Insurance is known as an industry built on caution and calculated decision-making. That isn't going to change, nor should it. But caution becomes a problem when it turns into avoidance.
As AI becomes more embedded across the insurance industry, the greatest risk for many organizations isn't moving too quickly. It is moving too slowly — because some leaders are waiting for the technology to be perfect before they are willing to engage with it at all. That is Passive Sabotage.
Passive Sabotage is the art of dismissing a capability simply because it isn't as good as you think it should be. Does AI code as well as an experienced engineer? Questionable. Does it do it 30 times faster? Yes. Does it know how to tackle new and complex functionality without weeks of meetings, spiking, and exploration? It does. The result is working features in hours or days, not weeks.
Passive Sabotage shows up in small decisions that undermine progress. Abandoning an AI tool after an imperfect result. Delaying a pilot until every problem is solved. Or defaulting to manual processes just because that's what's familiar.
The Real Consequences of Hesitation
This hesitancy comes with real consequences. Organizations that take this approach may miss critical information, move more slowly than competitors, keep experienced employees buried in administrative work, and fail to make full use of the data already available to them. Over time, the gap will widen between companies that are learning how to use AI responsibly and those still debating whether the technology is ready.
Insurance organizations do not need to force AI into every workflow to realize its value. Decisions that carry legal, financial, and human consequences still require consistency, oversight, and experienced judgment. The practical opportunity is to use AI to help teams move faster, reduce manual work, surface information, and test ideas that might otherwise never move beyond discussion.
This distinction matters because much of the skepticism around AI starts with the wrong question. Leaders often ask whether AI can do an entire job as well as a human expert. The better question is whether AI can help a team get to a usable answer, workflow, prototype, or insight faster than they could on their own. In many cases, it can.
For example, AI can summarize claim notes, extract key information from documents, identify missing data, draft communications for review, and help teams prototype new workflow ideas. None of these use cases require handing authority to a fully autonomous system — they require placing intelligence at the right point in an existing process, with the right guardrails, so people are better equipped to make decisions.
Personal Proof
I have tried to demonstrate this through my own work rather than just advocate for it. In the past six months, personally — in between meetings, on planes, nights and weekends — I built:
- A complete marketplace platform with custom integrations, installation workflows, reporting, and analytics. Historically, I had spent over $150,000 annually on white-labeled embeddable marketplaces for our SaaS products. No more. Built, tested, security-reviewed, and deployed in under four weeks.
- A centralized enterprise-wide content and knowledge library with a fully integrated newsfeed to deliver personalized updates across the organization — all AI-enabled summaries and agents — replacing a platform that had cost over $180,000 annually.
- An Outlook add-on built end-to-end in 90 minutes using publicly available API documentation. Another two to three hours of refinement based on testing and feedback.
Were those outputs perfect on the first pass? No. But they were usable — and they were created dramatically faster than they would have been through a traditional process.
Speed changes how organizations learn. Teams can move from idea to prototype in a fraction of the time. They can react to something tangible instead of spending weeks writing requirements for something they have not yet seen. Leaders can test concepts, refine workflows, and make more informed decisions earlier in the process.
This is especially important in insurance, where many organizations are led by experienced executives who did not enter the industry because they were passionate about technology. For many, technology has always been a means to an end. That is understandable, but AI requires leaders to build a new level of familiarity. They do not need to become engineers, but they do need to understand where the technology can create practical value.
Where Organizations Get Stuck
This is where many organizations get stuck. But companies moving ahead are not waiting for AI to become flawless. They are learning where it works, where it needs oversight, and how it can fit into existing processes.
Governance must be part of that process from the beginning. In insurance, requirements can vary by state, business line, client, and use case. Organizations need the ability to decide where AI is used, where it is not, and the option to quickly adapt capabilities if requirements change.
Because of this, modularity is essential. A rigid AI deployment can create risk and limitations, but a configurable approach allows organizations to apply AI where it makes sense, keep human judgment where it is needed, and adapt as the regulatory environment changes. In insurance, where a regulatory change can shift how you apply AI overnight — by state, by client, by risk type — a deployment you can't reconfigure by rule is a liability.
Trust will remain one of the biggest barriers to AI adoption. Many professionals are still getting comfortable with the technology in their everyday work. Technology providers and industry leaders have a shared responsibility to deliver tools that feel useful rather than threatening, and to be honest about where AI needs human oversight and where it does not.
AI does not have to be perfect to be valuable. It does not have to replace expert teams or solve every problem to create a competitive advantage. Insurance leaders who understand that will learn faster and make better use of the information already in front of them.
The companies getting ahead are learning where AI works, where it needs oversight, and how it fits into existing operations — through direct use, not by delegating the evaluation and waiting for a recommendation. If you're a "leader" riding side-car to the opinions of others on this, your days in that role are numbered. It's time to begin using AI yourself.
