Systems | Development | Analytics | API | Testing

Automation has long been a key driver of efficiency. Traditional RPA and IDP technology promised to relieve carriers from rekeying, extracting data from forms, and other repetitive tasks. At Appian, we saw early that automation in isolation doesn't achieve transformative outcomes. Why? Because AI has too often been deployed at the edges of workflows: copilots, chatbots, or analytics dashboards that assist us when prompted.

Every new protocol introduces complexities—onboarding new tools and SDKs, adapting to new processes, evaluating security and performance implications, ensuring compatibility with existing systems, and overcoming the learning curve. So when a new protocol shows up, the first question to ask is: is it really necessary? Let’s ask that about MCP—what problem is it trying to solve?

Build smart, innovative data products by pairing context-aware AI with automated data integration.

Let’s say you’re renovating a house. It’s a big project, and you’ll need a lot of planning, expertise, and equipment. Now imagine you live in a world where the hammers you bought at one store don’t match up with the nails you bought at another, and certain wrenches wouldn’t work on anything but the parts they came with. What a pain, right?

Some data problems are universal — like dealing with unstructured data. At Bystronic, a global leader in sheet metal processing, we have mountains of it. From technical documentation to sales decks, HR policies, and IT knowledge bases, data is scattered across folders, servers, and systems. Industry research shows that 80% of enterprise data is unstructured, meaning it’s often invisible to the teams that need it most. As a result, up to 68% of that data goes unused. The impact is real.