Insurance Turns to AI as a Critical Enabler to Boost Agility

Insurance is being shaken by a trio of climate- and economy-driven challenges; severe natural disasters, inflation, and high interest rates have driven withdrawals and nonrenewals–leaving both agents and policyholders scrambling.

Alongside this, the sector is also facing another layer of disruption in the form of technology and customer shifts. Huge advancements in artificial intelligence (AI) have meant that speed and precision are now baseline expectations, and the industry is seeing faster underwriting and claims processes as a result, as well as better risk scoring and fraud detection.

However, the idea of AI as a driver of agility–the ability to rapidly scale capacity, instantly apply improvements across the organization, or adapt decision-making in real-time based on the latest data–is still a relatively new concept in insurance conversations.

Despite the fact most traditional carriers are only just beginning to explore AI in this sense–especially given how legacy systems and compliance processes can slow agility–the untapped potential for AI-driven agility to transform how insurance operates is enormous.

Big Picture Thinking

So, how is AI bringing agility into insurance operations for both agents and carriers? Looking ahead, the industry is already seeing early indicators of AI’s agility in its potential to quickly strengthen the fundamental connections between agents and carriers. Advanced analytics can help agents identify the right carrier fit for their specific needs and risk profiles, while the same technology could work bidirectionally, enabling carriers to identify and connect with agents who align with their business strategies and distribution goals. This creates more strategic, data-driven partnerships rather than relying solely on traditional relationship-building.

Beyond this, for agents, AI can make decades of industry data available for easy analysis, helping even new professionals to deliver seasoned-level insights to customers. With consulting firm-quality research and analysis available to decision-makers at every level, complex questions that traditionally took hours or days to resolve can be achieved within minutes.

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.

The common thread across all these applications is speed of response to change–an ability that’s becoming increasingly essential as the pace of change accelerates across insurance, driven by volatile markets, extreme weather events, and shifting risk appetites. Where traditional processes are constrained by human resource limitations, training timelines, and system dependencies, AI provides a much more agile foundation for insurance operations.

AI-Driven Agility in Action

The most immediate and transformative impact of AI-driven agility lies in its ability to scale capacity overnight. Traditional scaling requires a months-long process of hiring, training, and waiting for new employees to become productive–a timeline that can stretch from one to six months before seeing real results. AI eliminates this bottleneck entirely.

Consider phone call management, where carriers historically struggled to handle influx periods or expand outreach capacity. In a traditional setup, 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, 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, not only can that capacity be dialed up overnight, but experimentation is also far simpler and faster. 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. 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. That ability to respond very quickly to business conditions, needs, and insights is the textbook definition of agility.

Market adaptation is another critical area where AI delivers real agility.

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.

Future Insurance Operations

Where will AI-driven agility transform future insurance operations? As it stands now, we’ll start to see AI help more agents handle the day-to-day tasks that can be a headache–routine tasks, calls, standardized processes, and keeping service consistent despite high staff turnover. AI can also fill the gaps where hiring and training have traditionally been a struggle. Carriers, too, will start to see more immediate benefits as they embrace AI by streamlining operations, improving claims processing, and ensuring more consistent service across their networks.

Looking further down the line, AI’s capabilities could have an even bigger impact.

The industry could see AI acting 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.

AI could also transform 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.

The common thread here is there’s a much quicker way to respond to change–an ability that’s becoming increasingly essential when change itself is accelerating across insurance, whether by volatile markets, extreme weather, or shifting risk appetites.

Pinkovezky is the CEO of First Connect. Website: www.firstconnectinsurance.com.

Topics InsurTech Data Driven Artificial Intelligence