It’s time for artificial intelligence to take its place, front and centre

29th June 2022

by Richard Billington

We often discuss the advantages of intelligent automation and the possibilities that open up when processes are digitised and automated through the use of new software applications and automation technologies. In those existing tools, artificial intelligence (AI) is already playing an important role. Whether it’s providing computer vision to help a bot ‘read’ a screen or natural language processing to understand the contents of an email, AI provides valuable utility and the uses for it are growing all the time. You can find out further information about what is AI and the benefits by viewing our feature.

Very often, process and workflow decisions need to be driven by the data and, as it grows that’s becoming an ever more challenging undertaking. We’re 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 with 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’s 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.

It’s time for AI to take its place front and centre.

What is machine learning?

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. That’s what our flavour of intelligent automation makes possible.

AI put into practice

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

Now that you have these insights, what can you do with them?

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.

Now you can make intelligent automation truly intelligent.

Automating processes in this way drives efficiencies, lowers costs, increases capacity and delivers a better customer experience.

What’s the alternative?

Liberty opens up this ability to create bespoke ML models, trained on your data to provide insights that can drive the enterprise forward with improved and automated decision making. And it makes it easy for business users with no specialist data science skills to do this.

This opens up exciting new possibilities for AI and advanced intelligent automation and we’re excited to see how customers take advantage of it.

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It’s time to understand and action your data

AI is baked into the Liberty Platform, playing an important role in digitising and automating processes.