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, instead of Artificial Intelligence then, 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.
How can businesses develop machine learning models which automate processes not just today, but reliably ongoing? How can they get continually rich insight from models when the data are changing around them? Is the irony that the data scientist cannot bring a model to life, but actually needs to constantly be the puppet-master: You can’t just build a training set of data and then automate, you need to keep feeding training data to keep the models up to date and prevent model degradation. Now the business problem is starting to emerge: You wanted to use AI to automate a process based on historic data so that you could free up the human resource that is currently being deployed in the process. But if all those humans disappear and you let the model loose and operationalise it, then ironically the source of fresh training data also disappears. The computer is left to mark its own homework. If the model is to be kept up to date, then you need to keep some or all of those humans around, and it isn’t at all calculable how many you need. This is the bane of a data science team, i.e. having to curate models and constantly be involved in refreshing and validating, when there is plenty of new data science opportunities to focus on to generate value. Also the curation process itself is almost always sub-optimal as there is no way the data science team would be able to second-guess if new signals have appeared without validation and involvement from users. Further, if the business process itself needs to change due to demands from the business, then the whole model could be left redundant with no relevant training data for the new model.
However, the latest in technology being used to analyse customer data uses machine learning to be able to classify interactions accurately to guide processes and generate early warnings of issues and trends. A perfect example of Augmented Intelligence. 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 judgement 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 maximise 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.
Warwick Analytics is one such company which has cracked this. It is a spin-out from The University of Warwick and it has developed software called PrediCX which uses machine learning to learn from various customer interactions to be able to classify them accurately to guide processes automatically.
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 harmonised classification across all and any channel, to break down silos, improve internal processes, save costs and most importantly optimise customer satisfaction.
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.