AI Fixes the Insurance Literacy Gap That Hurts Sales

A global consumer survey found the average insurance literacy score is 6.25 out of 10—and that number hides the real problem. The real problem is miscalibration: many customers who score lower feel more confident than customers who score higher. That perception vs. reality gap isn’t just an education issue. It’s a conversion issue, a retention issue, and a trust issue.

Here’s why I care about this (and why you should too): when a customer thinks they understand life or health insurance but doesn’t, they don’t ask the questions that would prevent cancellations, complaints, or “I didn’t know that” moments at claim time. When a customer does realize the complexity, they often freeze—then abandon the quote, delay the decision, or default to “I’ll think about it after the holidays.”

This post is part of our AI in Insurance series, and it focuses on one of the most practical uses of AI: personalized customer education that improves understanding without adding friction. The best insurers in 2026 won’t be the ones shouting “we have AI.” They’ll be the ones using AI to make customers feel informed, confident, and supported—across digital and human channels.

The perception vs. reality gap is the real buying friction

The key point: customers don’t drop out of the insurance journey only because of price. They drop out because they believe they understand the product when they don’t—or they realize they don’t understand it and feel overwhelmed.

In the podcast conversation behind the RSS article, Marisa Petriano (Remark Americas) describes findings from Remark’s Global Consumer Study across 22 markets. Two numbers stand out:

• Assessed insurance literacy averages 6.25/10 globally.
• Nearly 70% of respondents say they want additional education.

Now add the twist: confidence doesn’t track knowledge evenly.

• Gen Z and Millennials: 64% say they have “very good/good” knowledge.
• Boomers and Silent Generation: 44.6% say the same—despite having higher measured literacy.

That’s a classic “you don’t know what you don’t know” pattern. And in insurance, it creates two expensive outcomes:

1. The “overconfident buyer” problem: Customers skip reading, skip asking, and later feel misled.
2. The “paralyzed buyer” problem: Customers sense complexity and abandon the purchase.

AI helps with both—if you use it to calibrate understanding rather than to bombard people with more content.

Why this shows up harder in December

December is peak “life admin” season. Benefits enrollment wraps up, budgets tighten, families travel, and attention spans drop. People still shop—especially for life insurance, supplemental health, renters, and auto—but they have less patience for vague explanations.

So the perception-reality gap becomes sharper:

• Customers want quick clarity (“Do I need this? How much? What happens if…?”)
• Carriers often respond with generic PDFs and long FAQs
• Agents get stuck re-explaining the same basics instead of advising

A strong AI customer engagement layer can reduce that load by giving customers the right explanation at the right moment—then handing off to an agent when it actually matters.

What customers are asking for: digital education and a human backstop

The key point: consumers aren’t choosing between digital and human. They want both.

The study’s preferred education channels were close:

• 27% prefer an online course
• 24% prefer a one-to-one call

Even younger customers—often assumed to be “digital only”—still value real conversations. That matters for how you design AI experiences.

If your AI strategy is “replace humans,” you’ll create distrust. If your AI strategy is “make humans more effective,” you’ll create momentum.

Here’s what I’ve found works in practice:

• Use AI to handle repeatable explanations (definitions, exclusions, riders, billing, underwriting steps)
• Use humans for judgment and reassurance (needs analysis, trade-offs, beneficiaries, sensitive health topics)

The winning model is a hybrid education journey: AI for pace and personalization, people for confidence and accountability.

The simplest literacy strategy most insurers skip

Marisa’s advice in the podcast starts with something unglamorous: clarity in the normal customer journey.

That’s step one because most “education” fails when basic communications are confusing:

• unclear next steps (“What happens after I submit?”)
• inconsistent terms (“premium” vs “payment” vs “cost”)
• surprise requirements (“Why do you need my doctor’s info now?”)

AI can help here too—not with flashy features, but with disciplined execution: consistent language, plain-English summaries, and personalized explanations tied to the customer’s situation.

How AI closes the insurance literacy divide (without sounding robotic)

The key point: AI works best when it delivers contextual micro-education—small, relevant explanations triggered by behavior—not a generic content dump.

Think of insurance literacy like fitness. People don’t need a 300-page manual; they need the right coaching cue at the moment they’re about to make a mistake.

Below are the highest-impact AI patterns insurers are using in AI in insurance customer engagement.

1) “Explain this like I’m actually buying it” summaries

A customer doesn’t want a definition of a deductible. They want to know what it means for their budget.

