The insurance industry’s embrace of artificial intelligence (AI) is driving revolution across underwriting, claims processing, customer service, risk assessment, and other critical function areas. As AI adoption accelerates, insurance IT leaders have the opportunity to create valuable partnerships to accelerate their business’s successful digital transformation.
Effectively evaluating AI vendors requires knowing the right questions to ask and understanding which responses indicate strong partnership potential can significantly improve your odds of delivering long-term ROI from your AI investment.
Here are six key areas where vendor transparency and detailed responses indicate strong partnership potential – and where a lack of clarity suggests looking elsewhere for the ideal fit.
Non-specific answers about "following industry best practices" or "taking security seriously" without adding specific certifications or audit details.
Customer data security isn't negotiable in insurance. Your AI vendor should demonstrate compliance with SOC 2 Type 2 and ISO 27001 certification, undergo regular independent audits, and provide clear evidence of HIPAA, CCPA, GDPR, and 23 NYCRR 500 compliance to protect PII, PHI, and PFI.
Consider it a warning sign if a vendor can't produce current audit reports on demand and engage in a productive conversation about their specific compliance measures. Handling insurance data requires nothing less than a provider’s total organizational commitment to data protection through verifiable actions.
If a vendor is unable to provide demonstrations of their product functionality in operational areas that are material to your strategic needs, this might mean that their solutions might not be mature enough for your needs.
Experience matters in insurance AI. A vendor might have impressive technology, but without understanding the nuances of property and casualty underwriting, life insurance claims processing, or commercial risk assessment, their solution likely won't deliver meaningful results.
Push vendors beyond surface-level presentations. Tell them exactly which lines of business are in scope and which phases of your value chain you want addressed. Quality vendors should demonstrate their solution working with insurance-specific data, workflows, and decision points while aligning clearly with your target operating model. Stating definitive performance metrics and charging customers only when they deliver on these projections are a strong indicator of a service provider’s ability to deliver proven solutions that will meet your business’s needs.
If a vendor’s presentation isn’t fully aligned to your specific insurance context, inquire further to verify they have the deep industry expertise necessary for successful implementation.
Emphasis on the vendor’s existing pilot projects/PoCs or generalized answers (e.g., "we work with several major insurers”) lacking specific details or about deployments in production environments.
Proof-of-concept exercises are critical but seldom deliver concrete evidence that an AI solution can handle your business’s need for accuracy, scalability, or reliability. Vendors earning your consideration should be able to demonstrate excellent performance in real-world environments.
Be sure the vendor can refer you to customers who’ve moved their solutions beyond the test bench. Prepare questions about scalability, integration challenges, ongoing support, and actual business outcomes to avoid being an AI provider's insurance “test mule.”
General timelines without specific milestones, high-level QA descriptions, or reluctance to share detailed examples of past delivery performance.
AI implementation in insurance benefits from rigorous testing, validation, and quality assurance. Models perform best when thoroughly tested against diverse scenarios, edge cases, and exception handling. Top vendors have well-defined QA protocols and can demonstrate consistent delivery of production-ready solutions.
Look for vendors who clearly articulate their testing methodology and provide specific examples of ensuring model accuracy, reliability, and compliance. Transparency here indicates mature development processes and reduces unexpected delays or post-deployment issues.
Overly technical explanations without business context. Broad descriptions of training data and statements like "we retrain every six months" without explaining ongoing monitoring techniques.
AI models thrive with continuous monitoring and improvement, especially in insurance, where regulations, market conditions, and risk factors constantly evolve. Look for vendors that can clearly explain their training approach to business executives, provide details about data sources and quality controls, and describe comprehensive approaches to bias detection and model drift management.
The "retrain every six months" response demands greater scrutiny – understanding model performance between retraining cycles is crucial. Leading vendors implement continuous monitoring with real-time adjustment processes. They demonstrate clear understanding of bias issues and have established detection and correction protocols, showing commitment to responsible AI deployment.
HITL processes that don’t emphasize flexible thresholds, unclear exception handling processes, or inability to explain how human feedback improves the system.
Insurance AI needs robust human oversight and feedback mechanisms. You should focus on working with vendors that emphasize human-in-the-loop processes and tools to empower experts to drive better decision-making, reduce risk, and ensure continuous model performance improvement. Any vendor you’re considering should also have clear processes for handling exceptions, edge cases, and situations requiring human expertise.
If a vendor can't explain how human experts remain involved in AI decision-making – how exceptions are escalated and resolved – their solution may not be effective at solving problems in complex insurance scenarios.
Successful AI implementation is more than contracting a technology provider. Seek vendors who have proven their value as strategic partners committed to your long-term success.
By asking informed questions about security, industry experience, production deployments, delivery capabilities, AI training approaches, and human oversight processes, you can identify AI providers who share your philosophy and goals for operational efficiency, improved customer experience, and business success.
The right AI partner welcomes detailed discussions and provides comprehensive, clearly documented responses that build confidence in their ability to deliver transformational results.
Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.