Artificial intelligence (AI) has moved from the periphery of risk management conversations to the center of nearly every strategic discussion. Risk leaders across industries are being asked by executives, “What’s your AI strategy?” and many don’t yet have an answer. Despite the buzz, the reality is that most organizations are still in the early stages of understanding where AI fits, what value it can deliver, and how to operationalize it within existing systems and workflows.
At Redhand Advisors, we’re seeing a surge of renewed engagement from former clients, many reaching out not for another system selection, but for guidance on AI. The message is consistent: there’s interest, but also uncertainty. Risk teams know AI will transform their function, but they need help figuring out how.
The Hype vs. the Reality
AI has become a ubiquitous tagline across technology vendors, conferences, and marketing decks. Every product claims to be “AI-powered” or “agentic,” often without clarity on what that truly means. Yet behind the marketing noise lies a genuine wave of innovation that is reshaping the RiskTech landscape.
The challenge is that the AI marketplace remains fragmented and immature. For every proven solution, there are dozens of one-off products that lack both insurance domain expertise and deep integration capabilities. Many are experimental technologies searching for relevance in risk and insurance, not purpose-built tools solving real operational problems.
This misalignment explains why adoption remains slower than expected. While risk managers are intrigued by AI, most are still evaluating use cases and building foundational strategies. The year 2025 has been less about mass deployment and more about structured exploration, a necessary phase before large-scale implementation begins.
Where AI Is Gaining Ground
The most visible and practical applications of AI in risk management today are in claims. Predictive analytics for reserving, triage, and litigation propensity modeling are proving their worth. AI is also enhancing workflow automation, moving beyond simple rule-based triggers to intelligent systems that can interpret context and make real-time recommendations.
But the most transformative potential lies in the unstructured data that has long gone underutilized. It’s estimated that 70–80% of claims information lives in adjuster notes, attachments, and correspondence. Extracting insights from that data has always been a challenge; now, generative AI can summarize, classify, and contextualize it in real time. Features like automated claim summaries, updated dynamically as new information enters the system, illustrate how AI can improve efficiency, accuracy, and decision-making without removing human oversight.
Other functional areas are beginning to follow. Property valuation and COPE data collection, renewal forecasting, contract review, and policy interpretation are all seeing early experimentation. AI is being tested to summarize lengthy insurance policies, benchmark claim outcomes using historical claim data, and even predict potential exposure before a claim arises. Each of these examples reflects incremental progress rather than disruption, but together they signal a significant shift in how data will be managed and leveraged.
The Emerging Role of Agentic AI
The next wave of innovation is centered on Agentic AI, autonomous or semi-autonomous systems capable of taking action within defined boundaries. While some of the hype is premature, there are real pilots underway. Early adopters are experimenting with agentic AI to automate certificate tracking, manage document workflows, or perform background verification tasks traditionally handled manually.
That said, the concept is still evolving. Many “agentic” solutions marketed today are closer to advanced automation than true autonomy. Risk managers don’t necessarily care about the distinction; they care about outcomes. The challenge for vendors and consultants alike is to translate the potential of these tools into practical, trustworthy applications that complement human expertise rather than replace it.
Data: The Essential Foundation
Before any AI initiative can succeed, the underlying data must be accessible, accurate, and comprehensive. This continues to be a roadblock across much of the risk and insurance ecosystem. Data in this space is historically inconsistent, with limited standardization and fragmented ownership across carriers, TPAs, and internal systems.
Many organizations simply don’t have the data maturity to feed AI effectively. For example, claims administrators may not capture adjuster notes in a structured format, or contract and policy documents may live outside the RMIS entirely. AI can compensate for some inconsistency, but it cannot replace missing data. Building a sustainable AI strategy therefore starts with assessing data readiness, understanding what information exists, where it resides, and how it can be integrated responsibly.
Adoption Challenges
Even when AI tools are available, adoption remains a hurdle. Many organizations underestimate the complexity of implementation and the learning curve required to use these systems effectively. Features get released, but they often sit idle because teams don’t know how to turn them on, configure them, or integrate them into daily workflows.
This is not a failure of technology; it’s a gap in enablement. Risk managers, already stretched thin, need education, change management, and guided experimentation to realize value from AI. For most, the journey begins not with automation, but with exploration: understanding what AI can and cannot do, identifying pain points, and defining small, high-impact use cases to prove value before scaling.
From Features to Frameworks
In the short term, AI features remain a competitive differentiator among RMIS vendors. But over time, they’ll become table stakes, expected capabilities that every platform must offer. The real differentiation will come from how organizations enable AI success: through advisory services, implementation support, and ongoing optimization.
The future leaders in this space won’t just deliver AI tools; they’ll deliver AI frameworks, flexible architectures that allow clients to adapt models, connect external data, and build their own AI agents safely within their environments. That blend of capability and configurability will define the next generation of risk systems.
Looking Ahead
Risk management is past the digital transformation stage and entering the age of intelligent transformation. The next 12 to 18 months will see AI move from pilot programs to practical adoption. As capabilities mature, and as data quality and governance improve, AI will become woven into every aspect of risk operations, from claims and underwriting to safety, compliance, and resilience.
For now, the priority for risk leaders is simple: start somewhere. Develop a strategy. Define use cases. Evaluate your data. And most importantly, experiment. The organizations that treat AI not as a trend but as a long-term capability, supported by clear strategy, responsible governance, and continuous learning, will be the ones that lead the next era of risk management.
Ready to Build Your AI Strategy?
Redhand Advisors helps organizations move from AI curiosity to capability through our AI Readiness Workshop, a structured, vendor-neutral process designed to help you define use cases, assess data maturity, and identify practical next steps.
Whether you’re exploring how AI can enhance claims, underwriting, or risk analytics, our team helps you cut through the noise and build a roadmap that aligns with your goals.
Contact us to schedule an AI Readiness Workshop and start turning your AI ambitions into actionable results.