2025 Best Practices Study

A dedicated technology leader — whether a CTO, innovation head, or an employee who is passionate about leading the change — is critical. This individual should be empowered to own AI strategy and be accountable for execution. In high-maturity firms, 91% have already appointed a dedicated AI leader according to a 2025 Gartner Survey on how firms are adopting AI. Remarkably, this person does not need to be a technologist by training. Many successful AI leads in insurance brokerages bring a mix of 75% industry expertise and 25% technical fluency. They are operational leaders who understand the business and are comfortable with technology while bridging strategy and execution. Equally vital is the support of key business leaders. Their engagement ensures alignment between technology goals and business priorities, and they must hold teams accountable for using new tools effectively.

Beyond investments in data quality, brokerages must also implement strong governance frameworks that safeguard client data, ensure compliance, and prevent misuse. U.S.-based firms also need to stay ahead of evolving regulations that pertain to AI and consumer data, including state-specific requirements and federal guidance still in formation. Numerous online resources are available for monitoring AI regulation. The law firm BCLP provides a comprehensive tracker that covers both proposed and enacted regulations at the federal and state levels.

“Agencies that have invested in having a complete and accurate dataset of their customers and policies will get a much better return on their AI investment than those who have not.”

Kabir Syed CEO of ennabl

Organizations should allocate appropriate resources towards change management initiatives, including planning and training, to effectively address the transformations introduced by AI. Legacy workflows within insurance brokerages are often inflexible and slow to adapt. Without a strong commitment to preparing employees for these changes, successful adoption may be an uphill battle.

Rollout Structure

A well-executed AI roll-out plan is essential to turn intention into impact. AI won’t create value unless it is adopted at scale, and that adoption requires structure. The rollout must be phased, intentional, and aligned to specific workflows. The following are the key stages of an effective rollout structure.

Identify use cases and define KPIs for success

Assign a pilot team and build a beta model

Map employee workflows

Prepare and release into production with training and change management

Post-release monitoring and reinforcement

Roll into phased implementation

Generative AI: Transforming Insurance Practices

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