If People Need AI to Understand Your Product, Maybe the Product Isn’t Explained Very Well

WHAT THE REPORT FOUND

• 47% of consumers surveyed are considering buying life insurance; more than 40% of that group feel confused, uncertain or unconvinced by the information they find.
• Technical language was cited by 37% as a barrier, ahead of affordability concerns at 35% and lack of relevance to life stage at 25%.
• 51% plan to use generative AI to research and compare products, while 85% still want human-advisor interaction somewhere in the journey.
• 57% of people with employer-provided coverage say they feel generally confident in it despite never formally checking whether it fits their needs.

Life insurance is supposed to sell certainty. The new World Life Insurance Report 2027 suggests the industry has found a way to make the shopping experience feel like the opposite.

The report, produced jointly by the Capgemini Research Institute and LIMRA, surveyed 6,175 consumers across 18 countries between April and June 2026. It found that 47% are considering buying life insurance, but more than 40% of those potential buyers say the information they encounter leaves them confused, uncertain or unconvinced. One in four abandons the purchase journey before completing it.

That is not a demand problem in the usual sense. People are showing up. They recognize the product, consider the need and enter the process. The breakdown happens in the space between interest and comprehension, where a product designed to address one of the most basic forms of financial anxiety becomes another source of it.

The Product Is Important. The Explanation Is the Problem.

Technical language was the most commonly cited barrier to purchase at 37%, followed by affordability concerns at 35% and a lack of relevance to the consumer’s life stage at 25%. Those numbers matter together because they describe three different kinds of friction: people do not always understand what the product is saying, do not always know whether it is financially realistic, and do not always see how the thing being sold maps to the life they are actually living.

An industry can treat those as separate marketing problems. From the consumer side, they can feel like one continuous experience: I am not sure what this means, I am not sure what it costs in a way I can compare, and I am not sure whether this is even meant for someone in my situation. By the time the person has worked through all three questions, the easiest decision may be to close the tab.

That is where language stops being cosmetic. Plain language is often discussed as if it were a tone preference, a friendlier way to say the same thing. In a high-stakes financial product, it is infrastructure. A term that is technically accurate but functionally opaque still creates work for the person who has to interpret it, compare it and decide whether it matters. Every unexplained phrase becomes another small task added to a decision the consumer may already be postponing because the subject is death, money, family responsibility or all three at once.

Capgemini’s Samantha Chow put part of the problem plainly, saying that “complexity at the point of purchase and post-sale silence undermine policy ownership.” The second half of that sentence matters as much as the first. Nearly 40% of policyholders surveyed said they rarely hear from their life insurer after purchase, while half of consumers who discontinue coverage do so within the first three years. The communication problem does not end when the policy is issued; in many cases, the relationship appears to go quiet just after the consumer has made a long-term commitment.

AI May Be the Interpreter, Not the Advisor

The report’s artificial-intelligence finding is more interesting than a standard “consumers are adopting AI” headline. Fifty-one percent of respondents say they plan to use generative AI to research and compare life-insurance products. At the same time, 85% still want a human advisor involved somewhere in the process, and two-thirds prefer a human when making the final coverage decision.

Those figures do not describe a clean handoff from people to machines. They suggest a layered process in which AI may be useful precisely because the institutional material is difficult to navigate. A consumer can ask a chatbot to translate a term into ordinary language, summarize differences between products, generate a list of questions for an advisor or explain why two policies that look similar are not. The report does not tell us exactly how people will prompt these systems, but the combination of high AI interest and even higher demand for human interaction points toward interpretation and preparation rather than simple replacement.

That creates a strange new role for generative AI. The technology may become the unofficial translator standing between consumers and institutions whose own language did not fully do the job. The customer encounters the insurer’s terminology, takes it somewhere else to make it legible, then returns to a human for reassurance and judgment before committing.

There is value in that. A tool that helps someone formulate better questions can make a complicated decision less intimidating. But it also raises an uncomfortable design question for insurers: if a large share of prospective customers feel they need an external AI system to understand or compare the product, what exactly is the insurer’s own communication layer accomplishing? Adding a chatbot to a confusing journey is not the same thing as making the journey clear.

