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Robotic process automation (RPA) has become a prominent buzzword within the insurance space over the past couple of years, promising the potential to automate businesses’ high-volume and low-complexity processes. However, where RPA has seemed to miss the mark is that it is unable to integrate advanced cognitive capabilities that can operate in a dynamic business environment. Tasks like communicating, making inferences, or solving complex problems have been out of reach for RPA.
RPA also comes with a learning curve, requiring human labor for ongoing operations, and can be rather costly. Implementing AI solutions into your insurance business should genuinely help employees “work smarter, not harder” and not create more work or hassle for them.
Smaller RPA initiatives are particularly at risk of trading low-cost workers for high-cost programmers, which is the opposite of the intended purpose. The promise of citizen developers (employees that develop their own bots) doesn’t solve this problem either. This is actually quite an old problem, which you would recognize if you have ever experienced the nightmare scenario of dealing with broken Excel spreadsheets with unmanageable Visual Basic scripts from an employee that left three years ago!
The solution to the conundrum may reside in a carefully calibrated blend of cognitive process automation (CPA) with two-way live interaction, enabling the best in automated workforce outcomes.
The Clear Pitfalls of RPA
While RPA can be a great fit for tedious and repetitive processes, it won’t fix a process you don’t fully understand or that is otherwise fundamentally broken. RPA tends to fail in two scenarios: either the process being automated is not as well-defined as initially thought, or the resulting automation is run in an environment that is much more dynamic than previously identified. In either case, the tooling requires much more maintenance and ongoing development, both bad for your business case. These high levels of human capital involvement are the exact reasons why business leaders are considering RPA in the first place, so it’s counteractive.
A few other reasons RPA may not be the right choice for most organizations:
- RPA has significant hidden upfront costs and a daunting learning curve. It requires substantial in-house IT hours to manage and maintain the processes once set up.
- Out of the box, RPA has no machine learning capabilities and must be built from scratch. Sure, there are APIs with smart algorithms, but your team needs to know how to deploy such solutions in order to be successful.
- When it comes to OCR (optical character recognition) capabilities, RPA can read some text and clean images, but cannot understand more complex forms with checkboxes or make inferences for missing or messy data points.
- RPA will give up if unable to make a decision—you may have to manually modify the code to prevent the bug from occurring in the future.
- Reporting with RPA isn’t very dynamic. Often the best-case scenario is a spreadsheet report at the end of the day. There is little transparency, and your business teams have no insight into what’s happening with the processes as they are unfolding.
- To implement RPA, a company must buy licenses and add new technology, which is time-consuming and often very costly. Especially when better technology becomes available.
- RPA usually requires changes to the existing workflow. Instead of the bots supporting you, you’ll find that you’re supporting the bots.
Why choose cognitive AI solutions instead?
Cognitive solutions are a subset of the broader field of artificial intelligence. They facilitate self-learning by leveraging algorithmic capabilities in AI and machine learning by extending the AI’s capabilities to tasks conventionally completed by humans. Unlike RPA, cognitive automation imitates human thinking—like natural language processing, image processing, and contextual analysis.
Many other industries have also begun to leverage these cognitive solutions. One distinguished innovation that has become symbolic of the current AI renaissance is Google’s Alpha Go, which made headlines by beating the best human Go player, Lee Sedol. The chess-playing algorithm both trained how to evaluate different game positions and how to make creative decisions, learning from its interaction with human players.
Personal styling company Stitch Fix is also leveraging a combination of smart algorithms and human-interaction to dress their customers better. This type of cognitive technology has become increasingly intriguing as COVID-19 has shuttered many retail and apparel storefronts.
With their ability to take up significant amounts of data, cognitive solutions create unprecedented possibilities across every enterprise domain. Below are specific reasons insurers should invest in cognitive solutions:
Boosts Operational Efficiency: With a capacious amount of unstructured data growing exponentially, from emails and documents, to images and videos, insurance companies are now looking to make data-driven decisions more than ever. The unfortunate reality is that keeping pace with the escalating flow of unstructured data is a real hassle. For organizations with large datasets requiring collecting and synchronizing data from disparate sources, cognitive technology solutions are a lifesaver. With cognitive solutions, organizations can automate complex workloads that alleviate repetitive and burdensome data-intensive work, while not needing those tasks to always fit neatly into a template. With such flexibility, automation boosts employee productivity, leading to better operational efficiency.
Minimizes Human Errors: The AMA’s Health Insurer Report Card found that health insurance companies are averaging a 19.3 percent error rate, which wastes an estimated $1.7 billion annually. In the digital age, enterprises can rely on an algorithm to reduce the risk of human error in their interactions with complex systems. Once implemented, cognitive technology solutions can help enterprises minimize procedural-related errors. These types of human errors are linked to poorly implemented procedures, deficient instructions, and employee’s choice not to follow them. Cognitive errors, such as fatigue, stress, and illness are also minimized by implementing cognitive solutions. Since cognitive solutions automate a huge percentage of the tasks, human errors can be greatly reduced.
Reduces Labor Hours: Cognitive solutions can handle most of the laborious and time-consuming tasks that often strain productivity, such as automating certain production management elements and solving user tech issues. The automation of monotonous tasks enables employees to reserve their precious working hours and spend them on more important projects.
Minimizes Operating Costs: Having competent employees spending a significant amount of their valuable time on time-wasting tasks rather than on critical activities is not only a waste of time, but it’s also a complete waste of money. With cognitive solutions, employees can allocate their time to more critical and value-adding projects. Cognitive automation can help companies save up to half of their total spending on full-time equivalent (FTE) and other related operational costs.
The significant benefits of cognitive solutions are clear—they will boost employee productivity leading to better operational efficiency, will create a steadier, risk-controlled environment for the company, reduce working hours for staff, and save money. While RPA is trendy at the moment, it’s not the most efficient AI solution for insurance companies to implement. Enterprises will continue to see an increasingly meaningful and foundational impact from cognitive solutions.