What Is AI In Insurance? Examples, Challenges, Future Trends

AI is the engine driving innovation across the insurance sector. Once viewed as a long-term transformation bet, AI is now a mission-critical capability, redefining underwriting, accelerating claims, reducing fraud, and delivering the hyper-personalized customer experience today’s policyholders expect.

For insurance CIOs, the imperative is no longer just to explore AI; it’s to lead it. This entails designing enterprise-ready architectures, integrating AI into legacy systems, and ensuring AI’s ethical and compliant use at scale.

The challenge? Insurance is a highly regulated, risk-sensitive industry with deeply entrenched systems, making AI adoption particularly complex. At the same time, those that lag risk falling behind more agile insurance transformation disruptors and digital-native competitors.

AI’s value in insurance is already evident: predictive models that forecast risk more accurately, NLP (Natural Language Processing) engines that handle millions of customer queries instantly, and automation tools that cut claims processing times from days to minutes.

According to McKinsey, AI could deliver up to $1.1 trillion in annual value to the global insurance industry by 2030, driven by smarter pricing, faster claims processing, and personalized services at scale.

This guide is built for the next wave of AI leadership. We’ll explore:

• What AI in insurance really looks like today.
• High-impact use cases across the insurance value chain.
• Strategic priorities and pain points facing insurance CIOs.
• A proven roadmap for scaling AI adoption.
• The future trajectory of AI in underwriting, claims, fraud, and customer experience.

From foundational trends to implementation playbooks, this is your blueprint for turning AI into a competitive advantage.

What Is AI In Insurance?

AI in insurance refers to applying machine learning, natural language processing, computer vision, and other intelligent technologies to automate, optimize, and improve operations throughout the insurance value chain. From underwriting and pricing to digital claims management and fraud detection, AI enables insurers to make more accurate, personalized decisions at scale.

AI’s role goes beyond basic automation. It analyzes vast volumes of structured and unstructured data, including historical claims, customer behavior, and external risk indicators, to uncover patterns, generate insights, and inform predictive models. This shift transforms insurance from a reactive industry based on loss compensation to a proactive ecosystem focused on risk prevention and personalized protection.

Consider a few real-world applications:

• Underwriting: AI models assess risks more granularly by factoring in real-time data from IoT devices, credit histories, and even social determinants, creating dynamic pricing that adjusts to individual behavior.
• Claims: AI accelerates claim processing through image recognition and automated triage, reducing time-to-settlement from weeks to minutes in some use cases.
• Customer engagement: AI-powered chatbots and virtual assistants handle everything from quoting to policy servicing, enabling 24/7 support and reducing call center burden.
• Fraud detection: Insurers are leveraging anomaly detection algorithms that identify fraudulent activity in real time, spotting patterns that human auditors might miss.

Ultimately, AI is not just a technological upgrade; it’s a strategic enabler that improves operational efficiency, strengthens customer relationships, and creates more resilient, data-driven business models.

For CIOs, understanding the breadth and depth of AI’s potential is foundational to designing future-ready insurance systems.

Benefits of AI in Insurance

AI is rapidly becoming a core driver of innovation and efficiency in the insurance sector. Its value lies in its ability to transform operations, elevate the customer experience, and enable more strategic, data-informed decisions across the enterprise.

1. Increased operational efficiency

One of AI’s most immediate and measurable benefits for insurers is its ability to streamline day-to-day operations, reducing manual workload, increasing process speed, and improving overall efficiency:

• Streamlined processes through automation: AI automates time-consuming, manual workflows such as claims adjudication, policy issuance, document verification, and customer inquiries. This results in faster turnaround times, fewer errors, and improved consistency across operations.
• Lower operational costs and faster response times: Intelligent automation can reduce back-office costs by up to 30% in some insurance lines. AI-powered tools, such as robotic process automation (RPA) and intelligent document processing, enable insurers to manage high volumes of transactions with minimal human intervention, significantly reducing administrative overhead and increasing responsiveness.

2. Personalized customer experiences

Beyond efficiency, AI is revolutionizing how insurers connect with customers, enabling the delivery of hyper-personalized experiences that drive loyalty, satisfaction, and increased lifetime value.

• AI-driven personalization for customer interactions: By analyzing behavioral, demographic, and transactional data, AI enables the creation of real-time, personalized customer journeys. This includes tailored policy recommendations, proactive renewal notices, and adaptive coverage options.
• Tailored policy recommendations and improved customer satisfaction: Insurers like Lemonade and Progressive have used AI to dynamically match users with personalized coverage bundles, improving customer satisfaction and reducing churn. These experiences are designed to feel intuitive, transparent, and responsive to individual needs.