Feeling Covered Is Not the Same as Knowing You Are Covered

The employer-benefits finding exposes a different kind of friction because, on the surface, there may be no friction at all. Fifty-seven percent of people with employer-provided life insurance say they feel generally confident in that coverage even though they have never formally assessed or validated whether it actually meets their needs.

That gap is psychologically easy to understand. Workplace benefits arrive with the authority of the employer, the familiarity of an enrollment portal and the reassuring status of a checked box. Once the benefit appears on the confirmation screen, “I have life insurance” can quietly become “I am adequately insured,” even though those are not the same statement.

U.S. consumer guidance from the National Association of Insurance Commissioners makes that distinction explicit. The NAIC tells consumers to ask whether workplace life insurance is enough to meet their obligations and to evaluate coverage against factors such as income replacement, dependents, debts and future expenses. It also notes that employer coverage may not follow a worker when they leave a job. The point is not that employer-provided insurance is bad. It is that enrollment is not evaluation.

This is a useful example of default confidence: the feeling of security created by the presence of a system, a benefit or a label before anyone has tested whether the thing actually matches the underlying need. The same behavioral pattern shows up in plenty of other places — a privacy setting left on its default, a warranty nobody has read, a backup system nobody has restored from, a retirement contribution chosen once and never revisited. The presence of protection and the adequacy of protection can feel identical right up until the moment reality asks for proof.

Confusion Becomes a Conversion Problem

There is a temptation to talk about confusing financial language as though opacity itself is the industry’s business model. That overstates the evidence; the Capgemini-LIMRA report does not show insurers deliberately trying to confuse anyone. What it does show is that confusion has a measurable commercial consequence. People who are interested in the product are dropping out before they buy it, and people who do buy may receive too little ongoing guidance to keep the relationship strong.

That changes the incentive. Complexity can persist for years when its cost is dispersed across frustrated customers, abandoned research and vague feelings that a product is “not for me.” Once the same complexity shows up as failed conversion, early lapses and lost relationships, clarity stops being a courtesy and becomes an operating issue.

The report says only 18% of insurers surveyed have a unified strategy and customer-journey roadmap. It also describes its best-performing group as more likely to use plain language, tailor advice to life stages and engage customers around major milestones. Even without treating those correlations as a universal prescription, the direction is notable: the insurers doing more to make the product understandable and contextually relevant are also the ones Capgemini identifies as outperforming mainstream peers.

The larger lesson reaches well beyond insurance. Institutions often assume the customer’s job is to learn the institution’s language. Forms, disclosures, benefits, contracts, billing systems and service portals are built around internal categories that make perfect sense to the people who work inside them. The consumer gets the translation burden. When generative AI enters that relationship, one of its most useful functions may be to absorb some of that burden and hand the customer back a version they can actually act on.

The Human Still Matters — Just Later and Better

The human-advisor numbers are a reminder that clarity and judgment are not interchangeable. Consumers can want a machine to help them research and still want a person to help them decide. In fact, better self-service information may make the human interaction more valuable by moving it away from basic translation and toward questions that actually require context: what matters to this family, how stable is this need, what tradeoffs are acceptable, what changed since the last review?

Bryan Hodgens, LIMRA’s senior vice president and head of research, called education the industry’s “biggest opportunity.” That may be the cleanest reading of the report. The opportunity is not simply to automate more of the funnel or to place AI on top of the existing language. It is to reduce the amount of interpretation required before a consumer can make an informed choice, then use human expertise where human judgment is genuinely useful.

A life-insurance product does not become understandable because a chatbot can summarize it. If AI becomes the tool consumers rely on to translate institutional language into human language, that is useful technology — but it is also evidence of a communication gap.

A product built to provide certainty should not require a second system to explain the first one.

Methodology note: The Capgemini/LIMRA consumer survey is global, covering 6,175 consumers in 18 countries. The article uses NAIC guidance to ground the employer-coverage discussion in the U.S. consumer context.

SOURCE NOTES

• Capgemini Research Institute + LIMRA, “Life insurers under pressure: 42% of consumers are confused and unconvinced by policies,” Sept. 10, 2026.
• National Association of Insurance Commissioners, Life Insurance consumer guidance and Buyer’s Guide.