AI can generate:

• a one-paragraph summary of coverage in plain language
• a “what you pay vs what we pay” example based on selected limits
• a short checklist: what’s covered, what’s not, what changes the price

This is especially effective at quote time, when abandonment spikes.

2) Adaptive education based on confidence signals

The study highlights miscalibrated confidence. AI can respond by detecting signals such as:

• rapid scrolling through key disclosures
• repeated toggling between plan options
• hesitations at beneficiary fields or medical questions
• “rage clicks” on info icons

Then it can offer targeted help:

• “Want a 30-second explanation of term vs permanent?”
• “Here are 2 examples of how riders change a payout.”
• “Would you like an advisor to confirm your choice?”

This approach respects time and reduces overwhelm.

3) Personalized gap calculators that don’t feel like homework

The podcast mentions “insurance gap calculators.” Done well, they’re a lead engine.

AI can turn a calculator into a conversation:

• “If your household needs $4,000/month for 5 years, here’s a range.”
• “If childcare is a factor, here’s how that changes the number.”
• “If you already have employer coverage, here’s what it likely does not cover.”

This is where personalization vs. perception becomes tangible. Customers stop guessing and start seeing their own scenario.

4) Agent-assist that makes education consistent

Customer education often varies by rep. That inconsistency creates compliance risk and customer confusion.

AI agent-assist can:

• suggest compliant explanations in real time
• summarize customer context before a call (“They’re comparing Plan B and C; they’re stuck on exclusions.”)
• generate post-call recaps and next steps in plain English

Result: customers get clearer answers, and your team spends less time repeating basics.

Snippet-worthy truth: “Education isn’t a content library. It’s a sequence of moments where confusion turns into delay.”

A practical playbook: 5 steps to build AI-driven insurance education

The key point: you don’t need a moonshot program. You need a focused workflow that improves comprehension at the highest-friction points.

Here’s a five-step plan I’d use if the goal is measurable impact within one or two quarters.

Step 1: Map “confusion hotspots” across the journey

Start with behavioral data and frontline feedback:

• quote abandonment screens
• top call/chat drivers
• most-opened emails (and most-ignored ones)
• common complaints after purchase

Pick 3 hotspots only. Focus wins.

Step 2: Define the education outcomes (not content output)

Examples of measurable outcomes:

• reduce quote drop-off by X%
• increase “understanding confirmed” clicks on key disclosures
• improve call resolution time
• reduce early cancellations (first 60–90 days)

If you can’t measure it, it’ll become a content project instead of a growth project.

Step 3: Build micro-lessons tied to decisions

Create short modules:

• 30–60 second explanations
• 3-bullet “what this means for you”
• scenario examples (“If you’re renting…” “If you have dependents…”)

Make them reusable across channels: web, email, SMS, agent scripts.

Step 4: Personalize with AI—but keep guardrails tight

AI should personalize within approved boundaries:

• approved language patterns
• required disclosures that must appear for certain products
• tone and reading-level targets
• escalation rules (“offer a call if the customer loops twice”)

This is how you scale education while protecting compliance.

Step 5: Blend AI and human help intentionally

Design explicit handoffs:

• “Book a 10-minute coverage check”
• “Request a call about beneficiaries”
• “Talk to someone about pre-existing conditions”

Customers shouldn’t have to hunt for a human. If they feel trapped in automation, trust drops fast.

People also ask: does better literacy actually increase insurance sales?

Yes—because clarity increases follow-through. Higher literacy reduces the mental load of committing to a policy, and it reduces regret later.

Better education impacts revenue in three ways:

1. Higher conversion: less confusion at the point of choice.
2. Better persistency: fewer “I didn’t realize…” cancellations.
3. Smarter cross-sell: customers who understand one product are more open to complementary coverage.

And AI makes this scalable. Without AI, you’re relying on agents to carry the entire education burden—expensive, inconsistent, and hard to maintain.

What to do next if you want leads (and not just clicks)

The perception vs. reality gap isn’t going away. If anything, it’s widening as products get more configurable and customers expect faster answers. The opportunity for insurers is straightforward: use AI in insurance to deliver personalized clarity—then back it up with real human support when decisions get personal.

If you’re building an AI roadmap for 2026, I’d start with this question: Where are customers most confident—and most wrong—in our journey? That’s where education has the highest ROI.

Want a practical next step? Audit three customer touchpoints (quote screen, welcome email, and first bill notice) and rewrite them for clarity. Then add AI-driven personalization so the examples match the customer’s situation. If those three touchpoints improve, you’ll feel it in conversion, service volume, and retention.

What would happen to your growth targets if every customer could explain—correctly—what they just bought?