3. Enhanced predictive analytics

AI empowers insurers with enhanced predictive capabilities to anticipate risks, tailor offerings, and make more informed, data-driven decisions across the enterprise.

• Better forecasting and strategic decision-making: AI-powered predictive analytics models help insurers anticipate customer needs, forecast claims volume, and optimize capital reserves. These tools enable scenario planning and support real-time adjustments to business strategy.
• Accurate risk assessments and pricing models: Advanced AI models increase underwriting accuracy by incorporating a wide range of risk signals, including IoT data and geospatial analytics. This enables insurers to price policies more competitively while minimizing exposure.

4. Reduction in claims costs

By automating key steps in the claims lifecycle and detecting fraud in real time, AI helps insurers lower costs, minimize losses, and improve claims accuracy.

• Efficient claims processing and reduced fraud instances: AI expedites claims intake and triage, ensuring prompt resolutions and reducing operational friction. Insurers using computer vision and NLP can instantly assess damage via submitted photos or videos, while fraud detection models flag suspicious activity based on behavioral anomalies and historical trends.
• Improved resource allocation and claims accuracy: AI systems optimize adjuster assignment and case routing, freeing up human experts to focus on high-value or complex claims. The result is higher accuracy, reduced rework, and increased policyholder trust.

The benefits of AI in insurance are no longer theoretical—they are tangible and measurable and already reshaping operations across the industry.

From streamlining processes and increasing customer satisfaction to refining risk analysis and reducing claims costs, AI enables insurers to operate more efficiently and deliver greater value to both policyholders and stakeholders.

Examples & Use Cases of AI in Insurance

AI is moving from promise to practice across the insurance value chain. From claims automation to fraud detection, leading insurers are applying AI to streamline operations, enhance customer service, and improve risk assessment.

Below are key use cases illustrating how AI is integrated into real-world insurance workflows.

1. AI-driven claims management

Automated claims processing is revolutionizing how insurers manage the claims lifecycle. By leveraging technologies such as computer vision, natural language processing (NLP), and machine learning, insurers can assess damages and approve payouts in minutes rather than days, reducing friction and enhancing customer satisfaction.

Allianz introduced the Insurance Copilot, an AI-powered solution developed by Allianz Technology, to transform claims management. Launched in 2024 for automotive claims in Austria, this solution leverages generative AI (GenAI) to streamline workflows and automate key tasks, enabling faster and more accurate claim settlements.

2. AI support chatbot for policyholder self-service

AI-powered virtual assistants now manage policy servicing, claims updates, and customer queries around the clock, reducing call volumes and enhancing CX at scale.

Allianz Benelux deployed an AI-powered chatbot using Landbot to simplify the insurance claims process. The chatbot processed over 92,000 unique search terms and achieved a 90% positive feedback rating from more than 18,000 customer interactions.

3. Legacy code modernization

Modernizing legacy systems is crucial for insurers aiming to enhance operational efficiency, reduce costs, and improve scalability. AI-driven tools facilitate this transformation by automating code analysis, refactoring, and migration processes.

Athora, a European insurer, partnered with Avanade to modernize its core insurance systems (developed initially in Unisys on Unisys Libra 690 mainframes). Utilizing Avanade’s Automated Migration Technology, Athora successfully migrated 15 applications—including customer information, individual life policies, and account receivables—totalling 5.3 million lines of code. This initiative led to a 70% reduction in infrastructure and operational costs.

4. Fraud detection and prevention

AI excels at identifying anomalous patterns and high-risk transactions in real time, aiding insurers in minimizing financial exposure while maintaining customer trust. These systems learn from historical data to detect suspicious claim behaviors, identify anomalies, and prevent payout errors.

Zurich Insurance employs AI-driven tools developed by its in-house team, Zurich Customer Active Management (ZCAM), to improve fraud detection across multiple lines of business. By utilizing machine learning and natural language processing, Zurich’s system assigns a fraud risk score to claims, prompting further investigation when necessary. This approach has resulted in a substantial increase in the detection of fraudulent activities.

5. Risk assessment and management

AI enables insurers to assess risk more precisely by ingesting and analyzing structured and unstructured data at scale. These models consider diverse signals from IoT sensors, satellite imagery, and behavioral trends to deliver dynamic, real-time risk profiles.

