From AI-Curious to AI-Native: The Insurance Leader's Playbook

Executive Summary

Artificial intelligence is transforming industries at an unprecedented pace, and the insurance sector is no exception. Insights from Natalia Quintero, head of consulting at Every, highlight that successful AI integration is less about having the biggest budgets or most advanced tools and more about leadership commitment, empowering internal champions, and fostering a culture of experimentation. These principles guide companies from being merely AI-curious to becoming truly AI-native.

For insurance professionals, embracing AI requires a top-down approach where executives actively engage with the technology and create organizational conditions that encourage innovation. By identifying knowledgeable internal advocates and allowing teams time to explore AI solutions, insurers can streamline workflows, enhance underwriting accuracy, and improve claims processing efficiency. This article distills key lessons from Every’s AI consulting experience and explores practical applications tailored to the insurance industry. More details on these insights can be found in the original podcast episode hosted by Dan Shipper with Natalia Quintero at Every’s website.

Key Insights

• Leadership Engagement is Critical

Successful AI adoption starts at the top. Unlike traditional technology rollouts often delegated to IT departments, AI requires CEOs and senior leaders to be directly involved and conversant with AI tools. This leadership commitment signals organizational priority, drives resource allocation, and models the cultural shift necessary for transformative change. Insurance firms should ensure their executive teams not only endorse AI initiatives but actively experiment with AI capabilities themselves.

• Empower Internal AI Champions

Within every insurance organization are employees naturally inclined toward innovation and technology adoption. Identifying these internal champions and elevating their role is essential. These individuals serve as catalysts, helping colleagues understand AI’s potential and tailoring solutions to specific business needs. For example, underwriting specialists or claims analysts with AI proficiency can bridge the gap between technical teams and frontline operations.

• Detailed Workflow Mapping Enables Targeted Automation

As demonstrated in Every’s work with private equity clients, granular mapping of daily tasks allows organizations to pinpoint high-value areas for AI automation. In insurance, this could mean analyzing underwriting workflows to identify repetitive data entry or risk assessment steps that AI can expedite, freeing up human expertise for complex decision-making.

• Create Space for Experimentation and ‘Play’

Innovation flourishes when employees have time and freedom to explore AI tools without immediate performance pressure. This approach contrasts with typical work environments heavily focused on productivity metrics. Insurance companies should consider dedicated innovation labs, pilot programs, or scheduled ‘AI exploration time’ to encourage creative problem-solving and iterative learning.

• Invest in AI Systems as Collaborative Partners

AI tools, like the project management agent ‘Claudie’ developed by Every, require ongoing training and contextual understanding to function effectively. Treating AI as a team member needing onboarding, clear instructions, and continuous feedback ensures higher accuracy and integration success. Insurance firms adopting AI should implement robust governance and quality controls to guide AI tools’ performance and error correction.

Insurance Industry Applications

• Underwriting Efficiency

By mapping underwriting processes in detail, insurers can deploy AI to automate routine risk assessments, verify policyholder data, and generate preliminary risk profiles. This reduces turnaround times and allows underwriters to focus on complex cases requiring nuanced judgment.

• Claims Processing Automation

Internal AI champions within claims departments can lead adoption of AI-powered document analysis and fraud detection tools. Experimentation with AI chatbots can improve customer engagement by providing real-time claim status updates and guiding claimants through submission steps.

• Risk Modeling and Predictive Analytics

Leadership involvement in AI helps insurance firms embrace advanced predictive models that analyze vast datasets to forecast risk trends. Encouraging data scientists and actuaries to collaborate with AI specialists can refine model accuracy and inform pricing strategies.

• Customer Experience Enhancement

Allocating creative space for marketing and customer service teams to experiment with AI-driven personalization tools can yield tailored recommendations and improve retention. Pilot programs for AI-based policy recommendations or virtual assistants can be tested without disrupting core operations.

• Operational Project Management

Inspired by Every’s ‘Claudie’, insurers can implement AI project managers that integrate with existing workflows and tools (e.g., CRM, document management systems) to streamline onboarding of new clients, monitor compliance tasks, and provide status updates, significantly reducing administrative overhead.

Conclusion and Recommendations

For insurance organizations aiming to advance their AI capabilities, the path forward involves more than just technology procurement. It demands active leadership participation, identification and empowerment of AI champions within teams, and a cultural shift that values experimentation and iterative learning. Detailed workflow analysis is critical to uncovering automation opportunities that deliver measurable efficiency gains.

Insurance professionals should advocate for executive AI literacy programs and support internal forums where employees can share AI use cases and lessons learned. Investing in AI systems as collaborative team members, supported by ongoing training and quality oversight, will enhance adoption success.

Adopting these strategies will position insurance companies to leverage AI not only for operational efficiency but also to redefine customer engagement and risk management in a rapidly evolving marketplace. Further exploration of these insights is available through Every’s podcast episode featuring Natalia Quintero, accessible on platforms like Spotify and Apple Podcasts.

You can listen to the full conversation and access additional resources here: Every’s Head of Consulting on AI Adoption.