Why insurers should have a clear AI strategy

The gap between AI leaders and companies which are still in the preliminary stages of AI adoption stems from differences in strategic alignment, organizational culture, technological infrastructure, talent and execution capabilities. 

Here are the key areas of the gap:

• AI leaders have a clear AI strategy aligned with long-term business goals and view AI as a competitive advantage and innovation driver.
• AI leaders define clear KPIs and metrics to measure the success and ROI of AI initiatives.
• AI leaders believe in having robust data ecosystems with integrated, high-quality, and well-governed data.
• AI leaders have dedicated AI teams comprising data scientists, engineers and domain experts.
• AI leaders use modern, scalable infrastructure such as cloud platforms, AI tools and high-performance computing.
• AI leaders foster an organizational culture of data-driven decision-making, encouraging cross-functional collaboration between IT, business and operational teams.

• AI leaders design AI solutions that have customer-centric focus directly enhance customer experience, such as personalized recommendations or faster service resolution.
  

By addressing these gaps, companies can create a more robust foundation for AI adoption, enabling them to catch up with AI leaders and achieve sustainable, enterprise-wide transformation.

Here's how insurers typically approach AI readiness assessment and use case evaluation:

AI readiness

Organization: Leadership commitment, business objectives, skills and talent.

Data: Availability, quality, infrastructure, compliance and privacy.

Technology: Infrastructure, tools and platforms and integration capability.

Evaluating use cases

Identify use case - engage stakeholders to select high-impact problems AI can solve (e.g., fraud detection, dynamic pricing, customer retention).

Prioritize use cases - Business Impact (Revenue growth etc.), Feasibility, Time to Value etc.

Calculate ROI.

Conduct proof-of-concept.

Secure stakeholder buy-in.

AI and automation are reshaping the insurance industry by streamlining processes, improving efficiency, and enabling better decision-making. While these create new opportunities, they also impact certain roles.

Customer service representative - AI-powered chatbots and virtual assistants will manage routine customer inquiries, policy updates, claims statuses and automation will manage policy servicing tasks like renewals and endorsements.

Underwriters - AI models will analyze large datasets to assess risks more accurately and quickly than traditional methods, predictive analytics and machine learning (ML) will enable dynamic pricing.

Actuaries - ML models will perform complex data analysis, identifying patterns and trends in historical and real-time data, generate faster insights for pricing, risk management, and product development, etc.

Claims adjusters - AI will streamline claims processing, from intake to settlement. Technologies like NLP and computer vision will manage tasks like document review and damage assessment.

Fraud investigators - AI will detect anomalies and potential fraud patterns in claims and underwriting data, reducing manual investigations.