Progressive’s Snapshot program uses telematics data to personalize auto insurance pricing based on actual driving behavior. The AI engine evaluates factors like speed, braking, and time of day to assess individual risk levels, enabling more equitable and predictive underwriting.

6. AI-powered new product development

AI helps insurers uncover unmet customer needs, identify emerging market opportunities, and rapidly iterate on new offerings. By analyzing consumer behavior, sentiment, and usage data, AI enables the creation of personalized, modular insurance products that align with how people live and work today.

Metromile, a pioneer in usage-based insurance (UBI), leverages AI to power its pay-per-mile auto insurance model. The company uses machine learning to analyze driving patterns, mileage, and claims history, enabling it to tailor pricing dynamically. This model appeals to low-mileage drivers and has reshaped traditional premium structures.

7. Intelligent underwriting processes

AI is transforming underwriting from a slow, manual process into a fast, data-driven function. With machine learning and automation, insurers can instantly pull data from internal systems, third-party sources, and even customer-submitted documents to evaluate risk and issue policies in near real-time.

Aviva has adopted AI, in collaboration with Dataiku, to improve its underwriting processes, particularly in its life and health insurance lines. Aviva’s customer data science team uses Dataiku’s platform to develop and deploy AI models that streamline application processing, assess risk factors, and expedite approvals, all while ensuring compliance and accuracy. This integration has significantly improved their operational efficiency and customer experience.

Wrapping it Up: Scaling what works into enterprise impact

AI use cases in insurance are no longer isolated experiments; they’re signals of what’s possible when data, workflows, and customer experiences are redesigned around intelligence.

To scale these capabilities, CIOs must move beyond siloed experimentation and implement AI capabilities directly into enterprise-wide delivery models, supported by cross-functional governance, shared KPIs, and business-aligned outcomes.

The insurers who succeed will be those who industrialize what’s already working—and replicate it at scale.

Advanced AI Technologies Transforming Insurance

As insurers scale their AI capabilities, several enabling technologies are becoming foundational to their transformation strategies. These tools power AI initiatives and ensure scalability, interoperability, and compliance across complex IT environments.

• APIs and microservices architecture: Enables modular, scalable systems by facilitating seamless integration between core insurance platforms, third-party data providers, and AI engines. This architecture allows insurers to integrate AI capabilities without overhauling legacy systems.
• Business Process Automation (BPA): Automates repetitive workflows, such as claims triage, policy issuance, and document classification, freeing up skilled employees for higher-value tasks while reducing processing time and human error.
• GenAI applications: Power customer-facing experiences, including dynamic content creation, personalized policy explanations, and intelligent document generation. Generative models also support internal operations by drafting responses, reports, or claims summaries.
• Intelligent automation: Combines traditional RPA with AI-driven decision-making to automate both structured tasks and unstructured decisions. This hybrid model is ideal for workflows that require interpreting scanned documents, triaging exceptions, or routing approvals based on context.
• Machine learning: Provides pattern recognition and predictive insights across underwriting, pricing, claims, and fraud. Insurers utilize machine learning to optimize loss ratios, forecast risk, and personalize offerings based on behavioral and historical data.
• Natural Language Processing (NLP): Enhances chatbots, sentiment analysis, and intelligent search across internal knowledge bases. NLP also supports automated document review, regulatory compliance, and customer email triage.
• Optical Character Recognition (OCR): Transforms physical or scanned documents into machine-readable data, accelerating intake processes for applications, claims, and compliance documentation. Combined with AI, OCR enables smarter document classification and validation.

Wrapping it Up: Laying the foundation for long-term AI scalability

The technologies powering AI in insurance—APIs, automation, machine learning, and NLP—are only as valuable as the architecture that connects them. CIOs must prioritize systems that are modular, interoperable, and cloud-native, with governance structures that evolve in tandem with AI maturity.

Long-term scalability depends on building a stack that isn’t just future-ready but also future-resilient.

Top CIO Priorities for Scaling AI in Insurance

As AI moves from pilot to enterprise scale, insurance CIOs face new priorities that go beyond proof-of-concept and into operational transformation.

To drive real business value, they must shift from experimentation to enablement, ensuring AI is reliable, ethical, secure, and seamlessly embedded into day-to-day operations.

