You're integrating AI into underwriting. How do you ensure a smooth transition for your team?

Tell your team about the benefits and train them on how to use AI tools. Start small and gradually introduce AI into your work. Track your progress and be ready to make changes. Encourage collaboration and celebrate your successes.

It is important to understand that AI is not gonna replace the underwriters but rather assist them in improved risk assesment. The successful integration of AI will depend on the openness of the underwriting team to new ideas. It is about acquiring a new technique for more effective results.

Pilot programs are important to establish the confidence of the underwriting team. Parallel system/ process should be established to double check and insure the underwriters that the risk selection is in the appetite and knowckout decision making is full proof to augment the incorrect decision making due to hallucinated results. Stakeholders will be confident, once the right set of understanding is instilled

Whether integrating AI into underwriting or launching a generative AI chatbot like my team here at Migdal Group did, I like to encourage my team to ask a simple question that encourages them to think about things from a new perspective… "What if? What if… we developed a generative AI chatbot for our website, instead of simply adding a traditional chatbot?" Asking these sort of questions sparks immediate enthusiasm and encourages teams to rapidly mobilize internal resources, realigning priorities and dedicating extra effort.

Like in every change program it is important to overcome push-backs. AI has the potential to threaten experienced underwriters in particular. Underwriting expertise is a main differentiator in underwriting departments and all of a sudden implicit knowledge becomes explicit. An even playing field for everybody! It takes some time to overcome the „not invented here attitude“. Therefore, it is important to involve the best people from the beginning and team them up with individuals having the technical expertise. You will be surprised about the outcome!

Equip your team with the knowledge they need through comprehensive training programs. Focus not only on the technical aspects but also on how AI can improve their workflows. Tailoring training to different skill levels can ensure that everyone is on board and confident.

When introducing AI into underwriting, clarity and alignment with leadership are crucial. First, focus on the value: explain how AI will enhance risk assessment, reduce errors, and streamline processes. Address potential concerns by emphasizing that AI is a tool to augment human expertise, not replace it. Implement comprehensive training programs to ensure your team is equipped with the skills needed to work effectively alongside AI. Be transparent about the implementation process and set realistic expectations on outcomes to ensure leadership buy-in. How are you positioning AI to your leadership team?

Make sure support continues beyond the initial transition period, so team members can ask questions and discuss any challenges they face. You might also want to set up a mentorship or buddy system to provide extra guidance for those who need it.

Investing in tailored training programs is the cornerstone of a successful AI transition. I focus on offering diverse learning formats—workshops for collaborative learning, online courses for flexibility, and one-on-one coaching for personalized support. Practical, hands-on experience is non-negotiable; it’s where confidence and capability truly grow. By aligning training with real-world applications, the team feels empowered, not overwhelmed, ensuring a seamless integration of AI into their workflow. How do you approach skill-building during tech transitions?

when integrating AI into underwriting, adopt a gradual approach, blending AI tools with existing workflows. Provide continuous training, encourage open dialogue, and address concerns to boost team confidence and efficiency.

Gradual integration is key to making AI adoption smooth and successful. I start with pilot projects targeting high-impact, low-risk tasks to show immediate results and build confidence. This step-by-step approach gives the team time to adapt while gathering valuable feedback to refine the process. Celebrating milestones—no matter how small—keeps morale high and reinforces the benefits of AI. How do you ensure a phased rollout resonates with your team?

Rushing into AI implementation can lead to frustration and resistance. Gradual integration allows your team to adapt at a manageable pace, with phased rollouts giving time to learn and adjust. Start with low-risk processes and scale as comfort grows.

AI is definitely automating mundane tasks but should not be heavily relied on. We are actively using AI in several areas of our InsurTech but gradually integrating it into our systems. Certain tasks require human intervention and as we’ve seen with recent Google content updates, major reliance on AI can lead to huge losses. Incorporate your unique experiences into AI and you will stand out. Human touch + AI is a special recipe. Not AI instead of human.

Start with a focused pilot project. Choose a specific area, such as risk scoring or fraud detection, where AI can deliver quick, measurable impact. Ensure the pilot runs on clean, structured data and involves both AI tools and human oversight for validation. Set clear KPIs like accuracy, efficiency, and reduction in processing time. Regular feedback loops with underwriters will help refine the process and demonstrate AI's value before scaling it across your operations. How would you structure a pilot project for AI in underwriting?

Actively seek feedback from your team throughout the transition. Let them share their experiences, identify pain points, and suggest improvements. This will foster a sense of ownership and help fine-tune the AI system based on real-world use.

Creating strong feedback loops is non-negotiable during AI transitions. I prioritize regular check-ins to gather insights from the team about their experiences, challenges, and successes with the new tools. Acting on this feedback shows that their input matters, fostering a sense of ownership and collaboration. It’s about co-creating the process, not imposing it, which drives both better adoption and system improvement. How do you keep your team engaged and empowered during transitions?

Feedback loops are vital for integrating AI into underwriting. Companies with structured feedback processes are 21% more likely to achieve smooth tech adoption. So, what works? ✔️ Regular check-ins: Weekly sessions or anonymous surveys to gather honest insights. ✔️ Act on feedback: Teams are 4.6x more likely to embrace change when their input leads to visible improvements (HBR, 2022). ✔️ Monitor metrics: Track underwriting speed, error rates, and user satisfaction to refine processes. Empower your team by making them active participants in the transition. When people feel heard, they embrace change and any transformation should prioritise the people element over the technology.

Keep track of how the integration is impacting both performance and employee morale. Use data to assess productivity and efficiency improvements, but also listen to team members’ qualitative feedback to ensure the transition remains smooth.

Monitoring progress is essential to ensure AI integration delivers on its promise. I establish clear, measurable success metrics from the start, focusing on both system performance and team adaptation. Regular reviews with the team keep everyone aligned and provide opportunities to address gaps in training or support. It’s a balance—fine-tuning the technology while reinforcing confidence in its value. How do you track and measure success during significant operational changes?

Monitoring progress is essential when integrating AI into underwriting. Set clear success metrics—such as accuracy rates, processing times, and user adoption levels—and review them regularly with your team. 💡 Pro tip: Pair these reviews with team feedback sessions to ensure you’re addressing both system performance and user challenges. Research shows that teams who regularly evaluate outcomes are 33% more likely to sustain improvements (McKinsey, 2023). By tracking both technical outcomes and human adaptation, you can identify training needs early, refine workflows, and ensure that innovation enhances—not disrupts—underwriting excellence.

Building a robust support system is the backbone of a successful AI transition. I focus on creating multiple layers of assistance, like designating AI champions, setting up a responsive help desk, and encouraging peer mentoring. This ensures that everyone has access to guidance when they need it and fosters a culture of collaboration. When the team knows they’re supported, they’re more confident and open to embracing change. How do you ensure your team feels equipped and empowered during transitions?

Offer ongoing support, whether through dedicated AI specialists or a peer-based system. Knowing that help is available whenever they encounter issues builds confidence and ensures that the AI implementation doesn’t disrupt daily operations.