Insurance companies are focused more than ever on optimizing the experience they provide to customers. And if digital interactions were not at the top of the list of capabilities needed at the beginning of 2020, it became everyone’s #1 priority when the worldwide pandemic shutdown began. As this year draws to a close, we can all see that customer expectations have permanently shifted, with an Amazon-like experience quickly becoming table stakes for the insurance industry.
With a variety of insurance automation solutions available, how does an insurance company, keenly focused on managing its customers’ risks, also embrace the agile mindset of experimentation and failing fast, to as quickly as practical find the tools and vendor expertise that best meet their needs? While it’s enticing to say “go!” based on some impressive demonstrations, it is likely to put your organization on a path toward disappointment, surprises, and compromises when the chosen solution comes up short in solving your problems. After all, there are many types of automation, from RPA (robotics process automation) to AI (artificial intelligence) to ML (machine learning), to just name a few. Also not all solution providers are the same, in terms of their services, expertise and insurance industry experience, in addition to being a good cultural fit with your team.
The first step to making an informed selection is a proof of concept, as a test of feasibility and viability. Unlike a pilot, which runs in a production environment as a precursor to a full-scale implementation, a proof of concept might never move past a test environment.
A proof of concept not only enables an insurance company to test the technology from a feature perspective, but is also where the corresponding changes to roles and workflow are part of the experiment. Absent testing from the people-process-technology lens, jumping directly into a pilot increases the likelihood of using a modern solution in a tenured construct of work alignment and overhead. Utilizing the new technology to its fullest potential probably requires changes in roles and processes. Modern automation solutions often target business systems analysts as their primary users, versus older tools that require developer coding expertise for everything. And new products are prone to embrace agile practices, focusing on continuous delivery over documentation.
Ushur has created a proof of concept guide to share our expertise on what goes into a successful proof of concept for automation.
From how to build a cross-functional team, to having metrics up front to measure the impact, to how to progress from walk to jog to run, this guide offers the recipe to a successful automation proof of concept.
Throughout the guide, Ushur also includes insight on what an insurance company should expect from an automation vendor during this test-and-learn phase. Remember, a proof of concept not only experiments with the solution and your own organization – it also determines if the provider brings the right thought leadership, skills, and services to the table.