Unlocking the Value of Existing Technology Investments with AI in Insurance

Why insurers don't need to replace their systems to see real AI value

Insurance carriers have spent decades investing in core platforms for policy administration, billing, claims, underwriting, CRM and data. Yet many still face slow cycle times, high combined ratios and operating costs, compliance risks and heavy manual work.

The challenge isn't broken systems. It's that insurance operations still depend heavily on unstructured information—documents, emails, notes, calls and images—spread across platforms.

"At internet scale, we've learned that the fastest path to modernization isn't replacing systems, it's augmenting them. Across the traffic that Akamai sees globally, AI is already being used to interpret billions of signals in real time to detect anomalies, automate mitigation, and surface operational insights. Insurers can apply the same model internally, layering intelligence on top of existing infrastructure to accelerate decisions without disrupting core systems," says Tony Lauro, Sr. Director of Security Strategy, Akamai

AI changes this dynamic.

Leading insurers are using AI to wrap and enhance existing technology, turning core systems into faster, more intelligent engines for underwriting, claims, service, and operations without the risk, cost or disruption of core replacement.

Why this matters now

The timing matters.

Margin pressure, rising loss costs, workforce constraints, pricing discipline, capital resilience, and higher customer expectations are colliding as carriers are being asked to modernize faster—with less tolerance for large, multi‑year transformation programs.

At the same time, generative AI and machine learning have reached a level of maturity that allows them to be safely applied to regulated workflows with humans in the loop.

For executives, the question is no longer whether AI creates value but how quickly it can be applied to measurable operational outcomes without destabilizing the business.

That is why augmentation (not replacement) is becoming the dominant AI strategy.

AI as a system of work on top of the core

The fastest path to modernization is often AI on top of the core.

In this model:

• Core platforms remain systems of record
• AI becomes the system of work—extracting meaning from unstructured content, summarizing context, and coordinating workflows
• Integration layers reduce handoffs and swivel chair‑ work
• Governance ensures auditability, privacy and responsible decision-making

Core systems store transactions. AI activates intelligence across them. This approach allows insurers to move from pilots to production quickly and realize value in weeks, not years.

What changes when AI is applied pragmatically

When applied with clear outcomes in mind, AI helps insurers:

• Lower unit costs through automated intake, triage, and summarization
• Reduce cycle times across quote-to-bind, application‑to-issue, FNOL‑to-assignment and claim-to‑close
• Improve decision quality through consistent guideline checks and anomaly detection
• Enhance customer and agent experience by reducing delays and handoffs
• Achieve financial gains from leakage reduction and efficient risk management

The breakthrough is not experimentation; it is converting unstructured information into operational signals at scale.

Where to start: High ROI ‑ "Wrap & Enhance" use cases

Successful programs typically begin with two or three focused use cases that deliver measurable value within 90 days.

1. Document intelligence for intake
Automated classification, extraction, validation, and routing for new business and claims.
Impact: Lower NIGO, faster processing, higher throughput.

2. Underwriting, Claims, and Service copilots
AI copilots summarize case context, surface guidelines, and draft communications for review.
Impact: Reduced touch time, improved consistency, faster staff ramp-up.

3. Triage and prioritization
AI assesses severity, flags SIU indicators, litigation propensity, and recommends routing.
Impact: Faster response, better resource allocation, increased risk scoring, reduced leakage.

4. Contact center augmentation
AI summarizes omnichannel interactions and guides agents in real time.
Impact: Lower handle time and higher first-call resolution.

5. AIOps and observability for network stability
AI correlates signals across monitoring and IT operations tools to proactively detect issues, reduce alert noise and speed root-cause analysis.

Impact: Improved uptime, lowered MTTR and reduced operational risk.

Measuring what matters

AI initiatives succeed when KPIs are defined before launch and tracked consistently, including:

• Time to issue and NIGO rates
• Underwriting touch time and referral rates
• Claim cycle time and leakage indicators
• Contact center handle time and resolution rates

AI must be tied directly to business outcomes, not treated as an innovative exercise.

Governance enables scale

In regulated environments, governance is essential, not optional:

• Tier use cases by risk: assist > recommend > automate
• Keep humans in the loop for coverage and adverse decisions
• Log inputs, outputs, and rationale
• Apply strong access, masking, and monitoring controls

Done well, governance accelerates adoption rather than slowing it down.

"Governance around AI isn't just about regulatory compliance; it's also about adversarial resilience. From threat telemetry across the internet, we observe attackers increasingly automating reconnaissance and exploitation with AI-assisted techniques. Organizations deploying AI in regulated environments need governance models that include security telemetry, continuous monitoring, and threat intelligence to ensure those systems remain trustworthy," says Lauro from Akamai

Final thoughts

The insurers winning with AI are not replacing what already works. They are making it work smarter.

By wrapping AI around existing core systems, carriers can modernize operations, protect margins, and improve customer and agent experiences—without the disruption of large‑scale core replacement programs. AI is not a modernization program. It is a performance multiplier for the modernization investments many carriers have already made.

AI is no longer a future bet. Applied pragmatically, it is a system of work that unlocks value from the technology insurers already own.