How insurance leaders can scale AI: A strategic roadmap for claims transformation

This is the third and final installment in our AI + Claims series.

• Part one tackled the mounting pressure on claims organizations.
  
• Part two examined high-impact AI use cases.
  
• This final article outlines how insurance leaders can build a strategic AI roadmap—turning early pilots into durable, scalable advantage.

AI is no longer a moonshot for insurance—it’s a lever for material, near-term gains. What started as isolated pilots is fast becoming the new foundation for claims operations. Leading carriers are already deploying generative models to compress timelines, reduce leakage, and unlock operational bandwidth.

Consider the pace of adoption and results:

• Geico and Admiral use Tractable’s computer vision AI to assess car damage in minutes, cutting settlement times from weeks to hours
• AXA uses AI models to improve risk selection and pricing by analyzing historical and current data, offering fairer prices based on individual policyholder risk.
• Eastern Alliance reduced document processing time from 5 days to 1 hour, saving over 2,700 human hours using AI agents
• MetLife deployed AI in its call centers, achieving a 3.5% boost in first-call resolution, a 13% lift in customer satisfaction, and a 50% drop in average call duration.
• Allstate, Allianz, and Helvetia have integrated AI-powered chatbots into their customer claims operations, streamlining interactions, enhancing self-service, and accelerating response times.
• State Farm reduced fraud by 30% within the first year of using machine learning for anomaly detection in auto insurance claims 
• 65% of insurers, according to Reuters, now view generative AI as the single most effective response to rising claims costs
• NIB Health Insurance saved $22 million through AI-driven digital assistants, reducing customer service costs by 60% and decreasing phone calls with agents by 15%
• Deloitte estimates AI-driven fraud detection could save $80B–$160B by 2032 across P&C lines
• Boston Consulting Group (BCG) reports a 36% efficiency lift in complex lines of business by augmenting manual claims processes with AI

The signal is clear: AI isn’t just about tech modernization—it’s about reshaping claims economics and raising the ceiling on service.

But this isn’t a story of plug-and-play wins. AI at scale only delivers when built into the fabric of the business—across talent models, workflows, governance, and experience design.

Here’s how to lead that transformation.

Strategic imperative #1: Know where your customers want AI and where they don’t

AI should not be imposed uniformly across customer segments. It must be deployed with an acute understanding of preference heterogeneity based on claim type, emotional stakes, demographic factors, and transaction frequency.

• Commercial clients, accustomed to operational claims interactions, often prefer digital-first pathways and are comfortable with automation handling high-frequency, low-complexity tasks.
• Personal lines claimants, especially those navigating bodily injury or litigation, may require high-touch, empathetic engagement. For these individuals, human contact remains a critical trust vector.

Effective strategies segment customers and align AI interfaces accordingly, ranging from chatbot-driven triage for glass claims to human-led conversations for catastrophic loss scenarios.

Strategic imperative #2: Plan for the talent you’ll need (and the talent you’ll lose)

AI adoption is not a headcount reduction story—it’s a talent reallocation and upskilling challenge. The aging of the claims workforce has triggered a hollowing-out of experiential capital, particularly among frontline coaches, mentors, and adjudicators.

A robust AI strategy must include:

• A granular workforce capability audit
• Succession modeling for knowledge-heavy roles
• Identification of roles best suited for augmentation versus automation
• Training pipelines for employees to oversee, interrogate, and interpret AI outputs

For example, generative AI copilot tools that translate policy language into plain-English coverage validations are not substitutes for adjusters—they’re scaffolds that accelerate the learning curve for newer hires and reduce variance in claim quality.

Strategic imperative #3: Don’t automate the mess. Map it first.

Insurers must resist the urge to “plug in” AI to generic workflows. Effective integration demands a forensic-level mapping of process friction—identifying not only which tasks are repetitive or time-intensive, but where human decision-making introduces inconsistency, bias, or excess cycle time.

Categories to target:

• Low cognitive-load tasks (e.g., FNOL transcription, form pre-fill)
• Multi-system coordination gaps (e.g., retrieving policy vs. claimant data)
• Discretion-heavy moments with high error rates (e.g., liability assignment)

Once identified, leaders must evaluate AI’s fit-for-purpose: Does it improve precision? Reduce turnaround? Improve compliance auditability? These are not rhetorical questions—they should be codified into ROI frameworks before tech is deployed.

Strategic imperative #4: Weigh risk, then manage it aggressively

Generative AI is deceptively simple to pilot—but deceptively hard to scale. Without a strong risk governance framework, its deployment introduces systemic threats that go far beyond model drift or minor inaccuracies. We’re talking about fundamental exposures to data privacy, explainability, fraud, compliance, and even workforce culture. Model opacity, hallucinations, data leakage, and adversarial misuse (e.g., AI-generated false images) pose real threats, not only to operational integrity but to regulatory compliance.

