AI‑powered claims processing: proven use cases for modern CX leaders

AI‑powered claims processing: proven use cases for modern CX leaders

Over 20% of customers expect their claims to be settled within hours. But 43% wait over two weeks for resolution, according to a recent report. 

That’s a staggering gap between modern expectations and traditional processes. The disconnect is even sharper among younger demographics: most 18–24-year-olds now expect a claim to be handled within a week.

These numbers are a warning signal. In an experience-driven economy, slow claims processing is more than an inefficiency, it’s a customer experience (CX) failure.

Yet for many enterprises, the claims management process remains a frustrating, expensive black hole, for customers and employees alike. Delays, manual processes, miscommunication, and repetitive questions are still the norm. And while backend systems have improved incrementally, the front-end experience has barely evolved.

CIOs are now under pressure to modernize aging infrastructure while also delivering customer-facing experiences that reflect today’s digital expectations.

Here’s the challenge: Most claims automation still focuses on back-office efficiency, not front-end experience. But without thoughtful orchestration and conversational precision, even AI can feel like just another bottleneck.

Why claims processing needs a CX transformation

The business case for modernizing claims has typically focused on cost savings. But the conversation has now shifted. Customers expect fast, intuitive, and personalized experiences, especially when something goes wrong. The insurance claims process is now a defining moment in the overall customer journey.

That’s why CIOs and CX leaders alike are rethinking how claims get done.

High touch vs high trust

In traditional service models, high-touch often meant high-quality. Customers were routed to agents, agents took notes, and manual work drove the process forward. But in high-volume or low-complexity claims, this model creates unnecessary friction, for both the business and the customer.

Customers value control, clarity, and speed. They want to upload photos, explain incidents in their own words, check status updates, and escalate only when necessary.

As expectations shift, so must the service model. High trust experiences enabled by artificial intelligence (AI) agents and workflows let customers move quickly and confidently through routine claims, while reserving human support for complex or sensitive situations. It’s a smarter allocation of resources that drives better outcomes for everyone.

Real-world CX impact

The experience gap is well-documented. A PwC study found that 73% of customers say experience is a key factor in their purchasing decisions. Yet only 49% believe companies deliver a good experience. In claims scenarios—often high-stress and time-sensitive—the bar is even higher.

When customers face long hold times, limited status updates, and repetitive questions, frustration builds quickly. And that frustration often gets directed at the brand, not just the claims process.

AI-powered claims automation has the potential to change that narrative. By combining agentic AI, policy logic, and backend orchestration, insurance companies can resolve claims faster, reduce support load, and improve satisfaction, all without compromising compliance or empathy.

Real-world AI claims processing examples by sector

AI-driven claims automation is transforming how insurers and adjacent sectors deliver seamless, efficient, and customer-friendly service. Whether it’s handling first notice of loss (FNOL), triaging healthcare claims, or preventing fraud, leading organizations are using agentic AI to streamline operations without compromising quality.

Here are real-world examples that show how AI automation is driving meaningful results across industries.

Insurer self-service

More insurers are turning to AI agents to streamline the First Notice of Loss (FNOL) process. Instead of waiting on hold or navigating a phone tree, customers can report incidents in real time—by voice or digital channel—using natural language. The agent captures key details, kicks off the claim workflow, and routes information across backend systems automatically.

It’s especially useful during spikes in volume, like after major weather events. Customers get instant, consistent support. Claims teams get to focus on the complex or sensitive cases that actually need human judgment.

AI agents are also embedded deeper in the claims process to help flag fraud. They scan documents for tampering, detect anomalies, and escalate for review—so approvals move faster for legitimate claims, and red flags are caught early. That keeps payouts accurate and customer trust intact.

Healthcare claims triage

In healthcare, AI agents are automating everything from claims intake to review. They verify documents, check submissions against policy rules, and surface errors or suspicious patterns before they clog up the process. That means claims move faster, with fewer surprises—for both providers and patients.

