Insurance has always built its own technology. When there were no vendors, that was a necessity. When vendors arrived, it became a habit. Now AI is here, and that habit is harder than ever to break.
While building solutions in-house has never seemed more attainable, sustaining and improving what you build has become far more difficult and complex. Before deciding whether to build or buy AI systems, let’s take a moment to understand why this gap exists and how to avoid it.
The answer goes back further than many people realize. Insurance technology was not built in-house by choice. It was built that way because there was no other path, and by the time other options became available, the habit had already taken hold:
In the early days of insurance technology, there were no established vendors to choose from: Carriers relied on mainframes – in fact, insurance helped create modern computing. Carriers wrote custom code to run policy, billing, and claims systems because there was no one else who could. This was not a strategic choice. It was a necessity.
Technology became closely tied to competitive advantage: Pricing models, underwriting guidelines, and internal workflows lived inside those systems. Keeping them in-house felt vital to protecting the business itself. That changed gradually, but the habit did not.
Legacy systems made the case for staying put: Many carriers were already heavily invested in custom-built platforms. Replacing them would have created large-scale costs, disruption, and uncertainty.
There was a strong belief that internal control reduced risk: Insurance is a highly regulated industry. Leaders often felt more comfortable managing systems themselves rather than relying on external partners.
There was little pressure to disrupt this model: Customer expectations were lower. Competition moved at a slower pace. Manual processes were an accepted part of the business.
Then, beginning in the 1990s, insurance embarked on its long technological transformation. During this time, vendors matured. Outsourcing arrangements became more common. Core system platforms improved and became more configurable. Insurers started to recognize that not every part of their technology stack needed to be built internally. Vendors could deliver reliable solutions across policy, billing, and claims.
There was also a growing realization that underwriting risk, managing claims, and customer service are the core business of insurance – not software development.
Vendors could spread development costs across multiple clients, invest in ongoing improvements, and deliver updates more consistently. That model became difficult for individual insurers to match on their own.
Today, insurers face very different pressures. The environment has shifted in ways that make the old approach increasingly difficult to justify, and the gap between what building internally requires and what most organizations can realistically sustain continues to widen:
Speed, accuracy, and consistency now directly impact competitiveness: Policyholders and agents expect quick responses. Delays in underwriting or claims handling can lead to lost business. Inconsistent outcomes can damage trust.
Technology has become more complex: Modern systems include cloud infrastructure, data pipelines, integrations, and artificial intelligence. Building and maintaining all of this internally requires specialized skills and ongoing investment.
Talent is harder to find and retain: Many insurers struggle to hire the engineers, data scientists, and AI specialists needed to support large internal builds. Industry workforce studies continue to highlight a growing gap in technical and analytical skills, particularly in data science and artificial intelligence.
The cost of maintaining custom systems continues to grow: Updates, integrations, regulatory changes, and performance monitoring all require constant attention. What once felt like control is now just additional business overhead.
The cumulative effect is a technology environment that rewards focus over breadth. Trying to build and maintain everything internally no longer serves the business the way it once did.
AI has revived insurers’ instinct to build, and it is easy to understand why. The tools are more accessible – a working AI prototype can be assembled in days, instead of months. Early results look impressive. For many insurance leaders, building internally genuinely looks faster than working with a vendor.
This has happened before. When mainframes gave way to more flexible computing in the 1980s, insurers saw new capability and moved to own it. When the internet opened new data possibilities and distribution channels in the late 1990s, carriers built internal systems to capture the opportunity. In each case, the early phases felt manageable. The complexity emerged later after this commitment had already been made. Suddenly, maintaining and evolving what had been built became an ongoing operational priority that competed with everything else on the roadmap.
Artificial Intelligence follows this same arc, but with one important difference. Every previous technology cycle made its complexity visible early. Mainframes required specialists from day one. Core system migrations announced their difficulty upfront. The learning curve was steep enough that organizations had to plan around it.
AI does not work that way. The entry point is genuinely easy. A team with modest technical skills can produce something that looks and performs convincingly in a controlled environment. That part is relatively easy – which makes the trap harder to detect. By the time the full burden of sustaining, governing, and scaling AI in a production insurance environment becomes apparent, organizations are already committed. The decision to build has been made. The budget has been allocated. The team has been assembled.
That’s how the pattern repeats. Not because insurers make bad decisions, but because AI’s transformative power can make insurers feel like implementation alone is the entire journey.
The history of insurance technology shows a clear pattern that insurers built systems when they had no other option. They continued building to protect prior investments and maintain ownership of important processes. Over time, this approach has become harder to sustain.
Today, the environment demands a different mindset. Technology is no longer just a support function. It is central to how insurers compete and serve customers.
What made building feel like the right choice in each era was a sense of control. Owning the system meant owning the outcome. What this logic failed to consider was the toll of maintaining that high level of control over time. In every previous cycle, that cost eventually outpaced the benefit. There is no reason to expect AI to be different. In fact, AI may tip this cost/benefit balance even faster, putting carriers at an even greater competitive disadvantage.
Clinging to a build-everything approach can slow progress and increase risk rather than reduce it.
The question is not whether insurers can build technology. They always have. The more important question is whether the instinct driving the next build decision is strategy or simply habit.
Insurers that recognize this pattern have an advantage. They can make a deliberate choice to advance rather than default to the familiar.