Navigating AI Pricing Challenges in (Insurance) Technology

Executive Summary

The advent of AI technology has transformed pricing strategies across industries, presenting both opportunities and challenges for insurance professionals. A recent analysis by Anh-Tho Chuong, co-founder of the billing company Lago, highlights how AI’s usage-based cost structures have disrupted traditional subscription pricing models, forcing companies to rethink how they price products when costs scale directly with usage. This shift has important implications for the insurance sector, especially as insurers increasingly integrate AI-powered tools for underwriting, claims processing, and customer service.

Insurance companies and technology providers must understand the nuances of emerging pricing frameworks such as usage-based, seat-based, subscription with overages, credit-based, and outcome-based models. Applying these concepts thoughtfully can help insurers maintain profitability while delivering innovative AI-driven solutions. This article distills key insights from Chuong’s findings and explores practical applications tailored to insurance carriers, agents, and underwriters, with a focus on balancing cost management and customer value in an AI-enhanced environment. For the original detailed discussion, see the full article here.

Key Insights

1. AI Usage Drives Variable Costs, Challenging Traditional Pricing
   Unlike conventional software-as-a-service (SaaS), where fixed costs dominate and additional users do not significantly increase expenses, AI platforms incur costs proportional to usage. Insurance companies leveraging AI for data analysis, risk scoring, or claims automation face similar variable costs, which complicate fixed-price offerings.

2. One-Size-Fits-All Pricing No Longer Sustainable
   The era of flat-rate or per-user pricing models is giving way to more nuanced approaches. Insurance technology providers must tailor pricing to reflect the intensity and type of AI usage, ensuring that the cost of delivering services is covered without discouraging adoption.

3. Usage-Based and Outcome-Based Models Offer Alignment Between Cost and Value
   Charging clients based on actual AI consumption or successful outcomes can align incentives and enhance transparency. For example, pricing claims automation tools based on the number of processed claims or resolved inquiries encourages efficient usage and value demonstration.

4. Balancing Transparency and Customer Trust Is Critical
   Models involving overage charges or credits require clear communication to prevent unexpected costs that could erode trust. Insurance firms must ensure clients understand how AI-enabled features are billed to maintain satisfaction and compliance.

5. Cross-Functional Collaboration Is Essential for Pricing Strategy
   Developers, actuaries, finance teams, and underwriters need to collaborate closely to design pricing that reflects both technical costs and market expectations. This integrated approach helps optimize margins while supporting innovation.

Insurance Industry Applications

• AI-Powered Underwriting Platforms
  Insurers can adopt usage-based pricing for AI underwriting tools, charging based on the number of risk assessments completed or the volume of data processed. This model ensures that premiums for technology services scale logically with business growth.

• Claims Processing Automation
  Outcome-based pricing can be employed where insurers pay according to the number of claims successfully automated or resolved without human intervention, similar to how Intercom charges for resolved support tickets. This encourages efficiency and demonstrates ROI.

• Agent and Broker Tools
  Seat-based subscriptions combined with AI credit systems can be used for collaborative sales platforms. Agents pay a base fee per user with additional credits for advanced AI features such as predictive analytics or customer insights, balancing predictable revenues with variable costs.

• Customer Engagement and Chatbots
  Subscription models with overages or credits can apply to AI chatbots that assist policyholders. Insurers should implement usage caps and alert systems to avoid unexpected billing and maintain policyholder trust.

• Risk Management and Fraud Detection Services
  These services can integrate hybrid pricing models, where a base subscription covers standard monitoring and additional fees correspond to the number of flagged or investigated cases, aligning costs with actual service utilization.

Conclusion and Recommendations

As AI becomes integral to insurance operations, adopting flexible and transparent pricing strategies is crucial. Insurers must move beyond traditional subscription models to pricing structures that account for variable AI costs while delivering clear value to clients. Cross-disciplinary collaboration between technical, actuarial, and financial teams will be essential to develop sustainable pricing frameworks. Clear communication with end-users about usage metrics and billing can preserve trust and satisfaction.

Insurance professionals should evaluate their current pricing in light of AI-driven cost dynamics and consider piloting usage-based or outcome-based models where feasible. Continual monitoring and adjustment will ensure competitiveness in this rapidly evolving technological landscape.

For a comprehensive exploration of AI pricing challenges and models, including detailed examples and principles, read the original article by Anh-Tho Chuong available here.