Agents can also triage by complexity or urgency. Simple claims get processed right away. Edge cases or ambiguous submissions are routed to staff for deeper review. This kind of routing keeps the workflow moving, while making sure the right people focus on the right work.

Across the revenue cycle, agents assist with claims data extraction, denial management, and reimbursement tracking. The result: faster decisions, fewer delays, and more transparency—all without teams getting buried in manual work.

Key capabilities CIOs & CX leaders need today

AI platforms are not all created equal—especially when it comes to claims. To truly transform outcomes, automation must go beyond simple NLP or scripted flows. CIOs and CX leaders need tools that can flex with business logic, scale with volume, and meet regulatory demands without compromise.

The most effective AI solutions in claims processing are defined by 5 core capabilities:

1. Real-time orchestration across systems

Claims processing is rarely linear. One customer interaction can trigger updates across policy systems, CRMs, payment processors, fraud detection tools, and more. AI platforms must be able to connect these systems in real time and execute across them without interrupting the customer journey.

Our AI agent management platform’s (AMP) architecture is purpose-built for this kind of orchestration. With prebuilt connectors and open APIs, AI agents can access and trigger actions across enterprise systems — from legacy claims software to modern cloud-native apps — ensuring that every step of the claims workflow is automated and cohesive.

2. Dynamic conversation design instead of static scripts

One of the biggest pitfalls in claims automation is rigidity. Customers rarely follow predictable scripts, yet many solutions force them into predefined flows, creating frustration and failure points.

Modern claims automation requires flexible, intelligent agents that can interpret intent, handle nuance, and adapt to each scenario. Our platform enables this with a design layer that supports natural language briefs, contextual memory, and dynamic branching across voice, chat, and messaging channels.

3. Pre-deployment simulation & post-deployment monitoring

Trust in automation comes from testing—and improving—at every step. Before launch, teams must simulate real-world claim conversations, evaluate edge cases, and ensure every scenario is handled accurately. After deployment, performance must be continuously monitored to avoid drift, errors, or compliance risks.

Our platform makes this possible with agent simulation and stress testing tools that replicate thousands of real-life conversations. This allows teams to catch ambiguity, flag integration issues, and refine fallback behavior before customers ever see a flaw.

Post-launch, our optimization layer tracks key metrics like containment rate and handling time, while QA tools surface errors, hallucinations, and brand inconsistencies for immediate adjustment.

4. Enterprise-grade compliance and control

Claims involve personally identifiable information (PII), regulated financial data, and detailed audit requirements. When properly configured for your organization’s regulatory needs, any AI solution operating in this space must be secure by design and compliant out of the box.

Our platform includes built-in guardrails, PII redaction, audit logging, and continuous compliance monitoring. Whether you’re operating under HIPAA, GDPR, or NAIC guidelines, you can deploy automation with confidence, knowing your data is protected and your workflows are audit-ready.

And because compliance needs evolve, we give IT and governance teams deep visibility and control over how data is used, stored, and acted on, down to the agent level.

5. Scalability that adapts to volume and complexity

From bursty claim volumes during a storm event to routine warranty submissions across global markets, scalability isn’t just about handling traffic—it’s about maintaining performance and consistency under pressure.

Our platform is built to scale. AI agents operate seamlessly across languages, brands, and channels, with centralized management and performance optimization baked in. Whether you’re orchestrating thousands of claims per day or onboarding new lines of business, we support growth without tradeoffs.

Transform claims from a cost center into a CX differentiator

In the moments that matter most — when something goes wrong — your claims process is your brand. Customers want clarity, empathy, and the confidence that your systems are working for them, not against them.

Modernizing claims is about designing intelligent, resilient, and responsive experiences that earn trust, at scale.

That’s where agentic AI makes the difference. With AMP, you can build AI agents that aren’t boxed into static flows or limited by legacy systems. Instead, they understand context, act across platforms, and continually learn to deliver better outcomes for your customers and your business.