Artificial intelligence has moved rapidly from emerging technology to industry standard. According to MarshBerry’s 2026 Technology & Governance Study, nearly three-quarters of firms report some level of AI adoption. Yet beneath that statement lies a more nuanced reality: many firms remain in the experimentation phase, using AI for limited tasks rather than embedding it into core business processes. The question is no longer whether firms should use AI. Instead, the challenge is how to integrate it successfully in a way that creates measurable value.
The study found that 74% of firms are either actively using AI or testing it in parts of their business. However, only 13% report using AI across multiple areas of the organization, while 61% of firms are still testing or using it in limited areas. This suggests that while AI adoption is widespread, AI is not a core part of business processes at many firms for various reasons, including concerns around data quality to support AI initiatives. Current usage patterns reflect this reality.
How are most firms using AI?
Most firms are leveraging built-in AI capabilities within existing platforms such as Microsoft Copilot or Zoom AI Companion, while many also use publicly available tools like ChatGPT, Google Gemini, and other AI chatbots. These applications are often focused on improving individual productivity rather than transforming operations. Common use cases include meeting summaries, email drafting, document organization, policy comparison, and communication support.
The results have been promising, particularly in document-intensive workflows. Firms reported the strongest outcomes in AI-assisted policy checking, document comparison, personalized communications, and analytical support. AI is also increasingly being used to automate billing reconciliation, streamline client intake, help with proposal generation, and improve workflow documentation. These practical applications demonstrate AI’s ability to improve administrative efficiency without fundamentally changing the client relationship.
Importantly, industry leaders do not see AI replacing brokers. More than half of respondents believe AI will enhance broker productivity and client value while leaving the core role largely intact. Another 27% believe AI will significantly reshape the broker’s role, leading to new service models. Only 10% expect AI to displace some traditional broker tasks, potentially streamlining certain roles. AI appears largely viewed as a productivity tool instead of a replacement strategy.
Success factors for AI initiatives
Despite growing adoption, some firms remain uncertain about how AI fits into their business. Among organizations not currently using or exploring AI, the most common response was simple uncertainty. 58% reported they do not know how AI could fit into their operations. Others cited lack of time, limited resources, uncertainty about return on investment, or concerns that their systems and data are not ready to support AI initiatives.
One of the study’s key findings is that technology alone does not create scalability. Data quality, integration, and governance are often the factors that determine whether AI succeeds. While firms continue to prioritize AI and automation investment, many still struggle with fragmented systems and inconsistent data. Only 27% of respondents describe their data as well organized and ready to support analytics or automation. The majority note that their data requires cleanup or structure before it can be used effectively.
This creates a significant limitation for AI initiatives. Advanced models can only deliver reliable insights when they are supported by accurate, accessible, and reliable information. Without a strong data foundation, firms risk generating inconsistent outputs or automating flawed processes.
As a result, successful AI adoption should not begin with selecting a chatbot or purchasing the latest software. It should begin with strengthening the underlying infrastructure that makes AI valuable. Firms that invest in data quality and integration in order to achieve organized, reliable data across core systems will be better positioned to realize meaningful returns from AI investments.
MarshBerry’s study also found that some firms currently rely on publicly available AI chatbots to support daily processes and workflows. While these tools offer productivity benefits, they can also introduce compliance concerns if employees input sensitive client information, proprietary business data, or confidential carrier information into public systems. Firms should establish clear AI usage policies that define what information can be shared, consider enterprise versions of AI chatbots that offer enterprise-grade security, and decide how outputs should be used for client-facing communications or business decisions.
Conclusion
AI used to offer a competitive advantage for firms, but is now becoming a baseline expectation. The differentiator is no longer access to AI but the ability to integrate it effectively into daily operations. The firms that will create lasting value from AI may not be those making the largest investments. They are the organizations that align AI initiatives with business objectives, focus on high-impact workflows, establish governance around data and compliance, and ensure employees understand how to use these tools effectively. As AI continues to mature, success will belong to firms that move beyond experimentation and include AI as a part of the broader operating strategy.
In the years ahead, AI may become a standard part of insurance brokerages, similar to CRM systems, agency management systems, and digital communication tools today. The opportunity is significant, but realizing it will require more than adoption. It will require strategy and execution.
