Overcoming AI adoption challenges for life insurance

Artificial intelligence is undeniably revolutionizing the insurance industry. Many organizations are planning to double their AI budgets in the next few years, and 90% of carriers are currently in the process of adopting AI. But while the technology has shown great promise in improving underwriting, customer experience, risk assessment, fraud detection, and operational efficiency, AI has been greatly under-deployed in the life insurance industry.

Part of the slow adoption rate can be attributed to an unwillingness to justify the high investment AI requires, despite the widely accepted belief that it will ultimately be a cost-saver for the industry. Last year, the financial sector spent $20.9 billion on AI research and development, and it's predicted that AI could save the life insurance industry $300 billion annually. However, concrete figures demonstrating the real money saved and the subsequent boosted operational efficiency have yet to fully and accurately materialize.

The other factor limiting AI adoption is the nature of the industry itself. Life insurers collect and retain terabytes of sensitive information on their policyholders, which requires they employ a high level of diligence and assurance when implementing emerging technologies and can slow down deployment. Issues surrounding ethics, regulations, technical readiness, and organizational hurdles can also hinder AI implementation. As AI technology and its adoption continue to promise transformation, life insurers will need to remedy these core issues if they want to capitalize on the potential of AI.

Limited regulations complicate the path forward

Due to the rapid pace at which AI is advancing, it is nearly impossible for carriers to keep up with how quickly the innovation is evolving, and regulators are equally struggling to close the gaps.

Because there's no official national law about AI usage in life insurance, a bulletin issued by the NAIC has become the de facto guidance to supplement the patchwork of state-specific laws. The bulletin promotes developing and adopting AI systems into organizational frameworks, but it directs insurers to the Principles of Artificial Intelligence — a document that highlights fairness, ethics, accountability, and transparency — for further guidance.

While neither of these documents are official national regulations, they offer a strong starting point for insurers and a clear message: carriers should establish explicit AI model decision pathways. But that also comes with its own complications.

The inability to trace and audit AI is one of the biggest barriers to implementation

Life insurers have an obligation to regulators, auditors, and policyholders to understand and explain how and why decisions were made. With traditional rules-based systems, explaining the decision-making process is easy and produces clear audit trails. For example, since X and Y were true, rule A was applied. But AI, particularly machine learning and generative AI, doesn't work that way.

AI models operate probabilistically, weigh hundreds of variables in ways that aren't easily deconstructed, and can drift over time or hallucinate as they're retrained. This creates a number of problems. If a specific applicant asks why they were rated in a specific way, insurers aren't able to explain the reasoning. If a complaint or lawsuit is filed related to a decision made five months or five years ago, it's difficult to reconstruct the model's previous state, the data it ingested, and the outcome. If the model starts to behave unexpectedly, insurers need to determine how they can detect misbehavior and identify the responsible party.

There are emerging technologies that can help carriers track and document how their models come to specific conclusions, but many aren't specialized for the life insurance industry and require integration into existing workflows, alignment with compliance and legal teams, and ongoing operational discipline.

For insurers to implement AI, they need a life insurance-specific technology solution that can provide visibility into whether AI models are behaving as expected over time, generate an audit trail of all processed activities, and show the model's "work" and data sources.

An AI-forward culture is essential

While many organizations have been touting the potential benefits of AI, employees are a bit more skeptical. Overall, 52% of U.S. workers are worried about how AI will be used at their jobs, and 32% think it'll lead to fewer job opportunities. As AI is rolled out in life insurance, carriers must consider what internal non-technical changes and training need to be implemented to support deployment and create an environment where the technology isn't feared or overwhelming.

Part of this is training all employees, not just IT teams, on how to use it, as 61% of desk workers reported spending less than five hours learning how to use AI, and 30% said they've had no training at all. Without guardrails or guidelines on how to use AI in their day-to-day work, or which chatbot should be used, carriers risk exposing themselves and their customer data.

Educating all employees on how to use the technology to streamline operations, generate analytics, and understand which tools have data privacy agreements in place can help build an internal AI readiness that effectively balances automation with human expertise.

What are leaders doing now and in the future?

Consider what progressive carriers are doing today. They're deploying LLMs to read and interpret policy documents, extracting key information in seconds rather than hours. Machine learning models are handling routine underwriting decisions, flagging only exceptions for human review. Agentic AI, which refers to autonomous systems that act on predefined rules and learned patterns, is now managing entire customer service workflows from initial inquiry to resolution

These capabilities do not deliver value in isolation. The carriers getting results are integrating LLMs, machine learning, and agentic AI into a coherent architecture. Each layer has a defined role: LLMs handle language and intent, machine learning evaluates risk, and agentic AI executes the workflow, with all three drawing from a consistent, centralized data model.

This integration is what separates the leaders from the laggards. While many carriers are experimenting with ChatGPT for customer service or isolated machine learning models for fraud detection, the winners are implementing integrated AI platforms or suites of components that transform entire operational workflows — from underwriting to customer service to claims. They're not just automating tasks; they're reimagining how insurance operations function.

And even as they're optimizing operations by automating and increasing efficiency, they are beginning to look to leveraging those integrated AI-enabled components to consistently drive new streams of revenue by finding hidden opportunities in vast data sets and setting AI agents to manage specifically targeted campaigns to take advantage.

AI can change the future of life insurance

AI stands to fundamentally change how life insurers underwrite, serve customers, and compete but making it work for life insurance requires overcoming critical hurdles, including ethics, regulation, technical readiness, and building a culture that embraces AI.

As carriers explore the possibilities, it's essential not to let the trends dictate decisions and remember that true innovation comes from careful evaluation and alignment with a clear business strategy.

For small and mid-sized carriers, building AI solutions from the ground up is often impractical and costly. Meanwhile, big tech providers may offer impressive AI capabilities, but their solutions are typically broad and lack the specialized expertise life insurance demands.

A smarter approach is partnering with experts who understand the industry. Experienced third parties can objectively assess where AI delivers the most value and guide insurers toward practical, high-impact applications.

The right AI partner doesn't just provide technology; they bring industry-specific expertise that helps carriers move from experimentation to execution, avoiding costly detours along the way.