Augmented Intelligence is more intelligent than Artificial Intelligence
Most experts define artificial intelligence as technology with the capability of thinking of itself and making decisions based on its own ideas. It’s going to be years before this becomes a reality, if it actually does…as some experts argue. More philosophically, why would we want computers not to require guidance and input from time to time where the situation is new or uncertain? Particularly when dealing directly with customers.
What we should be using and aiming towards is Augmented Intelligence, that is, man plus machine (rather than man versus machine). The definition of this is inherently vague but essentially it is where software supports human decision-making and actions, and when it carries out repetitive or known tasks but defers to a human for more complex or unique ones. Unless one is familiar with the state-of-the-art of the technology, it is easy to believe the hype. The reality though is that even the most sophisticated AI applications ironically require armies of data scientists to develop and maintain them. For many, the Holy Grail in Augmented Intelligence is an application that is trained and guided by a non-data scientist, in particular, so that the front-line personnel are not directly doing all the tasks, but they are guiding the bots which help them.
The latest technology being used to analyse customer data uses machine learning to be able to classify interactions accurately to guide processes and generate actionable early warning insight. The first trick is that it ‘understands’ when it is uncertain about something, and it invites a human for assistance (sometimes this is referred to as ‘human-in-the-loop’). The human doesn’t need to be a data scientist, they only need to understand the domain to impart their judgment and knowledge to the machine and once it’s done, it’s done forever. The second trick is that it does this in an optimal way, asking for the minimal amount of input from a human to maximize the performance, and therefore the accuracy, of the machine learning. In this way, it can process more and more tasks to an acceptable threshold accuracy, and hand off to a human seamlessly when it is below this. PrediCX from Warwick Analytics is a perfect example of this.
Let’s take chatbots for example, they need to be curated to constantly improve and learn to new and changing signals from customer intents. There is no reliable feedback loop, even if the customer ticks “helpful” or “please can I speak to a human”, there is a lot of things that could go badly wrong to use this for training and maintaining. Further, it is critical to understand and classify the topics being talked about across all channels, to encourage and facilitate the right channels for the right topics, i.e. self-service/chatbots for FAQs and for complex queries to be quickly routed to a human on the ‘phone, with chat and other semi-synchronous channels perhaps somewhere in the middle. The latest Augmented Intelligence facilitates an ongoing virtuous circle of harmonized classification across all and any channel, to break down silos, improve internal processes, save costs and most importantly optimize customer satisfaction.
So in conclusion, AI is here and very much here to stay. However, AI is Augmented, not Artificial Intelligence and for the foreseeable future, if not forever, blends the best of machines with the best of humans to make the perfect customer experience.