to be tasked based using rules based algorithms.
Traditional AI has been a component of customer service for some years with many companies developing a strategy of how to leverage AI to improve both the customer and employee experience. This is mostly seen in self service bots, forecasting tools and agent productivity tools.
Throughout the history of traditional service, the ultimate aim of a customer interacting with a company hasn’t changed. Customers want to have their question or issue resolved in a seamless, friction free, convenient and quick way. This means that they will occasionally prefer to research themselves or may choose to reach out to a customer service representative at their convenience.
Many of the companies I’ve interacted with in the past are actively seeking ways to satisfy customers’ need for convenient and seamless service in-spite of the challenges they face in todays climate. Challenges such as :
- lack of skilled agents
- inability to keep up with customer expectations
- need to scale as the business grows
Implementing chat bots and leveraging conversational AI has been a familiar method of meeting the typical challenges mentioned above while reducing the cost to serve.
According to Gartner, 54% of companies interviewed had implemented a chat bot to enable self service capabilities.
Also in a recent BCG survey of global customer service leaders, it was found that 95% surveyed expect their customers to be served by an AI bot at some point in their customer service interactions within the next three years.
With the focus and promise of Generative AI, there is cautious interest in adopting this technology with an aim to further improving self service capabilities and agent efficiencies. This of course includes extending existing conversational AI capabilities as well as exploring other use cases such as case or conversation summarisation, knowledge management creation or draft responses for agents to use, which previously would have required significant financial investment.
Building your own LLM to mimic what’s possible in Open AI would require significant financial investment to set up and run, require storage space that many cloud providers would struggle to provide and is likely to have issues with latency due to this.
Therefore, there is great advantage of leveraging generative AI services such as Google’s Bard or Microsoft’s Azure Open AI which when used responsibly can make significant impact on the experience of both customer and employee.
The promise of Generative AI isn’t without its concerns. In my next blog, I’ll expand on the concerns surrounding Generative AI and how we may responsibly use it.
Also – since you’re here 🙂
I’ll be speaking at the next Nordic Summit being held on the 23rd September, 2023 in Copenhagen. I will be delving further into this topic and also demonstrating how Microsoft’s co pilot can enhance the customer service experience for both customer and agent. Register for the event here and please stop me for a chat!
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