It’s not uncommon to hear insurance professionals say, “We tried artificial intelligence (AI) before, and it didn’t work,” or “it’s too complex for us.” These sentiments may point to a deeper issue: learned helplessness — a psychological phenomenon in which repeated setbacks lead to inaction, resistance to change, and a belief that innovation is out of reach.

In an era where AI is transforming claims processing, underwriting, fraud detection, and customer experience, this mindset is a major threat to growth and competitiveness. While 77% of carriers are leveraging AI to streamline operations and gain market advantage, insurers are at vastly different levels of maturity with these efforts, and many insurers are getting stuck on the way to full adoption.

This blog explores why learned helplessness is so prevalent in the insurance industry, how to recognize the signs, and what steps forward-thinking carriers can take to reignite AI innovation.

Why Does Insurance Lag in AI Innovation?

The insurance industry is trailing behind others, such as tech, finance, and retail, that have embraced AI-driven transformation. While AI innovation initiatives can fail for many reasons, failure alone isn’t the problem. The real problem often lies in how the organization responds to that failure, and what that response means for future innovation attempts.

Why does learned helplessness seem to affect insurance carriers more than other organizations? There are a couple of reasons.

Insurers are in the business of managing risk, and that risk-averse mindset extends to internal innovation. Many carriers still operate on decades-old core systems that are costly and difficult to replace or integrate with AI solutions. A recent study found that 65% of claims professionals cite a lack of integration between systems as a major obstacle to AI innovation attempts.

On top of this, the insurance sector is among the most heavily regulated industries, with stringent guidelines governing underwriting fairness, claims handling, and customer data protection. Concerns over regulatory scrutiny often lead to a “wait-and-see” approach, where carriers delay AI adoption until competitors demonstrate its feasibility. The National Association of Insurance Commissioners (NAIC) has even issued guidelines emphasizing that AI-driven decisions must comply with all applicable insurance laws and regulations, adding perceived layers of complexity to AI implementation.

Even when proofs of concept succeed, they rarely move beyond the pilot phase. A 2024 Insurance Business report found that while 70% of insurers plan to adopt real-time AI models within two years, actual adoption remains below 30%. When no internal AI champions advocate for transformation, and projects are siloed within IT, AI initiatives lack strategic visibility. This often results in scattered efforts lacking executive buy-in, clear goals, or long-term investment.​

To top it all off, insurers struggle to compete for AI talent. Unlike banks or tech firms, carriers often rely on legacy infrastructure and move at slower innovation cycles — making it harder to attract and retain skilled professionals. Many insurers default to outsourcing, but without a strong internal foundation or business-aligned strategy, vendor-led initiatives often fall short.

4 Steps for Breaking Free From AI Paralysis

Overcoming learned helplessness is crucial for insurance companies aiming to harness the transformative potential of AI. By adopting strategic approaches, insurers can encourage a culture of innovation and effectively integrate AI into their operations.

Here’s how:

1. Shift from a failure mindset to a learning mindset.

According to EY, 43% of insurers haven’t implemented generative AI (GenAI) — let alone agentic AI — into customer-facing applications due to potential risk. To overcome this fear-based mindset, organizations need to view innovation as a learning opportunity, even if it ultimately results in a project failure.

Start by conducting thorough post-mortems on previous AI initiatives. These conversations can uncover valuable insights in areas such as data quality, integration challenges, and expectation management.

Fostering a culture that encourages experimentation allows organizations to pursue incremental improvements rather than seeking immediate, large-scale transformations.

2. Start with small, measurable AI wins.

Allstate began by applying GenAI in customer communications — a low-risk, high-impact use case that led to better service and operational efficiencies. By integrating OpenAI’s GPT models with company-specific terminology, Allstate found that AI-generated emails were less jargony and more considerate than those written by human representatives. Currently, most of the 50,000 daily communications from Allstate’s 23,000 representatives to claimants are written by AI and verified by humans for accuracy.

Any insurer can and should take the same low-risk, high-value approach to build confidence and demonstrate tangible benefits. For example, deploying AI for automated claims document processing, fraud detection, or customer service chatbots can lead to measurable improvements.

Establishing clear key performance indicators (KPIs), such as reductions in claims processing time or increases in detection accuracy, enables organizations to assess success effectively. Early successes can generate internal momentum and secure executive buy-in, facilitating broader AI adoption.

3. Invest in an AI Center of Excellence or model office.

Establishing a dedicated, cross-functional team comprising business leaders, data scientists, and IT professionals can drive AI innovation. A well-structured Center of Excellence (CoE) provides a centralized space to share best practices, develop governance models, mitigate risks, and align AI initiatives with business strategy.

In addition, creating an AI sandbox environment allows for the testing of new models without disrupting existing systems. Collaborations with insurtechs, academic institutions, or AI vendors can further accelerate learning and experimentation.

Farmers Insurance offers a strong example of this in action. The company established an AI CoE to streamline operations and enhance customer experience by centralizing its AI efforts across business units. This structure enabled more consistent execution, better resource allocation, and faster delivery of AI solutions.

4. Cultivate internal AI champions.

Identifying and empowering advocates within various business units can facilitate AI-driven transformation. Providing training on how AI can enhance roles helps reduce resistance and demystify the technology. When employees understand that AI is meant to support — not replace — their work, they’re more likely to embrace it. Hands-on training for decision-makers also ensures informed, strategic support for AI initiatives across the organization.

One Fortune 500 insurer recently deployed AI and aerial imagery to identify property risks like damaged roofs and undocumented swimming pools. By enabling internal business and technical teams to lead the charge, the initiative achieved $3.5 million in savings, exceeding expectations, and is projected to deliver over $27 million in long-term benefits. The success was driven not only by the technology itself, but also by empowering the right people within the organization to champion its use.

Together, these strategies create the foundation insurers need to break free from stagnation and move confidently toward enterprise-wide AI innovation.

From Inaction to AI Leadership

By fostering a culture of learning, starting with achievable AI wins, building internal champions, and tapping into external expertise, insurance carriers can move beyond stagnation and toward meaningful, scalable innovation. The future of insurance is undeniably AI-powered — and the organizations that act now will be best positioned to lead the charge.

For carriers that have previously struggled with AI adoption, the path forward starts with one shift: choosing to learn, adapt, and try again.

To learn more about AI and how it will evolve within insurance, read my e-book, “The History of AI in Insurance and Where It’s Headed.”