AI is creating real opportunities for insurers and risk managers to work faster, make better use of data and improve decision-making. However, the industry's vocabulary around the technology is still catching up to the reality of how AI should and is being used in insurance and risk management.
The latest phrase showing up in conversations is "AI agent." AI agents are tools that can reason, act and move work forward either autonomously or with limited human involvement. AI agents definitely have their uses, but the term is often applied more broadly than it should be. In many cases, the tools used in insurance are not fully autonomous agents that make decisions independently — and for good reason.
A more accurate description might be something like: AI-enabled workflow steps designed to summarize information, extract data, recommend next steps or automate specific tasks.
The distinction between autonomous and AI-enabled systems matters. In highly regulated industries such as insurance and risk management, autonomy can create greater risk. Organizations don't need autonomy for autonomy's sake, but rather AI that helps them move faster, reduce manual work and generate better insights to improve human-led decision making.
The goal is not to deploy an AI system that can make decisions on its own. In many cases, a "digital worker" that replaces an employee would not be a valuable use of AI. Instead, when intelligence is implemented at the right point in an existing workflow with the right controls around it, employees are empowered to make the best decision for the business.
Insurance decisions carry legal, financial and human consequences. We in the industry are all acutely aware that a claim decision, underwriting action, safety recommendation or risk assessment carry entirely different stakes than an automated calendar update. These decisions need to be explainable, consistent and auditable. When regulators, customers or internal stakeholders ask why an action was taken, the organization needs to be able to answer. Without human oversight, organizations could risk losing trust and credibility from clients.
Straight-through processing is a useful example. Technically, AI and automation could move some claims or underwriting processes from intake to decision with little human involvement.
But what we hear from clients is that they are not intending to hand over that authority, and in many cases, the regulatory environment wouldn't support it anyway. Their teams want to decide where AI belongs and where expert human judgment remains essential. That's the priority, and it's why the industry conversation needs to reorient around intelligent workflows as the real opportunity, rather than selling autonomy as the goal.
The most useful AI systems in insurance will not force every organization into the same prebuilt use case. Insurers, risk managers, claims teams, underwriters and safety professionals all have different pain points. Some organizations may want to summarize claim notes, and others may want to generate draft communications or flag missing information. These two organizations don't need the same AI solution.
If each use case has to be hard-coded one at a time, innovation will move too slowly. A more scalable approach is to give organizations configurable building blocks: actions that can summarize, extract, map, analyze, generate or route information, then allow teams to assemble those actions in the way that fits their business process.
In other words, everyone starts with the same set of pieces, but each organization builds something different. This gives companies the flexibility to solve the problems that matter most to them.
This approach reframes what "agent-like" capabilities can mean in practice. AI can make a workflow faster without being fully autonomous. It can read a document, extract key fields, summarize the content, recommend next steps or draft an email for review. Those capabilities can significantly improve efficiency while still allowing people to approve, adjust or override the output.
Balance is important because trust remains one of the biggest barriers to AI adoption. Many professionals are still getting comfortable with AI in their everyday work. While some are excited by its potential, others worry about accuracy, accountability or job replacement. Technology providers have a responsibility to educate users, provide transparency and deliver tools that feel useful rather than threatening.
Digital transformation has always been about helping people and organizations become more efficient and resilient. AI can advance that mission when it removes friction, strengthens decision-making and helps professionals focus their expertise where it matters most. Insurers and risk managers aren't looking for AI agents that operate beyond their control; they want AI-powered workflows that make their people better equipped to manage risk.
