Why Now Is the Time for Insurance Organizations to Embrace AI (Regulators have spoken)
Key Points
Regulators are focused on how AI is governed, not whether it's allowed.
Strong governance is becoming a competitive advantage.
Delaying adoption creates compounding operational, regulatory, and reputational risk.
Let's address the elephant in the room. AI in insurance is here, and it's not going away. It exists in budget lines, pilots, and live workflows across underwriting, claims, fraud detection, and customer service. In fact, roughly three out of four insurers now use generative AI in at least one business function, making its presence undeniable.
What's changed most recently is not the use of technology, but the regulatory direction. Lawmakers and regulators are increasingly clarifying how AI systems should be governed, especially as they become part of core operations.
As regulations become clearer and more standardized, the race to implement AI will intensify. Insurance providers and distributors that still rely on manual processes and legacy systems will find it increasingly difficult to compete with peers who have already modernized their operations and built governance frameworks that align with regulatory expectations.
Regulators Are Giving the Green Light
Regulatory clarity is emerging, and it's surprisingly permissive. Recent guidance from policymakers provides frameworks that allow regulated industries to move forward with confidence.
Case in point:
In 2024, lawmakers introduced the Roadway Safety Modernization Act, a bipartisan bill that encourages states to shift from reactive to proactive risk mitigation by adopting technologies such as AI and telematics.
While not insurance-specific, the message translates across risk-based industries: advanced analytics and AI aren't just permitted, they're encouraged when used responsibly.
As AI legislation matures (and it will), carriers that delay adoption face tougher competition from peers who have already built operational and governance muscle.
What Regulators Expect
Many insurance organizations hesitate to implement AI at scale because they may have questions when it comes to:
What's permitted
What must be documented
Who owns oversight
How regulators will assess outcomes
The good news: regulators are already answering these questions.
The Baseline Set by the NAIC
The NAIC Model Bulletin on AI Systems (2023) establishes a practical floor for AI governance in insurance. Let's be clear: it doesn't restrict AI use. In fact, it explicitly supports innovation that contributes to safe and stable markets.
At a high level, it expects carriers to:
Maintain formal AI governance programs
Assign accountability to senior management and the board
Apply oversight across the full insurance lifecycle
Manage AI from design through retirement
Govern both internal and third-party AI systems
Provide appropriate transparency to consumers
By 2025, over half of U.S. states had adopted the bulletin or issued similar guidance. Insurance organizations now have a clear framework to follow, reducing the compliance risk that once accompanied AI adoption.
Late Adoption: What's Actually at Stake
Maintaining the status quo may feel safer in a traditionally conservative industry. But those who operate with a “business as usual” mindset will find themself lagging behind innovators and early movers.
Here's what's at risk for late adopters:
Slower Growth and Competitive Drag
Carriers that only experiment with AI (or avoid it entirely) fall behind those that embed it into core workflows. Research from McKinsey & Company shows that AI leaders outperform laggards in growth and shareholder returns, and the gap continues to widen. Once AI-native competitors reach scale, catching up becomes harder.
Higher Costs from Inefficiencies
Manual processes are slow, expensive, and error-prone. That's why early AI adopters who redesign workflows gain lasting cost advantages. This trend is reflected across the industry. For example, insurance companies report a 40% reduction in onboarding costs after modernizing operations.
Late adopters face a double setback: they miss out on these gains and must compete against organizations that have already captured them.
Customer Expectations Reset
Customers have come to expect rapid service delivery and highly personalized experiences—expectations shaped and elevated by their interactions with e-commerce platforms and fintech applications. Insurance providers and distributors are now being measured against these standards.
So what are modern customers actively seeking in their insurance experiences?
Faster claims resolution
Clear, timely communication
Personalized interactions
Self-service options
Carriers that lag look slow, opaque, and feel frustrating by comparison. The reality is that customers won't wait for transformation plans when better experiences already exist.
Talent Drain and Innovation Slowdown
Modern tools attract modern talent. In contrast, firms that lag in digital transformation struggle to retain and attract skilled employees, especially tech-native employees capable of building, governing, and improving AI systems. When talent leaves for greener pastures, innovation slows. When innovation slows, the competition gap widens.
Greater Exposure to Fraud and Risk
AI is increasingly critical for detecting sophisticated fraud and managing emerging risks. As these threats grow, traditional methods become increasingly inadequate compared to modern detection capabilities. Firms that fail to modernize respond more slowly and are more vulnerable.
As a result, providers must rely on blunt measures such as premium increases to offset losses. This approach isn't sustainable and carries reputational risks when preventable issues escalate.
The Real Competitive Advantage
There was a time when calling an organization "AI-powered" set it apart from competitors. Today, nearly every carrier is experimenting with AI in some capacity. The real differentiator now is how effectively organizations implement and scale AI across their operations.
Despite widespread experimentation, only about 7% of insurers have scaled AI beyond pilots. Most remain stuck with testing tools without enterprise ownership.
When AI stays in pilot mode, carriers miss out on:
Compounding efficiency gains
Institutional learning from live deployment
Faster responses to regulatory or market shifts
Internal trust needed for adoption at scale
Meanwhile, competitors who treat AI as infrastructure pull ahead. The convergence of regulatory clarity and competitive pressure has created a unique window of opportunity for insurance organizations to modernize with confidence.
What Modernization Actually Requires
Successful modernization is all about operating discipline. In practice, that means building reliable systems, clear governance, and cross-functional alignment that allow AI to deliver consistent, measurable value.
Insurance providers that modernize effectively focus on:
Strong foundations: Clean, well-documented data and clear ownership across teams
Formal model risk management: Continuous monitoring, testing, and documentation over time
High-impact use cases: Claims, underwriting, fraud detection, and customer service, where value is clear and measurable
Shared accountability: IT, actuarial, compliance, operations, and business leaders operating together, not in silos
Organizations that build governance frameworks today will set the standard for responsible innovation in insurance tomorrow.
A Final Thought
Regulatory guidance around AI will only increase—federally, at the state level, and globally. And as regulations crystallize, the competitive gap between leaders and laggards will grow. Organizations that turn AI use cases into real infrastructure will move faster, earn trust, and adapt as expectations rise.
This brings insurance players to a critical decision point:
The question they’re asking today isn't whether to modernize. It's whether to do it intentionally now, or under pressure later.