Artificial intelligence is making waves across industries, but nowhere is the conversation more charged than in insurance. With every new breakthrough, the buzz grows louder—yet, as industry leaders like Laura Doddington of WTW point out, separating hype from genuine progress is crucial for insurers looking to harness AI’s true potential.
The Real Value: Targeted AI Applications
The insurance sector is no stranger to innovation, but the real promise of AI lies in its targeted, practical uses. Rather than chasing grand visions of full automation, forward-thinking insurers are focusing on specific use cases where AI can deliver measurable value.
Take claims triage, for example. Machine learning models are already helping insurers quickly sort claims—identifying which are straightforward and can be fast-tracked, which might be fraudulent, and which require deeper investigation. This not only speeds up the process but also ensures resources are allocated where they’re needed most.
Underwriting is another area seeing tangible benefits. AI tools don’t replace underwriters, but they do offer smart recommendations, helping professionals prioritize their workload and make more informed decisions. The result? Greater efficiency and improved risk assessment.
Generative AI: Unlocking Unstructured Data
While machine learning has laid the groundwork, generative AI—especially large language models (LLMs)—is opening new doors. One of the biggest challenges in insurance has always been making sense of unstructured data, like freeform text in claims reports or call center transcripts. LLMs can now extract, organize, and analyze this information at scale, turning previously untapped data into actionable insights.
Imagine a claims handler’s detailed notes being automatically summarized and structured, feeding directly into fraud detection systems or underwriting models. Or consider customer service environments, where AI can distill lengthy conversations into concise summaries, saving time and improving service quality.
The Next Frontier: Agentic AI
Looking ahead, agentic AI—systems that can autonomously make and communicate decisions—could further transform insurance. While still in its early days, this technology holds promise for automating routine decision-making, freeing up human experts to focus on complex or sensitive cases. However, the industry is proceeding with caution, recognizing that these tools must be deployed thoughtfully and responsibly.
Why Human Oversight Still Matters
Despite the excitement, it’s important to keep expectations grounded. Insurance is a highly regulated field where accuracy, advice, and human judgment are paramount. While AI can handle data processing and routine workflows, it’s not yet reliable enough to operate independently—especially in customer-facing roles.
AI systems, particularly large language models, are still prone to errors or “hallucinations”—confidently incorrect outputs that could have serious legal or reputational consequences. That’s why human oversight remains essential. Insurers need robust guardrails: humans reviewing AI outputs, validating decisions, and ensuring technology is used ethically and effectively.
Actionable Takeaways for Insurers
- Focus on targeted AI applications with clear business value.
- Use AI to augment, not replace, human expertise—especially in regulated or high-stakes areas.
- Prioritize human oversight and validation to mitigate risks.
- Stay informed about emerging technologies like agentic AI, but adopt them thoughtfully.
Summary: Key Points
- The real promise of AI in insurance lies in targeted, practical applications—not full automation.
- Machine learning and generative AI are already delivering value in claims triage, underwriting, and data analysis.
- Agentic AI could automate routine decisions in the future, but is still in early stages.
- Human oversight is essential to ensure accuracy, compliance, and responsible AI use.
- Insurers should focus on augmenting human expertise, not replacing it, to achieve the best outcomes.