The risks fall into two broad buckets:

• Technological risks — these include data leakage, untraceable decision logic, algorithm theft, and model hallucinations. Generative models often lack transparency in how they derive conclusions—making them vulnerable in highly regulated insurance contexts where auditability is mandatory. If an algorithm is breached or manipulated, the integrity of the system collapses.
• Usage risks — stemming from human behavior. These include reliance on biased or inaccurate training data, misuse of AI tools outside their intended purpose, and user confusion around what AI outputs actually represent. In a worst-case scenario, bad data in means bad settlements out—at scale.

There are also cultural risks: generative AI may bypass entrenched workflows that rely on apprenticeship, step-by-step case handling, or supervisor reviews. This threatens to erode institutional trust unless counterbalanced with human oversight.

To mitigate these risks, insurers must go beyond surface-level controls. A robust framework should include:

• Creating a centralized AI governance council across business, legal, and compliance
• Implementing model audit trails and strict version control systems
• Defining explainability thresholds per use case, especially in customer-facing applications
• Embedding kill-switch mechanisms and override protocols in all production models
• Instituting continuous edge-case testing and failover scenarios

Insurers should start small: limit initial deployment to use cases where risk is manageable and oversight is guaranteed. Think file summarization, coverage verification tools, and knowledge assistants—not fully autonomous decision engines.

Moreover, insurers should take a phased approach to AI deployment—one guided by both value at stake and risk complexity. Use cases should be prioritized not solely on novelty or automation potential, but on where the return is clear and governance is manageable.

High-priority zones include knowledge assistants, FNOL tools, and claims file summaries, where value is high and complexity is low. As risk and complexity grow, so too should human oversight and implementation caution.

When paired with structured governance, these constraints don’t slow you down—they keep your future viable. Because at scale, what breaks isn’t just a tool. It’s trust, compliance, and your ability to serve customers safely.

Strategic imperative #5: Rationalize build vs. buy across the use case portfolio

The vendor ecosystem is evolving rapidly, and insurers must resist building bespoke tools for commodity problems. Build only where differentiation is strategic and enduring. License elsewhere.

Here’s how to prioritize:

• Build when strategic differentiation is essential, such as copilots tailored to proprietary policy logic, workflows tied to sensitive claims data, or when the UX must fully reflect your brand experience.
• Buy when the task is generic but essential, such as generative summarization, document parsing, call transcription, or PDF extraction.
• Partner when AI needs to be embedded in third-party platforms like Salesforce, CRM systems, or telephony infrastructure.

This strategic segmentation avoids fragmentation, controls technical debt, and accelerates time-to-value.

Insurers that ruthlessly prioritize will leapfrog the ones dabbling everywhere.

Strategic imperative #6: Operationalize at scale—two paths forward

Once early wins are validated, insurers must avoid stagnation in pilot purgatory. Scaling can proceed along two mutually reinforcing axes:

1. Horizontal scaling – Clone successful use cases across lines of business (e.g., a triage assistant for auto-replication for homeowners or specialty lines).
2. Vertical scaling – Stack multiple AI interventions into a single workflow. For example:

• AI logs the FNOL
• Summarizes the call
• Flags missing info
• Drafts the follow-up
• Preps the payment

Mature organizations also begin to track adoption metrics (time saved, leakage reduced, customer NPS shifts) as core KPIs for scaling decisions—not just anecdotes or usage logs.

Strategic imperative #7: Design for adoption, not just deployment

Many AI strategies fail not for technical reasons, but because of cultural inertia. The claims function, steeped in precedent and apprenticeship, requires deliberate change management.

That means:

• Bringing frontline claims staff into the solution design process
• Defining how AI outputs will be reviewed, overridden, or escalated
• Creating champions across role levels who evangelize use cases internally
• Telling stories of improved customer experience and employee impact, not just savings

Technology must be introduced not as surveillance, but as augmentation—tools that de-risk decisions and elevate the adjuster’s role from data wrangling to judgment-making.

Conclusion: From experimentation to structural advantage

Claims leaders are standing at a critical juncture. The difference between early adopters and fast followers will be determined by more than tooling—it will be defined by whether they integrate AI in ways that elevate people, streamline workflows, and institutionalize accountability.

Futureproofing isn’t about betting on tech. It’s about creating an organization where AI becomes a second skin—not a bolt-on. And it starts now.

Caveat: This piece concludes a three-part series on AI in claims. The first article explored structural challenges facing modern claims organizations. The second mapped tactical AI use cases across the lifecycle. This final installment offers a strategic framework for senior leaders to integrate AI at scale and build durable, future-ready organizations.