Just as the Industrial Revolution transformed the world during the late 18th and early 19th centuries, the ‘AI revolution’ will engender an equally far-reaching change in the coming years. AI has reached a point where it is capable of surpassing human decision-making in many situations – consistently, accurately and continuously. But why is this revolution happening now, and how do businesses harness this capability?
AI has been around since the 1960s, but only now is the necessary confluence of three key factors coming to fruition. First, AI was initially dismissed by industry commentators as a ‘disembodied brain in a jar’, isolated from real-world situations.
Today, however, we have reached the point where digital channels are becoming the norm and provide the brain with all the senses and limbs it needs. Our customers and suppliers communicate with us electronically, so it makes little sense to introduce a human into the loop to make decisions.
The AI revolution is set to take the service industry by storm, changing the role of the white-collar worker irrevocably
The second major change is the availability of data – the new ‘digital fuel’. Data lakes are popping up everywhere, application-programming interfaces are available on the internet allowing us to access all manner of data, and we (as individuals and businesses) are generating a huge but insightful ‘digital exhaust’.
Finally, just as steam engines were fashioned out of steel to improve efficiency and reduce production costs during the Industrial Revolution, the increase in cloud computing capacity has allowed large swathes of data to be stored for mere dollars within a matter of seconds. As a result of these developments, the AI revolution is set to take the service industry by storm, changing the role of the white-collar worker irrevocably.
Race to embrace AI
Organisations are scrambling to become AI leaders in their respective fields. All manner of companies are searching for opportunities to automate or augment numerous decision-making tasks by using more information in real time. But the starting gun has already fired and a leading pack has emerged, with the likes of Amazon, PayPal, Google and Facebook stealing a march on the rest.
And with their low cost bases, seamless, real-time customer interactions and competitively priced products, these companies could become impossible to catch if competitors delay further. These stakes are compounded by governmental regulations such as the UK’s Open Banking initiative and the EU’s Second Payment Services Directive, which are driving competition in this space.
AI can generate significant insights into a company’s customers, suppliers and business relationships by utilising huge volumes of seemingly disparate data. This underpins the ability to make automated – but unique – decisions for each individual or organisation on a whole range of topics. The applications are potentially limitless, so it’s imperative companies adopt a proactive, rather than reactive, approach to AI.
Imagine being able to increase your customer prospects by 80 percent, simultaneously making them more targeted and pre-qualified without adding to your workload. By combining global lists of businesses, directors, investors and shareholders with your existing customer base, AI systems can do just that.
AI systems will study the characteristics of your best customers, scour the globe to find similar businesses, and rate them based on a whole range of factors before presenting you with the best opportunities. They will even go as far as telling you which of your current customers could make an introduction.
AI systems can also help manage compliance, reducing the hefty costs financial institutions face when employing tens of thousands of staff to perform Know Your Customer checks and anti-money-laundering transaction monitoring.
This financial outlay is often exacerbated by public reaction to negative news stories, such as the Panama Papers leak in 2015. However, by providing a full contextual overview of prospects and customers, and by linking data across numerous sources, AI systems can automatically classify potential criminality far more accurately than traditional rule-based systems or humans following guidelines.
In fact, through the identification of illicit money movements and the improvement of false-positive rates, AI can reduce staff effort by up to 70 percent.
In addition to the numerous business benefits, improvements in compliance will also help to minimise the funding of organised crime, reducing incidents of human trafficking, terrorism, radicalisation, corruption and inequality through tax evasion.
Put simply, by providing more contextual information on the businesses being assessed, AI can identify and predict risk more accurately. After all, you wouldn’t choose to buy a house by simply looking through the letterbox: you would go inside, look around the neighbourhood, compare it to similar properties and then make your decision.
Seeing eye to AI
In order to keep pace, businesses must prepare their data properly. While many organisations understand that data provides fresh insights into both businesses and customers – employing chief data officers and setting up data lakes – not all have realised the value of adding automated AI decision-making capabilities. People also panic about data quality.
They shouldn’t – it will never be perfect. AI is a game of statistics, and you can use quantity to overcome quality. Just remember that context is critical; the AI engine needs to be fed with networked data that provides the full picture.
For example, you shouldn’t assess a claim in isolation when you could look at a network of connected claims to prove whether it is genuine or part of an organised fraud ring.
The second step is understanding AI. Many businesses confuse AI with robotic process automation, which is a basic capability that introduces another computer to automate existing legacy systems by following the same rules a human operator would.
AI, meanwhile, is a step change to achieve superior decision-making based on deeper insight and more data. Don’t think of AI as simply being machine or deep learning: AI is more effective as a combination of techniques.
The third step involves identifying the right problems. Organisations that have struggled with AI will likely have either applied it to an unsuitable situation, prepared the data incorrectly or alienated the user.
It is very important, therefore, to carefully select problems that can be solved by the available data. To solve the user interaction issue, you have to move past the belief that AI is purely a machine-learning tool providing yes or no answers. Engage users by ensuring the interface presents the full picture and explains the reasons underpinning its decisions.
The final step is ensuring you have a well-structured programme in place. You need streams of work to underpin a series of AI pilots and projects, such as data acquisition, data science skills, data lake environments, IT engagement, pilot-to-production processes, awareness and communicating success. Don’t wait for the race to be won: make a start today, take baby steps and reap the benefits of AI success.