Well implemented AI delivers both operational and competitive success

That’s the dystopian vision… but savvy business leaders are increasingly aware of the need to embrace AI and tap into its potential to transform the way they operate for the better. We work with a number of major companies to understand how they can use AI to their business advantage and implement software solutions to suit their individual needs. And yes, it’s about transforming, not simply changing like for faster like.

We begin each project by testing for six requirements that apply for the AI to be truly valuable.

• Find the proverbial needles in the haystacks

The first requirement for AI is the ability to sift through vast sets of complex data or ‘big data’ and draw out valuable information. To do this without human prompting or supervision, as ‘unsupervised learning’, it needs to be programmed with the capability to break the data down according to recognised parameters and then to sort the data accordingly in order to make the discoveries required for effective modelling.

The obvious advantage of AI over humans in this respect is the machine’s ability to quickly investigate millions of records in detail, yielding a far more accurate overall picture. For businesses that process large volumes of data, such as a financial services company that carries out millions if not billions of transactions, AI revolutionises the scope and depth of analysis that it is possible to carry out.

• Value AI’s predictive powers

Second comes the need to make predictions based on the data. A prime example of this is weather forecasting, which has been using computers for decades to match weather patterns with past weather events and learn from accurate and inaccurate forecasts to fine-tune the accuracy of new forecasts. The boom in computing power and AI has taken weather forecasting to new dimensions, enabling far bigger data sets to be processed faster, for ever more accurate forecasts.

In business, the same principle is being applied to behaviours such as data security breaches, where machines scan for patterns and anomalies in user behaviour and predict where an intrusion is likely to occur. As organisations gather and store ever-greater volumes of personal data, this is becoming increasingly important for mitigating risk.

• Deliver justification of predictions

It’s not enough for AI to detect and predict, it needs to be able to justify its findings to be credible. It can’t be left to human operators to look for the arguments behind each prediction or suggestion that AI puts forward – the machine must be capable of presenting its own arguments. This is where the autonomy of AI really comes to the fore. Like any good employee, it must be ready to act off its own bat to carry out its tasks and to justify its findings to management.

• See machine learning as an essential
  A further ‘human’ trait required of AI is the ability to adapt and learn as the landscape changes. Data sets are evolving constantly and a set of rules that applies on one day may be irrelevant the next. This calls for a degree of self-awareness – the ability for AI to monitor its own performance and rules, recognise the need to evolve as the underlying data distributions change and make or recommend the necessary changes. This trait is crucial for future-proofing and enabling businesses to scale up and move into new areas.
• Make AI ‘live’ through rigorous testing

The testing of AI is required to ensure its systems have the ability to act and in itself requires new skillsets. Most AI experiments tested for effective discovery, predict and justify functionality fail to make it to the real world – so test and test again!

• Generate not just faster, but totally new insights

In practical terms, the reporting function is a significant step forward facilitated by AI. An AI enabled organisation can distribute reliable data reports across the whole organisation, in an instant, from a central view, with analysis taking place in real time. Machine learning can be used to replace the tedium of monitoring, with insights brought to a manager’s attention through a feed, with different viewpoints powering actions, backed by insights.

Financial institutions are leveraging AI in this way, to improve the way they meet regulatory reporting requirements. For example, Digiterre’s centralised AI solutions collect trading data and present it to view, analyse and report across organisations. They are built to satisfy not only current day needs but are scalable and adaptable to accommodate strategic plans for growth, and at the same time, accommodate the uncertainties this brings.

Meeting regulatory requirements through AI can create new commercial opportunities

By addressing and meeting the six requirements shown, such AI solutions not only enable companies to meet their regulatory obligations reliably and efficiently, but also offer them a competitive advantage, now and for the future, by enabling intelligent real-time, business-wide analysis of data flows and by prompting new commercial opportunities. Furthermore, management can sleep that much better at night knowing machine-learning solutions are flexible enough to tackle unknown future trading volumes.

AI will inevitably replace some human roles, speeding up the more mundane, processes, increasing their efficiency and effectiveness but more so by doing the work that people cannot do. It’s not a binary switch, but a continuum of technological sophistication that leaves the workforce with more time to focus on their areas of expertise, to leverage the new world of AI and machine learning and to open up new areas of business opportunity. Rather than being rendered redundant by AI, it’s the way we all learn to work with and optimise AI that will determine how our businesses evolve in the years to come.