Here are the top priorities CIOs must focus on:

• Ensuring data quality and integrity: AI is only as good as the data it learns from. CIOs must lead data governance efforts; standardizing formats, eliminating silos, and implementing strong data validation and lineage protocols. High-quality, real-time data feeds are essential for underwriting precision, fraud detection, and customer personalization.
• Regulatory compliance and ethical AI usage: The insurance industry is highly regulated, and AI introduces new risks—from algorithmic bias to opaque decision-making. CIOs must establish governance frameworks that ensure the transparency, fairness, and auditability of AI models, while remaining aligned with emerging AI legislation, such as the EU AI Act and the NAIC Model Law.
• Talent recruitment and upskilling: Scaling AI requires more than tools. It demands skilled people. CIOs must partner with HR and L&D (learning and development) teams to attract data scientists, AI engineers, and analytics translators while upskilling business users to work effectively with AI systems. Cross-functional collaboration is critical to sustaining innovation.
• Integration with legacy IT infrastructure: Many insurers operate mission-critical systems on outdated mainframes. CIOs must prioritize interoperability, deploying APIs, data orchestration layers, and modular platforms to integrate AI capabilities without disrupting existing operations.
• Building agile, scalable technology platforms: True AI scale depends on flexible architectures. Cloud-native platforms, event-driven architectures, and scalable MLOps frameworks enable continuous deployment, monitoring, and improvement of AI models across all business functions.

Wrapping it Up: Turning AI strategy into operational reality

Executing AI at scale means transitioning from a proof-of-concept to embedded process transformation. For CIOs, that means establishing enterprise frameworks that drive AI alignment with underwriting, claims, and customer experience. It also requires building systems of accountability—from data quality and compliance to cross-functional training—that turn vision into velocity.

The goal is to operationalize AI so thoroughly that it becomes invisible to the user yet essential to the business.

Key Challenges Holding AI Adoption Back in Insurance

While AI offers enormous promise, many insurers struggle to move from experimentation to scaled deployment. Even with a compelling vision, structural challenges—ranging from fragmented data to regulatory risk—continue to stall progress.

Below are four key obstacles that CIOs must address to unlock enterprise-level AI transformation.

1. Data quality and accessibility issues

AI models are only as powerful as the data on which they were trained. However, many insurers face a complex web of legacy systems, siloed databases, and inconsistent data standards. Incomplete, inaccurate, or outdated datasets lead to unreliable model outputs and erode executive confidence.

Even the most advanced algorithms will fail to deliver real business value without a unified data foundation.

2. Potential for discrimination and bias

AI systems trained on historical insurance data can inadvertently perpetuate systemic biases, affecting claims approvals, pricing, and customer targeting decisions. CIOS must collaborate with data scientists, actuaries, and compliance officers to implement bias detection frameworks, ensure transparent model governance, and conduct continuous audits.

Without adequate ethical safeguards, adopting AI may expose firms to reputational and regulatory risks.

3. Regulatory and compliance complexities

The insurance sector is subject to stringent regulations, and integrating AI introduces additional layers of scrutiny. Emerging legislation (like the EU AI Act) requires that AI models be explainable, auditable, and compliant.

CIOs must ensure that AI applications, especially in underwriting and fraud detection, meet these standards to avoid compliance issues.

4. Skill gaps and talent shortages

AI transformation in insurance is outpacing the industry’s ability to recruit and retain the necessary technical and cross-functional talent. There is a notable shortage of professionals skilled in areas such as machine learning operations and responsible AI.

Many organizations struggle to reskill existing teams or bridge the knowledge gap between technical and business domains. Without a robust talent strategy, scaling AI initiatives remains a significant challenge.

Wrapping it Up: Diagnosing what’s holding AI back—and how to address it

Understanding the barriers to AI adoption is just the beginning. The real value lies in building strategies to overcome them, including cleaning and filtering data, closing talent gaps, creating ethical guardrails, and investing in transparent and compliant model operations. CIOs who approach AI as an enterprise readiness challenge (rather than just a tech upgrade) will be best positioned to scale confidently and sustainably.

Best Practices for Successfully Implementing AI in Insurance

Real value from AI emerges when strategy, talent, and technology are aligned, not just to deploy tools, but to drive measurable transformation across the enterprise. These best practices provide CIOs with a roadmap to transition from experimentation to execution, ensuring that AI initiatives scale smoothly, ethically, and sustainably.

1. Develop a strategic AI implementation roadmap

Effective AI adoption starts with a clear, enterprise-wide strategy. CIOs must define how AI aligns with organizational goals, whether improving claims efficiency, enhancing customer experience, or reducing operational costs. This roadmap should include prioritized use cases, success metrics, change management plans, and a governance structure.

