Measuring up: robotic process automation versus real-time decision-making
With pressure constantly on profit margins, organisations need to strike a balance between improving cost efficiencies and customer satisfaction. This has led to a growing interest in the rapidly advancing area of robots to create a virtual workforce; transforming business processes by automating manual, rules-based, back-office administrative processes. This also has benefits for future workers – there’s now a strong argument for reducing workers’ pension age as a result of boosting UK productivity through artificial intelligence (AI).
The benefits of robotic process automation
The re-engineering of processes through automation, known as robotic process automation (RPA), is being applied to a wide range of industries to improve speed, quality and consistency of service delivery.
There’s no question that RPA technology provides a number of straightforward and powerful business benefits. It can automate repetitive tasks that are often undertaken by humans, significantly cutting costs, while improving the consistency and quality of the process.
RPA is best used on manual, high-volume, repetitive and rule-based processes involving structured data, such as transaction processing. The financial services sector, for example, has seen higher rates of adoption than other industries.
Some people often use the terms RPA and AI interchangeably, but this is incorrect and leads to confusion. There is a clear need for clarity and awareness around the terms and technologies themselves, and how they can benefit businesses. After all, while RPA is an advanced technology, it does not have ‘self-learning’ capabilities.
For example, an RPA robot could be tasked with monitoring what is happening say, on an advisor’s desktop. It can register where they clicked, what systems they accessed, what they did next. Based on this data, the system can recognise and mimic the processes carried out by humans – along with their mistakes!
The key difference is that the robots designed for use in RPA are not ‘smart’ or ‘self-learning’. They are limited to the process they have been assigned to and how they have been programmed to carry it out, and their job is to repeat that process over and over again, rather than autonomously refine and improve it.
This RPA approach is fine when you have rules-based processes where compliance to the process and accuracy are the most critical factors. However, where there is any ambiguity, usually when the inputs into a process are unstructured (such as customer emails), where there are significantly large amounts of data, or where you want to personalise the outputs for each customer, then analytics and AI is the appropriate technology to use. This is because it can manage that variability and, most importantly, determine the most profitable outcome for the business.
A system that is capable of managing and executing real-time decisions through AI is the best option for automating complex decision processes. Using advanced analytics and machine learning allows such systems to become intelligent and constantly adapt and learn from new, contextual data.
Consider RPA as an answer to integrating processes – taking the pain away from businesses and workers by automating the much-needed, but fairly manual repetitive input, cut-and-paste type work. A step up from RPA is a real-time decision-making system, applying AI-based analytical decision-making to highly complex problems in real time. This reduces the need for highly skilled employees to make decisions that take time, such as underwriting, or for fairly skilled workers that make complex decisions that are not as accurate as could be, such as claims, for example.
Real-time decision-making is able to analyse thousands of business inputs, constraints and options and arbitrate between multiple internal targets and trading decisions. It can apply an analytical constraint-based optimisation approach to models across thousands of possible actions – whether it be specific content, an offer, a price, sort order or a recommendation – and determine the best outcomes for the organisation. It can then qualify those decisions against certain criteria – be those from a profitability, risk or revenue perspective. Leveraging that built-in decision-making engine means it can optimise decisions based on contextual data and inject the optimum decision into business processes. All this can be done in real time, at the moment a customer is interacting with you.
Lessons learned from other systems
A similar scenario happened with enterprise resource planning (ERP) systems. Yes, effective ERP implementations enhanced the competitiveness of many companies, but a greater number of companies found the experience more of a nightmare. The rules-based approach meant that once implemented, most ERP system were unable to adapt to ever-changing organisational processes and thousands of exceptions to the process without a constant rewriting of rules and links.
Introducing the concept of ‘intelligent analytical automation’
Whilst ‘intelligent’ solutions like those that can perform real-time decision-making are very different to RPA solutions, it’s clear that the two technologies complement one another. By combining RPA with AI technologies, organisations can take advantage of ‘intelligent analytical automation’. This will enable businesses to complete repetitive, rule-based processes as well as deliver bottom line benefits through automated complex decision-making capabilities that previously needed human labour.
Intelligent automation also reduces reliance on human intervention for complex decisions. This significantly increases productivity and efficacy, as well as allowing for faster, more cost-effective business decisions and actions in real time. RPA delivers benefits around cost reduction and process consistency.
Think about the additional percentage improvement in risk accuracy, margin, churn and customer lifetime value that could be delivered by automating complex decisions. It’s a no-brainer!
Tiffany Carpenter is Head of Customer Intelligence at SAS UK & Ireland, a leader in innovative analytics.