Vetting an agentic AI provider in the insurance space requires a multi-dimensional approach that balances innovation with risk, compliance, and a long-term strategic fit.
Before you begin, consider your present-state conditions, especially the underwriting and claims customer experience. For example, you should have a clear view of how easily brokers or agents can submit applications and get prices, or how your present tech is (or isn’t) enabling fast claims filing and settlements – and how this information informs your AI automation goals.
The process of vetting an AI provider is unique to your organization’s products, people, and practices. However, that’s not to say there aren’t proven methodologies, governance structures, and measurement tactics that will help streamline the task.
Here’s a proven framework based on best practices and expert insights that you can adopt when performing discovery on whether an insurance AI vendor will provide the right fit to drive efficiency and profitability.
1. Define the Type of AI Vendor
Before diving into evaluate a vendor, make certain your team understands who you’re researching and validating:
- Clarify the vendor’s role: Are they offering standalone agentic AI tools or embedding AI into broader platforms?
- Understand and define how your teams will use AI: What are the applications – underwriting, claims automation, fraud detection, customer engagement, or others?
- Assess the vendor’s tech stack: Are they building proprietary models or leveraging third-party solutions, like public AI?
2. Identify Key Risk and Compliance Considerations
Agentic AI introduces unique risks due to its autonomy and adaptability. In general, your AI vendor should possess deep insurance domain knowledge. One critical element of this is an understanding of how to protect your data and your customers’ privacy and data security. After vetting the vendor, you should have clarity on potential issues around:
- Data security compliance: Can a vendor document that they meet all certification requirements for ISO 27001, SOC 2 Type2, HIPAA, CCPA (where needed), as well as NYCRR 500 and GDPR compliance?
- Regulatory exposure: Ensure the vendor complies with any state-specific insurance regulations wherever you do business, especially for underwriting and claims.
- Data privacy: Evaluate how the vendor handles sensitive policyholder data. Can personally identifiable information (PII) data be removed or anonymized?
- Model explainability: Transparency is critical to building trust. Can the AI’s decisions be audited and explained to regulators or customers?
- Cybersecurity: Protecting your customers’ data is paramount. What safeguards are in place to prevent unauthorized access or misuse?
- Ethical governance: Does your organization have a framework for bias detection, fairness, and human oversight?
3. Evaluate Operational Risk Mitigation Strategies
Once you've identified potential risks, examine how vendors plan to address them through concrete safeguards. Examples of this can include robust Human-in-the-Loop functionality to ensure human oversight and improve accuracy through future learning.
Apply the following criteria to determine if a potential partner adequately covers gaps in your existing controls, provides sufficient legal safeguards, and includes robust technical measures for secure AI deployment:
- Gap analysis: Compare their capabilities with your organization’s existing risk controls.
- Contractual protections: Does the vendor include clauses for data ownership, audit rights, and triggers for termination?
- Technical controls: Do they use pseudonymized datasets, access restrictions, and sandbox environments for testing?
- Operational availability/resilience: Is the technology available at times to suit business hours – and how does the vendor respond to service outages?
4. Assess Strategic Fit and Scalability
To confirm an AI vendor can support both your current workflows and long-term growth, evaluate how well their solution aligns with your strategic priorities and operational realities. Use these guidelines to gauge whether their technology can integrate and evolve alongside your business needs:
- Modular adoption: Can the vendor implement agentic AI in stages? Key use cases requiring repetitive manual tasks, like policy submissions, FNOL (First Notice of Loss) or claims indexing are ideal starting points for AI-powered automation.
- Integration complexity: Consider how well the vendor’s solution integrates with your legacy systems. Does the vendor require APIs for any integrations?
- Proof of Concept: Does the vendor have experience consulting streamlined paths to production (e.g., broad “gated” use case implementation and measurement strategies to determine business value)
- Data fit: Will the vendor’s AI deliver high accuracy results using my specific data?
- Scalability: Can the solution grow with your business and adapt to new lines of business, evolving risks like climate or cyber threats?
5. Watch for Red Flags
Even the strongest demos can hide underlying risks, so it’s essential to scrutinize signs that a vendor may struggle to deliver on their promises. These checkpoints will enable your team to identify gaps in transparency, expertise, compliance, or support that could jeopardize your AI initiative:
- Proof of concepts positioned as successful client implementations: The vendor is still lacking in the number of successful implementations and not proof of concepts.
- Limited or small number of referenceable clients: Can the vendor produce three current customers who will provide a reference?
- Opaque decision-making: There is a lack of transparency in how their AI solutions reach conclusions.
- Overpromising capabilities: Claims of full autonomy without clear boundaries or oversight are a definite red flag. Will they back their stated accuracy levels with monetary commitments?
- Weak compliance posture: Can they produce a clear roadmap for regulatory alignment?
- Limited domain expertise: Don’t trust insurance applications with a vendor that doesn’t live and breathe insurance concepts and taxonomies.
- Inflexible contracts: Does a vendor allow for customization of service terms or exit strategies for missed targets?
- Poor support and training: Lack of onboarding, documentation, or ongoing education.
Best Practices for Implementation
Ask the vendor to explain how their internal knowledge and “lessons-learned” from production environments have driven previous successful insurance AI integrations. Their answers should include some or all from this list:
- Establishing strategic alignment with prospective customers
- Building cross-functional teams and identifying skilled resources
- Creating AI governance frameworks
- Ensuring data governance for quality, privacy, lineage, and access controls
- Keeping project scope in check Manage change proactively Having a strategic roadmap for scaling AI deployments
- Documented best practices/“lessons-learned” from previous pilot programs
- Development of repeatable deployment frameworks
- Rigorous testing regimes throughout the process
Applying a structured and strategic approach to vetting AI vendors gives your teams a solid foundation for confidently pursuing AI initiatives without sacrificing compliance, security, or operational stability. Implementing the above best practices can help you cut through marketing noise to focus on your unique challenges, select measurable proof points and milestones, and create partnerships capable of driving real impact.
With a clear roadmap and a shared internal understanding of goals, your organization will be better equipped to simplify the vetting process and adopt AI solutions that build organizational resilience and profitability.
Make it easy for your team to review, evaluate, and approve AI vendors, without losing track of the process. Get the AI Vendor Vetting Toolkit.