Leading Beyond the Hype: How Life Insurance Executives Can Evaluate AI Without Being Experts

In today’s life insurance landscape, artificial intelligence is no longer theoretical. It is embedded in underwriting workflows, shaping distribution strategies, and redefining customer expectations.

Yet for many executives, a paradox persists: AI is increasingly central to strategy, but remains only partially understood.

Key Insight: Effective AI leadership does not require technical mastery. It requires disciplined evaluation.

Moving from “Impressive” to “Useful”

One of the most common pitfalls in AI evaluation and deployment is confusing capability with organizational value. A model that produces impressive outputs in a demonstration does not necessarily solve a meaningful business problem.

For life insurance leaders, the more productive question is not “Is this AI good?” but “Is this useful to our objectives?”

Three Criteria Help Anchor This Evaluation

• Relevance: Does the AI address a prioritized business problem, such as underwriting speed or claims accuracy?
• Reliability: Does it perform consistently under real-world conditions, including imperfect data?
• Return: Does it produce measurable improvement over current processes?

AI should be judged relative to existing performance, not against an idealized standard of perfection.

A Practical Scenario: Underwriting Acceleration

Consider a carrier evaluating an AI-driven underwriting tool that analyzes medical records and third-party data to accelerate decision-making.

• When tested on less structured medical records, output quality varies significantly.
• Certain edge cases, such as applicants with complex comorbidities, produce inconsistent risk assessments.
• The model’s rationale is difficult to explain in terms that would satisfy regulatory scrutiny.

A surface-level assessment might greenlight rapid deployment. A more calibrated approach reframes the opportunity: the tool may still be valuable, but best positioned as a decision-support layer for underwriters, rather than a fully autonomous system.

This distinction between replacement and augmentation is where many AI investments succeed or fail.

Five Ways to Evaluate AI Without Technical Expertise

1. The Second-Order Question

Ask: What happens after this works?

A faster underwriting decision, for example, may require changes in audit processes, compliance oversight, or agent communication. Value is only realized if downstream systems adapt.

2. The Edge Case Probe

Do not focus on average performance. Ask where the system fails.

Prompts such as “Show me when this breaks” often reveal more than success cases.

3. The Human Baseline Comparison

Compare AI performance to your current average employee, not your top performer.

This creates a realistic benchmark and highlights where augmentation is viable.

4. The Repeatability Check

Run the same input multiple times. If outputs vary significantly, the system may introduce operational risk, particularly in regulated environments.

5. The Explainability Threshold

Ask whether decisions can be explained to regulators, customers, or auditors.

In life insurance, this is not optional for high-stakes decisions.

Avoiding Common Traps

• The Demo Fallacy: Polished demonstrations can obscure real-world variability.
• The Authority Shortcut: Over-reliance on experts, consultants, or influencers limits critical thinking.
• Binary Framing: Labeling AI as either transformative or useless ignores nuanced value.
• Premature Scaling: Expanding before understanding limitations compounds risk.

Disciplined skepticism is not resistance, it is a necessary component of sound decision-making.

Confidence Through Calibration

AI will continue to reshape the life insurance industry, but the pace and nature of that change will vary widely across organizations. The differentiator will not be who understands the technology best, but who evaluates it most effectively.

The goal is not to eliminate uncertainty, but to navigate it with discipline. Executives who ground their decisions in calibrated judgment rather than overconfidence or hesitation will be best positioned to capture AI’s value while managing its associated risks.