If you have a legacy system with customized capabilities and valuable features but nearing end of life, refactoring the system is a potential choice for modernization. Insurance platform modernization usually involves one-to-one code migration, which can often be more costly and time consuming than expected and typically tends to miss some of the integration and data architecture modernization that is foundational for getting the full value of digitization.

My first attempt to understand accessibility and how to test it started back in 2016. I was working for a company where accessibility was already an important part of the process. I was asked to perform accessibility testing on the product to determine whether we could claim to be accessible. Since it was a new subject for me, I was quite confused. Where should I start? Should I be certified to provide a proper assessment? Do I need to request assessments from third-party companies?

We tested a product-led go-to-market motion last year. Here are the exciting results and learnings.

What is hyperautomation for insurance? It might sound like just another buzzword, but let’s peel back the layers to see how hyperautomation works. Imagine this: you go on a deep dive to evaluate your insurance company’s processes and operational models. You discover inefficiencies, lag times, and that your team manages transactions with disparate, often manual processes. It’s clear you need a new solution. And the solution you need doesn’t currently exist.

The rise of AI-powered features in today’s analytics solutions has increasingly displaced our reliance on dashboards by making it easier for business people to analyze their data. With this rising paradigm shift, a new label has emerged: The augmented consumer. On a surface-level, the word ‘augmented’ may conjure up a variety of different interpretations. AI-based dashboard assistants? Natural language-led reporting?

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.