2. Continuously monitor and optimize AI systems

An AI deployment is not a “set it and forget it” exercise. As models interact with real-world data, their accuracy, fairness, and performance can degrade over time. CIOs must implement robust monitoring frameworks to ensure AI systems align with business goals and regulatory expectations. This includes tracking drift, bias, and operational impact, as well as embedding governance mechanisms that support regular auditing, updating, and retraining.

KPIs to measure what matters

Recommended KPIs include:

• Model accuracy and precision.
• Time-to-resolution for AI-assisted processes.
• Claims automation rates.
• False positive/negative rates in fraud or churn models.
• User satisfaction scores in AI-enabled touchpoints.

Tracking these indicators ensures AI systems align with operational goals and customer expectations.

How Whatfix Analytics adds value

To effectively monitor and optimize the performance of AI systems, insurers need visibility into model outputs and how users interact with those systems in real workflows. Whatfix Analytics delivers this layer of insight by capturing real-time user behavior data across enterprise systems.

With features like journey mapping, drop-off funnel analysis, content engagement metrics, and in-app feedback, CIOs gain a deeper understanding of where users succeed (or struggle) with AI-enhanced processes. These insights help surface friction points, optimize user flows, and accelerate adoption, without requiring manual instrumentation or engineering support.

3. Invest in AI employee training and upskilling

Scaling AI initiatives demands a workforce that is willing and prepared to engage with intelligent systems. CIOs must reinforce how critical employee buy-in and proficiency are for AI success:

• Identify and address common skill gaps in insurance teams.
• Offer continuous, role-based training to empower users with AI-driven tools.
• Embed performance support into employee workflows with tools like Whatfix DAP to reduce ramp-up times and drive proficiency at the point of need.

4. Leverage strategic third-party partnerships

Even the most advanced insurers cannot build and scale AI capabilities on their own. External partners bring specialized expertise, proven implementation playbooks, and scalable infrastructure, enabling insurers to move faster and reduce execution risk. From deploying AI models to integrating digital adoption solutions, the right partner accelerates time to value and ensures transformation is sustainable, secure, and user-centric.

Criteria for choosing the right technology partner

CIOs evaluating AI and digital enablement vendors should look beyond functionality and assess each partner’s ability to deliver at scale. Key criteria include:

• Proven experience in the insurance domain or regulated industries.
• Seamless integration with legacy systems and cloud infrastructure.
• Strong governance and compliance alignment (e.g., GDPR, SOC 2).
• Support for user adoption and change management.
• Transparent performance metrics and post-deployment support.

Why Whatfix is a strategic partner

Whatfix is more than a product—it’s a partner in transformation. Insurers choose Whatfix for its ability to embed contextual guidance, automate training, and deliver real-time support at scale. Just as critically, they value a collaborative approach—working closely with IT, operations, and L&D teams to ensure seamless implementation, rapid onboarding, and measurable ROI.

With Whatfix, AI adoption isn’t just an afterthought. It’s a core outcome, enabled by expert support and frictionless technology integration.

5. Foster a culture of innovation and adaptability

No matter how advanced the algorithms or seamless the integrations, AI will stall without an organizational culture that embraces change. CIOs must collaborate closely with executive peers to foster an environment that encourages experimentation, continuous learning, and data-driven decision-making.

This means:

• Giving underwriters, claims leaders, and IT teams shared ownership of AI experiments (such as piloting an NLP-powered claims assistant or testing usage-based pricing models).
• Rewarding teams for operational efficiency and surfacing insights, identifying failure points, and collaboratively evolving business processes.

Cultural readiness is a competitive advantage

Insurers that embed adaptability into their DNA are faster to operationalize emerging technologies and more resilient in the face of disruption. A culture that supports innovation empowers employees to engage with AI tools, pose critical questions, and refine processes organically. This reduces resistance, improves model trust, and increases the long-term ROI of AI initiatives.

How CIOs can lead the shift

CIOs are uniquely positioned to:

• Champion cultural transformation, not just through technology but through behavior.
• Lead by example, frame AI as an enabler rather than a threat, and invest in cross-functional AI literacy programs.
• Establish innovation councils, co-ownership models between business and IT, and lightweight governance for experimentation.

Fundamental transformation doesn’t come from technology alone. It comes from aligning tools, teams, and culture around clear business priorities. These best practices provide CIOs with a blueprint for institutionalizing AI, from building a roadmap and measuring impact to enabling adoption and fostering agility. With the right foundation, CIOs can lead AI’s evolution from a promising pilot to a performance-driving capability, enterprise-wide.

