Where AI Creates Real Value in Insurance and Protection
AI in insurance has moved past slide decks and pilots. The question that matters now: where does it deliver real, measurable value without adding complexity or risk?
Here's how leaders across protection and advice are putting AI to work today-and what they expect next.
Make communications clearer and more personal
Ian Douglas, Head of Architecture at Guardian: "AI delivers real value in insurance and protection when it genuinely helps us serve advisers and customers better, making things clearer, faster and more personal - not more complicated."
Practical wins are already here: summarising long documents, unifying information from multiple sources, and generating clearer, context-specific communications. For products that often feel abstract, explaining policy details in plain language increases engagement and understanding.
Conversational tools and real-time translation remove friction for advisers and customers. They turn jargon into useful answers and help teams serve people whose first language isn't English.
Strengthen accuracy, consistency, and compliance
Ian Douglas: AI can flag data inconsistencies between applications and supporting evidence, which reduces rework and claim friction later. It's a straightforward way to spot issues early and protect customers.
Post-issue checks and continuous monitoring add another safeguard. Think early-warning systems that scan for unusual patterns, emerging risks, and potential misrepresentation-always with human oversight.
Cut admin; free people for judgment and empathy
John Underwood, Director of Technology at Cirencester Friendly: "In an admin-heavy sector like ours, using AI to carry out repetitive work has the power to free up the peoples' time."
Less manual admin means more time for creative problem-solving and better customer outcomes. The sector is data-rich; the edge comes from surfacing actionable insights for advisers so they make faster, higher-quality recommendations.
Importantly, the goal is augmentation, not replacement-helping advisers understand underwriting logic, product particulars, and next-best actions.
Faster claims, more human support
Jonathan Sandell, CEO at Shepherds Friendly: Claims and underwriting are set for near-term gains. In claims, AI can review medical evidence, check coverage, summarise files, and screen for fraud to speed up low-complexity workflows.
That shift moves professionals from chasing paperwork to supporting sensitive, complex cases with empathy and expertise.
Underwriting triage and refined risk
Jonathan Sandell: AI can separate low-risk cases for straight-through decisions while routing edge cases to experienced underwriters. Validating medical data can reduce reliance on GP reports and shorten cycles.
Longer term, more granular risk models can move the industry away from blanket loadings and broad exclusions-toward assessments that better map to actual risk.
Give customers what they actually want
Kesh Thukaram, Co-Founder, Best Insurance and excitare.ai: Most customers don't want bells and whistles. They want affordable cover for a specific risk, bought quickly with minimal effort.
Generative AI enables product simplification and hyper-personalisation: fewer low-usage add-ons, more targeted benefits shaped by data and individual preferences. That means cleaner journeys, more relevant offers, and higher take-up.
Adviser enablement across the business
Cameron Erskine, Wealth Management Consultant and Financial Adviser at SeventySeven Wealth Management: AI acts as a research assistant, turning broad knowledge into fast, usable insight. It can draft suitability reports, summarise fact-finds, and translate technical detail into plain language for clients.
Inside the firm, AI supports performance analytics, knowledge bases, and operational optimisation. Clear boundaries matter: secure data, defined usage policies, human review, and firm-level accountability.
Governance that builds trust
- Human-in-the-loop: AI supports decisions; it doesn't own them.
- Auditability: Keep logs, sources, and reasoning traceable.
- Data protection: Minimise personal data, apply retention rules, encrypt in transit and at rest.
- Consumer outcomes: Align to fair value, clarity, and support obligations under the FCA Consumer Duty.
- Model risk: Test for bias, monitor drift, and set clear escalation paths. See guidance from the ICO on AI and data protection.
High-ROI use cases to ship in 90 days
- Document summarisation: Application packs, medical evidence, complaints, and policy terms distilled for faster review.
- Adviser and customer chat: Policy Q&A, underwriting criteria, and process guidance in plain language with citations.
- Inconsistency flags: Cross-check declared data vs. evidence before bind and at claim.
- Claims triage: Route low-risk cases for quick settlement; escalate complex cases with a clean summary.
- Real-time translation: Improve access and reduce misunderstanding in service interactions.
Metrics that prove value
- Claims: Cycle time, straight-through rate, rework, fraud hit rate, and complaint ratio.
- Underwriting: Time to decision, GP report requests avoided, referral quality, and loss ratio impact.
- Service: First-contact resolution, average handle time, CSAT/NPS, and abandonment rate.
- Product: Quote-to-bind conversion, add-on utilisation, and lapse/renewal performance.
Implementation checklist
- Scope: Pick 2-3 workflows with clear owners, clean data, and quick integration paths.
- Data: Map sources, set retention, and define consent boundaries before build.
- Controls: Human review, escalation triggers, PII redaction, and policy-aligned prompts.
- Evaluation: Baseline metrics, test sets, A/B pilots, and regular model performance reviews.
- Change: Train users, document playbooks, and communicate limits and expected behaviours.
Bottom line
AI earns its place when it removes friction, makes communication clearer, and protects customers-without sidelining expert judgment. The leaders above are aligned on that.
Start small, prove value, and scale what works. For deeper sector use cases, see AI for Insurance, and for LLM-specific workflows, explore Generative AI and LLM.
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