However AI is a term that’s used too freely. It has become confused with machine learning, losing its original meaning of a completely independent decision-making engine. True AI would make decisions based on so much information that you couldn’t know the decision it’s going to make before it makes it – much like a human being. In contrast to that, current machine learning applications are very controlled – they put a set data flow through a known algorithm which auto-updates based on the results it receives, but they’re nowhere near the level of independence required for true AI.
It’s become the done thing in the last couple of years to espouse AI as the answer to all of businesses’ problems, from production line to marketing department. The rise of automation as a hot topic – and a going concern – has also dovetailed with the growth of customer experience as a key differentiator for businesses, as growing analytical abilities have driven increasing levels of personalisation and ‘value-add’ services peripheral to the main offering. Add all of that together and it seems that AI is the obvious answer to the need for a perfect customer experience – the more you know and the faster you can analyse it, the more likely you are to deliver a positive customer journey.
The dangers of bias
There are good business reasons why AI remains in the realm of hype rather than frontline use, though. Financial services firms aren’t using AI, for example, because they can’t control its results – there’s too high a risk that mortgage applications could be affected by unconscious bias, for example.
A good example of this played out a few years back when Amazon attempted to take the human element out of hiring. It took data about the type of people it had previously hired, processed it and then set the resulting algorithm to hiring people. However, it ended up only hiring men - because that was what Amazon had historically done. It discounted women based on past experience – and therein lies the problem. AI based on past data doesn’t let you change for the better.
This kind of coded bias is also indicative of the people who’ve made the algorithm - if it’s a non-diverse group designing the algorithm, they’re more likely to almost accidentally code bias in. This is one of the main reasons why pure AI isn’t often used - the data you have might be biased, and you won’t know until it’s running. If it goes wrong, you can’t unpick it. Think of it like a child growing up: once they’ve picked up negative behavioural traits, it’s very difficult to undo the hardwiring.
Data volume is key
Despite all this, genuine AI is a possibility. The key is ensuring that applications have access to sufficient data – and the right kind of data. The more data you have, the more likely it is that you can build something that will provide accurate, non-biased results.
Unfortunately, we’re simultaneously seeing a sharp decrease in willingness to share data, as scandals like the Cambridge Analytica debacle damage confidence in the safety and privacy of large-scale data sharing. As a result, businesses have to navigate a very fine line between feeding enough data into their automated systems to achieve an optimal, non-biased customer experience and preserving customers’ privacy and trust.
Take the worked example of calling HMRC. As most of us will know, it can often take forever – it’s not a positive experience. To solve that problem, HMRC recently announced a desire to use voice biometrics for identification to speed up the process and remove the endless questioning. The system would be able to tell your mood, quickly identify why you’re calling and put you through to exactly the right person to solve your problem.
The idea is a good one, but it depends entirely on people being comfortable sharing their voice data. Without that data, how do you optimise the system? As we move away from data-sharing as the default, how can companies still optimise their customer experience with AI-driven technology?
Cover the basics first
- The customer is always right: The importance of keeping the customer experience at the forefront of digital transformation
In a nutshell, the answer is preparation. Lots of organisations come to my practice and say they want their customer journey to take advantage of AI and machine learning, but often they don’t have the tools in place to support it - no security, no data automation and no clear understanding of the business need they’re trying to address.
They’re starting from the wrong point. Technology is not the end goal – the customer experience is.
Companies need to start their AI CX journey with basic identity and access management. They need to inform their customers that they value their data and their security, and that they’re going to use their data with set controls in order to then keep them informed and give them proactive customer service, say. Transparency is key – and you have to have the technology in place to back it up.
In short, deciding that you want to use AI before you’ve established a secure base on which it can function is a mistake. Start by finding out what your customers are asking for and therefore where you can add value - don’t start with AI, start with the customer value.
Ajmal Mahmood, propositions lead - customer contact, KCOM