By Richard Billington, Chief Technology Officer at Netcall
Intelligent automation is changing the way businesses work in many sectors by detecting and producing vast amounts of information, artificial intelligence (AI) can automate entire processes or workflows – learning and adapting as they go. From collecting, analysing, and making decisions about information and data, to guiding advanced robots, AI is rapidly revolutionising every commercial process or industry worldwide.
Very often, process and workflow decisions need to be driven by the data and, as it grows that is becoming an ever more challenging undertaking. We are long used to building rules into our apps and basing decisions on the data. Using an ‘if this… then that’ approach works well. However, as the amount and the complexity of the data grows, that approach is becoming more difficult. Whilst you can build rules upon rules (and there’s no technical limit how far you can go), when you get to four or five levels of rules, it becomes increasingly difficult for our brains to understand and follow what is going on. It’s similar to working on a spreadsheet model where our brains struggle to cope with juggling more than about five variables that are impacting the outcome or result.
With the vast amount of data we record, and the complexity of data relationships growing all the time, we need a better way of making sense of the data. That’s where AI and in particular machine learning (ML) comes in.
Machine learning, what is it and how can it be used?
ML is the application of artificial intelligence to understand and learn from large amounts of data. ML models can be created that use complex mathematical algorithms (neural networks) to manage and make sense of very large, complex data sets. In the past, ML models have been trained on very large, anonymised data sets to provide capabilities such as speech or facial recognition that are made available over the internet for widespread usage.
However, ML can now be used to address specific data challenges for enterprises. It’s now possible to build and train your own ML models with your own historical data and use those models to predict likely future outcomes.
How can AI be used?
For example, let’s say you’d like to determine the likelihood of a patient missing a scheduled appointment because each missed appointment costs money and you want to minimise their occurrence. Traditional processing would use a set of rules to decide whether a person might miss an appointment. However, an ML model trained on historical appointment data, would predict on its own, the likelihood of a patient missing an appointment. It does this by learning from historical data and creating its own rules with no limit to the number of factors it might consider. You then use this result to determine actions to be taken, such as offering the patient a different time or location for their appointment that would deliver a higher prospect of them attending. This will reduce the number of missed appointments, deliver a better experience for the patient, and save the hospital significant amounts of money.
AI can now play an important role in helping understand your business better through gaining insights from the vast amount of historical data collected. Whether it’s:
Predicting the likelihood of a customer or patient missing an appointment
Identifying those struggling to pay council tax or that might fall into rent arrears so you can offer support earlier
Predicting the impact of promotions and pricing or demand into the contact centre
Detecting fraudulent claims at the start of a claims management process
Identifying those customers at risk of churn
Or many other use cases
How the insights created can be beneficial to the business
Predicting future outcomes can be presented to those who need to make decisions with the data. They can be used to make it easier to understand data being presented (e.g. colour code on-screen data based on likely outcome). More than that though, these predictions can be used to drive the next step in a process or workflow automatically within an app or an automation.
Automating processes in this way drives efficiencies, lowers costs, increases capacity, and delivers a better customer experience.
Ultimately, AI and ML are two of the most potent protagonists of digital transformation and the basis for the most efficient digital tools today. They are enablers of increasingly innovative and effective solutions directly impacting the market’s acceleration and competitiveness, and customers’ experience and expectations.