Expecting an AI model to handle specific unique requests and solve complex problems that your customers might experience is simply unrealistic.
AI automation has penetrated corporate life in more ways than even the most optimistic proponents dared imagine. One of the areas where it has definitely secured a foothold is in customer service, to varied success.
To cut a long story short, there are a few pitfalls of AI automation in customer service and the goal of this article is to inform you about them, as well as to provide a few tips on how to avoid them.
Not tempering your expectations
Artificial intelligence and its use in the business world have been hyped for such a long time now that many people have become completely caught up in it, expecting every kind of AI automation to be this silver bullet that will get rid of all their problems and turn their company into a Fortune 500 member.
When we are talking AI automation in customer service, there are a few ways in which unrealistic expectations can result in total disappointment.
For one, some adopters expect the simplest of AI models to be this sci-fi thingamajigs where a sentient robot handles every customer inquiry as well (or better) than a human customer service agent can. This is simply not the case. We are still a long way from AI customer service that will be able to handle conversations with the same responsiveness and agility that humans have.
More realistically, some people get disappointed that AI automation does not bring the return on investment that they expected going in. Once again, they expect far too much from something that can improve their customer service, but which is not this absolutely revolutionary thing that it is sometimes hyped for.
Temper your expectations. AI automation is best used as an additional layer to your customer service strategy.
Going huge straight away
There is some sense in trying to go all in and attempt this overarching, comprehensive AI automation strategy for customer service straight away, setting up multi-channel coverage, piping it straight into your CRM software (probably with another layer of AI capabilities), assembling an entire (superbly expensive) data and AI training team and so on.
In reality, however, for most companies (including the not-so-small ones), taking it step by step is usually the best course of action. Trying on a quality chatbot, setting it up and then moving on from there is a much better solution.
By taking things slowly, you are reducing your initial investment, meaning that if your new customer service ally doesn’t work out, you are not wasting too much money. Also, it is much easier to tinker with and modify a smaller system and try out new things.
This will also make an analysis of the ROI much easier.
Taking humans out of the equation
The next trap is something we hinted at when we talked about the unrealistic expectations that some people have of AI when used in customer service. Namely, due to certain misconceptions they might have of AI, they believe that they can put all of their customer service into the hands of an AI.
This simply does not work.
Even the most advanced AI models used in customer service are still very limited and they are capable of only very limited improvisation when conversing with customers.
For instance, a well-trained AI model will be able to recognize questions that resemble those that you would find on a FAQ page and serve an answer or initiate a Q&A sequence where the customer will get an answer after few more steps.
However, expecting an AI model to handle specific unique requests and solve complex problems that your customers might experience is simply unrealistic. However, what you can do is to set up your automated AI customer service system to recognize such situations and guide the customer to the right human customer service agent who will then be able to solve that issue.
In essence, you will want to let your AI handle more basic customer service requests and free up your people to handle the more complicated ones. The end result is that every customer will be provided with information and solutions quicker.
Setting it up and leaving it
Another trap that plenty of organizations fall into when adopting AI automation for customer service is that they see it as this out-of-the-box solution which they just set up and leave running without ever coming back to it and modifying it.
Anyone who knows the first thing about AI can tell you that this is not the way AI is to be used. AI solutions, even those that are sold out of the box, need to be maintained continuously. Of course, at first, you will be spending huge amounts of time training.
Later, you will not be spending as much time with them, but you will still need to stay involved and analyze their performance.
For example, you might find out that your customers are using certain expressions and semantic constructions that you didn’t foresee and that you need to modify your model so as to better answer such inquiries. Or, you might discover that your customers have troubles understanding what is asked of them at a certain step in the conversation and you will need to modify your model.
Of course, we must not forget about one of the most harmful things for any AI model and its application – bad data.
Besides the standard problem of bad data inputs which are common for any system that operates with large data sets, AI introduces the so-called concept drift where data is changed as the result of being used by an AI model.
Handling all of this tells you that you cannot just leave your AI automation to chance once you set it up. Instead, you might need a more hands-on approach.
Adopting AI automation for customer service can definitely be a good thing for your organization.
However, it is essential to temper your expectations, go small at first and make sure you are using it the best way possible (as additional support for your existing team(s)).
Michael Deane is one of the editors of Qeedle, a small business magazine. When not blogging (or working), he can usually be spotted on the track, doing his laps, or with his nose deep in the latest John Grisham.