AI in insurance has long been discussed in terms of speed and precision; enabling faster underwriting and quicker claims processing, or better risk scoring and fraud detection.
But a new conversation is emerging - one that sees AI not just as a tool for efficiency, speed, or simply automation, but also as a means of creating agility.
It's a shift that couldn't be more timely. The industry is being reshaped by a perfect storm of disruptive forces, all unfolding at once and all demanding faster, more flexible ways of working. Natural disasters have rewritten risk profiles across regions, persistent inflation pressures have pushed carriers to shift focus from premium volume to profitability, and high interest rates have driven withdrawals and non-renewals. Meanwhile, huge advancements in technology and abrupt customer shifts have seen baseline expectations rise even further, especially among the more digitally savvy.
The result is that many agents and carriers struggle to keep up, with traditional legacy systems, compliance processes and slow decision-making cycles slowing them down.
Agility - which, in these terms, is classified as the ability to rapidly scale capacity, instantly apply improvements across an organization, or adapt decision-making in real time to new data - is fast becoming the industry's most valuable currency.
How AI is already bringing agility into insurance
As it stands now, the industry is already seeing early indicators of AI's ability to boost agility where traditional processes are constrained by human resource limitations, training timelines, or system dependencies. Examples include:
- Real-time market adaptation: When carriers shift their risk appetite or market conditions change rapidly, traditional systems rely on API updates or core system changes that can be painfully slow, inconsistent, or lacking in nuance. AI provides remarkable flexibility here - new information from documents, marketing brochures, or support tickets can be ingested directly into the AI engine, enabling immediate updates to underwriting logic and business processes. This means insurers can respond in real time to changing market conditions instead of being constrained by legacy system limitations.
- Strengthening agent-carrier connections: Not only can advanced analytics help agents identify the right carrier fit for their specific needs and risk profiles, but the same technology can work bidirectionally - enabling carriers to identify and connect with agents who align with their business strategies and distribution goals. This approach complements traditional relationship-building with a more strategic, data-driven method.
- Making data analysis easy - and accessible: For agents, AI can make decades of industry data available for easy analysis, helping even new professionals deliver seasoned-level insights to customers. When consulting firm-quality research and analysis is available to decision-makers at every level, complex questions that traditionally took hours or days to resolve can be figured out in minutes.
- Improved speed and operational consistency: AI is known for its speed and consistency, but being able to respond to change in real time enables true agility. For carriers, this extends to claims processing, underwriting, and employee workflow optimization, where AI has demonstrated completion rates that actually exceed human performance while delivering more consistent service across networks.
- Ability to scale capacity overnight: AI eliminates the bottleneck that traditional scaling causes. In phone call management, for example, if a carrier wanted to scale up outreach or avoid missing an influx of incoming queries, they'd need to post job listings, interview, negotiate, hire, onboard, and train - a lengthy process that for entry- to mid-level roles can take anywhere from one to six months before anyone is truly productive. With AI, that capacity can be dialed up overnight.
- Experimentation is far simpler and faster: Easing the process of experimentation allows organizations to respond very quickly to business conditions, needs, and insights. Following on with the phone call management example, traditionally, testing different scripts for handling in- and outbound calls means splitting agents into groups, measuring results, deciding which approach works best, and then retraining people. However, if a new script or approach works better with AI, it can be applied instantly across every interaction, without the slow grind of retraining or overcoming resistance to change.
What AI-driven agility means for the future of insurance
While all of the above is already starting to improve agility across the industry, if we look a bit further down the line, AI's capabilities could have an even bigger impact. It could, for example, act like a virtual sub-agent, capable of finalizing or even binding straightforward policies. A bit like self-service, but with the reassurance that a human agent is still there, making customers feel comfortable while speeding up the process.
It could also affect the traditional quoting experience, such as comparative quoting. In this sense, AI wouldn't just pull APIs and return quotes, deductibles, premiums, and limits - it could also provide deeper insights into the nuances of each coverage. For example, it could draw on customer reviews, past issues, and other relevant data to give a more complete picture. In this way, it could act as a force multiplier, enabling agents to deliver richer, more informed advice to customers. Even a new agent could have decades of experience at their fingertips, helping them provide the same depth of insight as a seasoned professional.
Regardless of which possibility we explore in the future, the main point here is that AI can, and very much should, provide a much quicker, more agile way to respond to change - an ability that's becoming increasingly essential as change itself continues to accelerate across insurance.