The Future Outlook for AI in Insurance

As insurers mature their AI capabilities, the next wave of transformation will shift from task-level automation to holistic, intelligence-driven ecosystems. CIOs must prepare now for an environment where AI is not just a tool, but the fabric of how insurance operates, engages, and evolves.

Here’s what the near future holds:

1. Embedded insurance and real-time policy personalization

AI will enable insurers to move beyond static policies and traditional distribution channels. Through APIs and embedded platforms, AI-driven insurance products will be integrated directly into digital ecosystems, such as rideshare apps, e-commerce checkouts, and smart home platforms, delivering real-time, personalized coverage when and where consumers need it.

For CIOs, this means architecting systems that can dynamically generate quotes, adjust terms, and activate coverage instantly, binding policies in real time based on contextual data streams such as location, user behavior, or transaction history.

2. Advanced conversational AI and virtual agents

Today’s chatbots will evolve into full-fledged virtual agents that answer questions and manage complex policy servicing and claims processing with minimal human oversight. These systems will understand natural language, detect sentiment, and interface directly with core systems to resolve issues end-to-end.

CIOs should anticipate AI service layers that can manage multi-turn conversations, integrate with customer data, and escalate intelligently when necessary. These agents will operate 24/7, reduce call center volumes, and significantly enhance the customer experience.

3. Hyper-automation through intelligent ecosystems

AI will no longer be confined to individual processes; instead, insurers will build hyper-automated ecosystems that connect underwriting, claims fraud detection, and policy servicing into a unified, AI-orchestrated continuum. These systems will sense, analyze, and act in real time, autonomously routing tasks, escalating exceptions, and optimizing workflows.

For CIOs, the opportunity lies in integrating RPA, machine learning, and API-led architectures to eliminate silos and unlock cross-functional intelligence at scale.

4. AI-enabled autonomous claim assessment

Machine learning and computer vision will enable fully autonomous claims evaluations, particularly for standard or high-frequency cases like auto damage or property losses. These systems will analyze images, predict repair costs, and authorize payouts, often without human intervention.

5. AI-driven cybersecurity solutions

As the threat landscape becomes increasingly sophisticated, AI will play a central role in protecting insurer and policyholder data. Adaptive cybersecurity models will detect anomalies, predict threats, and trigger automated containment measures before breaches occur.

CIOs will need to shift from reactive protection to proactive detection, investing in AI-powered threat intelligence, behavioral analytics, and autonomous incident response.

Wrapping it Up: Shaping the future of insurance with intentional AI strategy

What’s ahead isn’t just evolution; it’s reinvention.

From embedded policies to autonomous claims and proactive cybersecurity, AI will fundamentally change how insurance is bought, experienced, and delivered. CIOs must stop reacting to the future and start designing for it by investing today in the platforms, partnerships, and processes that will define tomorrow’s leaders.

AI Adoption Clicks Better With Whatfix

AI is rapidly becoming the foundation for modern insurance, streamlining operations, unlocking new revenue models, and transforming customer engagement. But AI’s true value isn’t realized through technology alone—it’s activated through adoption.

That’s where Whatfix comes in.

Overcoming the last mile of AI implementation

For CIOs, implementing AI tools is only half the battle. The most challenging aspect is ensuring that underwriters, claims handlers, call center agents, and operations teams utilize these tools effectively, confidently, and in compliance with regulations.

Whatfix bridges this gap by embedding in-app guidance, automated onboarding, and contextual support that guide users through new AI-powered tools in real time, without relying on lengthy classroom sessions or dense training manuals. Whether it’s a predictive underwriting engine or a fraud analytics dashboard, Whatfix ensures users adopt with speed and accuracy.

Built for scale. Designed for change

Whatfix DAP integrates seamlessly with your enterprise software stack, enabling:

• Interactive in-app flows and self-service guidance.
• Contextual support tailored to user roles and actions.
• Higher engagement with AI workflows through behavioral nudges.
• Real-time insights into where users succeed or struggle.

As AI becomes core to insurers’ operations, Whatfix ensures that transformation reaches the people driving it—empowering them with the right knowledge at the right time in the flow of work.

Ready to activate AI at scale?

CIOs who want to go beyond pilots and scale AI across all business functions, roles, and regions need a digital adoption platform that accelerates both performance and change. Whatfix is purpose-built to make AI adoption faster, smarter, and frictionless.

Eliminate AI deployment bottlenecks in insurance by implementing Whatfix’s digital support. Request a demo today.