By Bryan Du – Head of Insurance Practice, Sotatek US
For much of the past decade, insurers have focused on digitization—moving systems to the cloud, modernizing policy administration, and improving access to data. These efforts have delivered meaningful progress. Yet beneath that progress, many core insurance operations remain highly manual. Claims still arrive through emails and attachments. Underwriters still spend considerable time assembling fragmented risk information. Brokers still rely on disconnected tools to manage client submissions and communications.
These operational realities explain why the discussion around AI agents is gaining momentum. Unlike earlier automation tools that simply executed predefined instructions, AI agents are capable of interpreting incoming information, organizing it, and assisting in coordinating workflows across multiple systems. However, while the technology itself is advancing rapidly, scaling it effectively in insurance requires a clear understanding of where it delivers practical value—and where it does not.
In our work with insurers and insurtech partners across life, health, and general insurance, the most consistent lesson has been that successful adoption begins with operational clarity rather than technological ambition. The greatest gains come from addressing areas where operational friction already exists.
Claims processing is one such area. Even with modern systems in place, claims teams often deal with submissions that arrive in inconsistent formats and require manual review before they can be processed. This preparatory work—extracting information, validating details, and routing cases—can consume a significant portion of the claims lifecycle.
AI agents can help streamline these early stages by classifying incoming documents, extracting relevant data, and directing claims to the appropriate workflows. This does not replace claims professionals, but it ensures they begin with complete and structured information. In practice, this leads to faster processing, fewer errors, and more consistent handling. Just as importantly, it allows experienced staff to focus on decision-making and customer engagement rather than administrative preparation.
Underwriting presents a similar opportunity. Underwriters play a critical role in assessing risk, yet much of their effort is spent gathering and reconciling information from multiple sources. AI agents can assist by consolidating data, highlighting relevant insights, and presenting risk summaries in a structured manner. This allows underwriters to focus on evaluating risk rather than assembling inputs.
The impact of this shift becomes more apparent as organizations grow. Without operational support, increased business volume inevitably leads to greater complexity. AI agents help manage this complexity by improving how information flows across systems and teams, allowing insurers to scale more efficiently without proportionally increasing operational overhead.
Brokers and managing general agents face comparable challenges. Their effectiveness depends on responding quickly to client enquiries, preparing submissions, and navigating policy options. Yet these workflows are often fragmented and manual. AI agents can assist by organizing client information, preparing documentation, and supporting routine communication. This improves responsiveness and consistency while allowing brokers to focus on client relationships and advisory work.
Customer servicing is another area where practical benefits are already emerging. Policyholders today expect faster responses and clearer communication, particularly during claims events. AI-powered assistants can help address routine enquiries, provide status updates, and guide customers through processes such as policy changes or claims submissions. When implemented thoughtfully, this improves accessibility without diminishing the human connection that remains essential in insurance.
At the same time, scaling AI agents depends heavily on the underlying operational foundation. AI agents rely on structured access to data and clearly defined workflows. If systems remain fragmented or data is inconsistent, their effectiveness will be limited. This is why many insurers are pairing AI adoption with broader efforts to modernize infrastructure and improve system integration.
Cloud-based platforms have played an important role in enabling this transition. By providing flexible, scalable environments, they allow insurers to integrate new capabilities incrementally rather than attempting disruptive system replacements. This approach reduces risk while enabling steady operational improvement.
Governance is equally critical. Insurance operates within a framework of trust, accountability, and regulatory oversight. As AI agents become more involved in operational workflows, insurers must maintain visibility into how decisions are made. This includes ensuring outputs are transparent, decisions are auditable, and appropriate human oversight remains in place.
Experience shows that the most effective implementations follow a measured and practical approach. Rather than attempting to automate entire workflows immediately, insurers begin with specific use cases where operational improvements are clear and measurable. Over time, as systems mature and confidence grows, the role of AI agents can expand naturally.
It is also important to recognize that not every process should be automated. Insurance relies fundamentally on human judgement, particularly in areas involving complex claims, underwriting decisions, and sensitive customer interactions. AI agents are most effective when they support professionals by reducing administrative workload, rather than attempting to replace expertise.
Looking ahead, the role of AI agents will continue to evolve alongside the broader modernisation of the industry. As systems become more integrated and data becomes more accessible, agents will play an increasingly important role in supporting day-to-day operations. This evolution will not occur overnight, but through gradual and deliberate adoption.
Ultimately, scaling insurance operations has always required managing complexity. What AI agents offer is a practical way to reduce that complexity, by improving how information is organized, how workflows are coordinated, and how operational teams interact with systems.
The insurers, brokers, and MGAs that benefit most will not be those that adopt AI agents the fastest, but those that implement them thoughtfully. By focusing on operational foundations, data quality, and human oversight, they can ensure these systems deliver sustainable value.
In the end, AI agents are not a replacement for insurance expertise. They are a tool, one that, when applied carefully, strengthens operational resilience and allows insurance professionals to focus on what matters most: managing risk, serving customers, and building trust at scale.
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