\n<\/p>\n
The insurance industry has spent nearly three decades waiting for a technological revolution. Straight-through processing, predictive underwriting and dynamic pricing have been tomorrow\u2019s promises for so long that many stopped expecting tomorrow to arrive. With AI, we believe it\u2019s here today.<\/p>\n At the 2026 ReSource Pro Summit, more than 300 insurance professionals gathered around a single, urgent theme: Capture the Shift<\/em>. The message from ReSource Pro CEO Dan Epstein Tagger at the opening keynote was candid and practical, not a pitch for a particular technology, but an honest look at what it takes to make AI work in insurance. And it starts somewhere most organizations are overlooking.<\/p>\n Most AI conversations in insurance begin the same way: What can AI do for us? It sounds both natural and like an energetic starting point. But as this year\u2019s keynote argued, it may be precisely the wrong question.<\/p>\n Consider the data. Roughly 90% of insurance organizations have AI pilots running today, 22% have live deployments in production, but only 7% have successfully scaled those projects enterprise-wide. According to research from the Boston Consulting Group, the biggest barriers are not technological but arise from people and processes. Teams are not set up to manage AI, and operations are not structured to let it succeed.<\/p>\n The more productive question is: What does AI need from us?<\/strong><\/p>\n Think of AI as a brilliant but highly literal analyst. Give that analyst clean data, standardized processes, and clear decision logic; and strong performance follows. Give that same analyst inconsistent workflows and undocumented rules, and results are equally consistent, just in the wrong direction. That\u2019s because AI does not fix fragmentation, it scales it.<\/p>\n Andrew Ng, co-founder of Google Brain, has described AI as \u201cthe new electricity.\u201d And it\u2019s not just big tech companies seeing this metaphor in action. When factories first adopted electricity, they kept their existing layouts and saw little improvement. Only after redesigning their operations around electricity did meaningful gains follow. The same dynamic is playing out in insurance today with AI.<\/p>\n Operational readiness and AI readiness are not two different goals, but one and the same.<\/p>\n For anyone in insurance operations, this will be familiar. The industry needs to navigate what ReSource Pro calls \u201cthe messy middle,\u201d a system of fragmented workflows, tribal knowledge, and process variation that runs far deeper than most organizations realize until they try to train an AI system on it.<\/p>\n In insurance, 95% accuracy is a failing grade. It means a five-point risk to compliance and to your business. Across several hundred of our clients, ReSource Pro supports approximately 300 core workflows. Those workflows have spawned 70,000 versions and an estimated 7 million variation points. For example, a single employee benefits task can require 500 pages of documentation for a 15-minute procedure.<\/p>\n This variation has understandable roots. Insurance is a client-centric profession, and the attentiveness that builds lasting client relationships also produces highly customized processes. That orientation is a genuine strength, but it creates a real challenge when AI needs consistency and documented rules to function reliably at scale. Zach Hansen and Sivan Iram showed how ReSource Pro is addressing this historical challenge to put AI into action to address these complications with live demos.<\/p>\n ReSource Pro\u2019s answer is a three-pillar framework: Integrate, Optimize, Digitize (IOD). Integration means standardizing workflows and centralizing functions. Optimization involves streamlining processes and building functional teams. Digitization covers the data layer: cleansing records, establishing governance, and setting up the platforms AI requires to operate responsibly.<\/p>\n This foundation not only gets companies ready to build AI into their frameworks but also generates real returns before any AI is deployed. Organizations working through the IOD framework consistently see productivity gains, stronger client retention, and improved business outcomes from the process improvements alone.<\/p>\n At ReSource Pro, operational readiness is not only about standardizing workflows and cleansing data. It also requires that AI systems make their reasoning visible. When AI handles insurance decisions \u2014 renewals, submissions, compliance checks \u2014 those decisions must be auditable. Regulators, clients, and internal teams need to understand not just what the AI decided but why. That means documented decision traces, expert review at critical judgment points, and governance structures that keep the AI informed as business rules change. Without this layer of transparency, even a well-trained AI becomes a liability.<\/p>\n Instead of a black box, we need a crystal box that we can see through to understand what happened and what course corrections are needed going forward.Are you asking the right question?<\/h3>\n
Operational readiness is AI readiness<\/h3>\n
The messy middle<\/h3>\n
\n
Dan Epstein Tagger, CEO, ReSource Pro<\/cite><\/p>\n<\/blockquote>\nA practical path forward: Integrate, optimize, digitize<\/h3>\n
AI decision-making clarity: From black box to crystal box<\/h3>\n
\n
Dan Epstein Tagger, CEO, ReSource Pro<\/cite><\/p>\n<\/blockquote>\n