The Current State of RPA
When any innovative technology emerges, it can be difficult to separate the hype from reality. Robotic process automation (RPA) is no exception: while the market for RPA is predicted to grow 57 percent in 2019, other reports have found that current RPA tools are struggling to scale.
Do these discrepancies mean that RPA is all hype? Far from it. But to get a realistic picture of what RPA tools can achieve for your organization, it’s helpful to explore the current state of the technology and its adoption. In this roundup, we’ll analyze the current state of RPA by industry, including how various sectors are currently applying RPA tools and some specific challenges they may be facing.
RPA in life sciences
Rules-based tasks dominate many areas of operations in life sciences, such as clinical drug trials and sales force operations, making the industry ripe for improved productivity and cost reductions using RPA tools. However, many businesses within the sector are still in the earliest phases of RPA adoption, while others are lagging behind due to the stringent validation requirements in this highly regulated industry. As such, successful examples of RPA in life sciences – and their high level of auditability – will pave the way for greater adoption.
One of the most successful in-market examples of RPA in life sciences to date is an initiative by AstraZeneca to automate the onerous manual work related to “adverse event” reporting. Around the world, AstraZeneca Patient Safety teams manage approximately 100,000 such cases each year, a task that requires extensive paperwork and communication between AstraZeneca employees, patients and their doctors. But in automating follow-up activities relating to these communications, AstraZeneca was able to both reduce manual workloads and “free up resources to focus on value-add activities, and manage future workloads without increasing costs, compromising quality, or jeopardizing compliance.”
RPA in energy
Further ahead on the adoption curve, we are already seeing successful examples of RPA in energy, both on the front lines and in back-office functions. In part, this has been due to necessity – as oil prices have declined worldwide, RPA in energy has been viewed as one of the greatest opportunities to control costs and maintain profitability.
In back-office functions, for example, RPA bots have enabled energy enterprises to reduce their spend on audit functions, and also take on the day-to-day tasks of employees related to billing and payments. Doing so has resulted in fewer human errors and freeing up these teams to focus on higher-value work instead. By turning over their billing tasks to RPA bots, one utility organization was able to process significantly more service orders than their employees were manually, totaling an increase of 50 percent.
This rapid pace of innovation doesn’t come without risk though – if not applied thoughtfully, enterprises run the risk of automating inefficient processes and tasks, thus reinforcing them. This underscores the need to revisit underlying data on an ongoing basis as part of implementing RPA in energy.
RPA in insurance
With an annual compound growth rate of 60.5 percent and adoption rates of 93 percent, RPA is already among one of the most transformative drivers in the insurance industry. No longer seen as an innovation, RPA in insurance has become standard, allowing insurers to improve efficiencies and costs, better assess risks and identify fraud.
From the automation of onboarding and claims forms to policy cancellations and other key functions, the use of RPA in insurance is already delivering dramatic cost savings and efficiency improvements. For example, Zurich Insurance Group, Switzerland’s largest insurer, reported $1-billion in savings from the automation of 48 workflows by using RPA in insurance. Among the improvements seen, RPA in insurance reduced the cost of processing claims by 30 percent and shrunk payment windows for claims to less than a week, compared to the industry benchmark of 50 days.
RPA in banking
While RPA in banking hasn’t reached the level of proliferation of the insurance sector, banking and financial services account for about 40 percent of the RPA market, much of it focused on similar initiatives.
Deutsche Bank, for example, says RPA in banking has enabled them to achieve between 30 and 70 percent automation of functions such as trade finance, cash operations, loan operations, and tax.
RPA in banking is also resulting in improved accuracy, by automating transaction reviews for better fraud identification, data entry of client onboarding materials, and other daily processes. With RPA in banking, organizations have seen these results despite facing similar challenges to other industries, among them not having processes in place to identify the best business cases from automation, business silos, and strict regulations regarding privacy and security.
The common theme across all industries?
While the implementation of RPA tools in each sector continues to vary, the quality of enterprise data that feeds each system presents a common link as to why some RPA efforts are struggling to scale.
RPA cannot run on poor-quality, unstructured data. And while many organizations may have high goals for RPA tools, scanned documents, images and other unstructured formats in their content stores will create a roadblock if they’re not structured for machine ingestion. Having a robust data capture and extraction solution in place is essential to ensuring an RPA system isn’t stopped by sources of data it can’t work with. This, combined with improved data governance, and smaller, scalable RPA pilots can ensure businesses in all industries make better inroads with RPA tools.
Most industries have far to go to reach RPA maturity. While there may be large gaps between the promise and the current state of RPA, it’s helpful to look at where different sectors are seeing success. If you’re interested in exploring how to get started with RPA or how to accelerate existing RPA projects, click here to find out how.
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