30 Years Later, Insurance Tech Still Chases the Same Goal: Speed

In three decades of building systems for insurers, I’ve watched the technology landscape change completely. We’ve moved from fax and mainframe to distributed computing, APIs, microservices, and now artificial intelligence. Every new wave has promised to make the business faster: faster quotes, faster claims, faster decisions. The real challenge has always been finding ways to increase speed without letting risk rise alongside it. That balance is what defines success in insurance technology, and it’s a lesson that has had to be relearned in every decade.

1990s: Back in the Stone Age

Back when I first entered software engineering, batch processing didn’t involve dashboards or automated pipelines, it involved card decks. Entire jobs arrived as neatly rubber-banded stacks of punched cards, each one representing a tiny fragment of the batch schedule. If something failed overnight, the engineer on call didn’t log into a console; the computer operator called them to identify the offending card, pull it from the deck, and resubmit the whole stack. It remains the only time in my career when “debugging” literally meant removing a physical card and praying the schedule ran clean on the next attempt. In hindsight, it was DevOps with a touch of origami.

Those early days reflected a broader reality across the insurance industry. Much of the business still ran on paper files and green-screen terminals, and digitization felt revolutionary. We replaced file cabinets with databases and moved processes like “first notice of loss” into basic client-server applications. For the first time, information could move instantly through the organization. But the shift also exposed how fragile early digital systems could be. Data models were inconsistent, security was minimal, and even a small outage could halt an entire claims department. We quickly learned that speed only mattered if the underlying data could be trusted.

2000s: Web 1.0 — Portals and Databases

By the early 2000s, insurers were racing to build web portals and real-time integrations. Agents could quote and bind directly through online interfaces, and internal teams could see the same information at the same time. That connectivity was a huge leap forward for efficiency. It also introduced a new level of complexity. The more systems that were connected, the harder it became to keep data synchronized and compliant with evolving privacy regulations. It was in this decade that architecture began to matter as much as code. The best results came when someone stepped back to see how all the moving parts fit together and ensured that what appeared seamless to the user was equally sound underneath.

2010s: Enter the Cloud — DevOps and Microservices

A leading national homeowners insurance provider was struggling under the weight of its legacy, on-premises monolithic quoting application. Scalability was limited, performance was inconsistent, and upgrades required lengthy downtime, all of which hampered customer experience and slowed operational agility. To overcome these constraints, the company partnered with VividCloud to modernize its platform on AWS. By decomposing the monolith into microservices, adopting Amazon ECS for container orchestration, and implementing DevOps practices with fully automated CI/CD pipelines, the insurer achieved near-zero-downtime deployments, dramatically improved scalability, and accelerated quoting performance. The result was not only enhanced customer satisfaction and reduced infrastructure overhead, but a technology foundation capable of supporting rapid, future-focused growth.

This transformation reflected a broader industry shift powered by cloud computing. Suddenly, infrastructure could scale on demand, environments could be replicated globally, and applications could be deployed without waiting for change windows or weekend outages. DevOps practices and microservice architectures unlocked a new level of agility, at times making it feel as though previous constraints had disappeared entirely. Yet this speed brought a new category of risk: a single misconfiguration could expose sensitive data, an unstable service could cascade across dozens of dependent systems, and cloud costs could escalate quickly without governance. The organizations that made real progress were those that paired automation with accountability using sound architecture, guardrails, and disciplined practices to ensure innovation moved quickly but never outran control.

2020s: AI Is Here

Now artificial intelligence has entered the picture, promising to unify everything we have built so far. Large language models can read and reason across the policy documents, claims notes, and third-party data that once lived in isolation. For underwriting and claims, the potential is enormous. It is finally possible to imagine quoting and decision systems that respond in seconds rather than hours or days. Yet the same question remains: how do we gain speed without losing trust?

AI demands a level of design maturity that few organizations have yet achieved. Models must be explainable, data pipelines must be auditable, and integration must respect the same compliance boundaries that have always governed this industry. This is where experience matters more than ever. The architect who has seen multiple generations of modernization understands the importance of data lineage, validation, and security in ways that no algorithm can replicate. Their judgment defines the framework that makes AI safe to use and valuable to scale.

The Right Kind of Speed

At VividCloud, we bring that balance to every engagement. Our teams pair seasoned enterprise architects with engineers, data scientists, and offshore development talent to deliver the full chain, from system design and modernization to AI platform integration. That combination of experience and execution lets us move quickly without sacrificing confidence.
Technology will keep evolving, but the principle will stay the same. Speed creates value only when it comes with control, and control lasts only when it is grounded in experience.