Picture this: A major insurer invests millions in cutting-edge AI technology, only to watch it spiral into a PR nightmare when their claims chatbot makes unauthorized coverage promises. For example, a health insurer's AI system makes potentially life-altering coverage decisions that go undetected for months.
Although we wish situations like these were hypothetical, they’re real effects of rushing into AI implementation without proper governance in insurance operations. As artificial intelligence transforms the insurance industry at breakneck speed, carriers and their AI partners face a critical choice: embrace structured AI governance or risk joining the growing list of cautionary tales.
The reality is stark. Insurers regularly stumble into four major pitfalls when deploying AI:
In their 2025 Market Guide for AI Trust, Risk and Security Management, Gartner research confirms similar pitfalls for strategic planning. Through 2026, at least 80% of unauthorized AI transactions will be caused by internal violations of enterprise policies concerning information oversharing, unacceptable use or misguided AI behavior rather than malicious attacks.
But there's good news: These pitfalls are entirely preventable.
Insurance companies that are thinking ahead are finding that strong AI governance is not just about risk management. It is a powerful way to create new ideas, improve operations, and grow in the insurance industry.
As more companies use AI, governments and industry groups are creating rules to make sure it's used safely, fairly, and responsibly. While maintaining regulatory compliance is important, good AI governance does more than just check boxes - it helps organizations:
To succeed with AI, companies must take a comprehensive approach to help their organizations understand both the technology and the importance of proper governance.
That said, many organizations chose to use AI before they knew its impacts, or they had AI inserted into embedded vendor products with little time to learn the effects. No matter the situation, they both foster siloed projects and a lack of understanding about where AI should actually enhance experiences across silos, not slow them to a halt or make them more rigid.
Even if a company’s AI adoption plan didn’t happen in a magnitude of order, they should still look for opportunities to establish literacy and alignment. This effort starts with training all employees in AI basics, creating clear communication between technical and business teams, establishing processes to review and improve AI systems, and defining clear AI responsibilities for each team member.
If you don’t know where to begin with organizational AI literacy, or you’ve been tasked to reset roles and expectations across multiple business functions, check out primers such as the The Essential Guide to AI Governance or the exclusive follow-up guide AI Governance: From Framework to Implementation (OCEG)
This question verbatim comes up often, and it's understandable why. The term "governance" often carries negative associations, stemming from organizations that have historically turned processes into bureaucratic or fragmented quagmires. Yet when decision-makers can't trace who did what and why in algorithmic systems, AI's effectiveness diminishes due to lack of transparency.
Part of organizational literacy is alignment around what governance means for extracting value from AI. Consider the impact of governance in two ways:
What’s critical is finding an AI governance solution that enables your teams to take actionable steps throughout the lifecycle of your AI system.
Organizations that invest in great AI governance and organizational literacy are better equipped to use AI successfully while reducing risks. They can move faster, make better decisions, and create more value from their AI investments.
If you want to learn how to build AI literacy, standards, and active governance in your organization, contact us.
