Why UX Design is Vital to User Satisfaction and Ongoing Job Security

Robots are not new. Henry Ford was one of the first to realise the advantage of taking robots out of humans. In other words, gatting machines to do the boring, repetitive tasks done until then by people.

Today we need to consider the digital workforce as also an HR challenge and not (just) a technical one. Humans are not upskilling and progressing as fast as robots are. This is the real cause of any work losses that may happen as automation rolls out.

THE FUTURE OF WORK

Now that I’ve touched on the elephant in the closet, that of job losses, let’s talk about employment. The future is not so much about replacing workers, as in expanding and amplifying their work through the use of AI. And again the trend will only be amplified as businesses look to make serving their customers safer.

The future will be a world of work plus AI, not work minus AI. When, not if, robots take on many of our current tasks, people will need to supplement their knowledge with soft skills, ones that AI can’t replicate, at least for now. This is why I, like many others, refer to AI as augmented intelligence rather than artificial intelligence.We are not replacing people but increasing their capacities in many areas.

One area that will certainly need a tremendous amount of human input is in speech analytics. You probably don’t realise it, unless you’ve learnt another language or two, but speech has enormous diversity in the ways to say the same thing. Just ask any owner of Alexa, Siri or Cortana! Sometimes their responses are hilarious, at least at first, but these quickly become irritating when you can’t make yourself understood.

For me, this is when I am trying to get my BMW to call someone. The proposed people are rarely the person I am wanting to speak to and more frustratingly, the list of names have nothing in common with either the spelling or pronunciation of their name. Perhaps it would be easier if I spoke German!

If robots are to understand humans, then the alternative expressions need to be programmed in, before being understood. Although machine learning may speed our progress, the foundations must be identified and created by humans.

AI AND CARE CENTERS

Most businesses have customer service departments and many are jumping on the bandwagon of requesting AI. However, most don’t really know why they need it! The case for AI has to be put into terms of its business impact and relevance in order to be valued beyond mere “modernisation.” Just ask anyone who has chatted with a bot or gone round in circles clicking numbers on a self-service phone line! So many corporations today have increased their technology but have not improved their customers’ satisfaction.

AI is already proving to be of great value in following and analysing customer service connections. A supervisor can’t listen in or read every exchange, but AI can. However, as previously mentioned, understanding speech is still in its infancy, especially when it comes to sentiment. An agent will quickly sense when something is wrong or an answer is unsatisfactory, even when the customer is saying everything is alright.

The sequence of events that led to the customer’s connection, is just as important as the call to customer services itself. This is where total integration of all touchpoints is vital. The customer already sees them as such, but most companies do not. This leads to irritation when a customer must repeat their details and experiences with each new customer service agent.

It could be so easily eliminated, by simply integrating multiple data sources and then assessing the customer’s “effort” in getting the answers they are looking for. The greater the effort has been, the quicker a solution should be found and ideally, it should be more than the customers expect. This would be when surprise and delight become absolutely essential.

I believe that not taking the customer’s perspective here is the root cause of this less-than-satisfactory situation today. Once again, adopting a customer-first strategy is the answer.

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Customers in developed markets already have far more interaction with AI than they probably realise. However, when developing chatbots it is important to allow for far more variation in the words people use than we usually think of. The challenge is not only understanding the variations in vocabulary, but also the jargon, colloquialisms, spelling mistakes, acronyms and alternative expressions. This is one area where I believe we still have a long way to go in UX design.

Instead of aiming for perfection, by brainstorming all possible variants, our time would be better spent in identifying the 20% of variations that cover 80% of the cases. Ideally, we should first collect the information on the words people use and the way that most express themselves when speaking about a topic. Only then should we analyse what the company is most likely to receive and integrate the additional phrases. Perfection is once again the enemy in progressing the use of chatbots.

We also need to be transparent about when chatbots are being used. It may be a good idea to make them respond in a friendly way, but pretending to actually be a human is not a good idea. Customers will eventually understand that they are exchanging with a chatbot when the responses they are getting do not meet their expectations. That can only lead to frustration and perhaps even irritation that they have been cheated in some way.