2025 Best Practices Study

1) Map employee workflows

The first and most critical step is to map out employee workflows in detail. AI solutions are only valuable when integrated into employees’ daily tasks and existing platforms. If the AI application sits outside of the workflow, requiring users to toggle between systems or perform extra steps, adoption rates will plummet. This mapping should identify core responsibilities and repetitive tasks; manual processes that rely on judgment, pattern recognition, or structured decision-making; current bottlenecks and inefficiencies; and platforms or tools currently used by employees. The mapping process creates the context for AI and helps ensure that solutions are not built in isolation but instead solve real problems where users already work.

2) Identify use cases and define KPIs for success

With workflows documented, technology leaders can identify use cases where AI is likely to create the most immediate impact. Look for tasks that are repetitive and time-consuming or that require interpretation of unstructured information such as emails, PDFs, or policy documents as well as those that have clear benchmarks for improvement (e.g., time saved, error rate reduction).

Defining success early on is just as critical. What will a successful implementation look like? Setting specific KPIs ensures alignment among stakeholders and enables data-driven evaluation post-rollout.

3) Assign a pilot team and build a beta model

Select a small, representative group of users to test the AI tool in a low-risk, high-value function of the business. The pilot team should include individuals who are open to new technology and can provide constructive feedback. The goal is to build a functional “beta” model that proves feasibility while allowing for iteration.

During this phase: • Collect user feedback continuously • Track defined KPIs • Identify integration gaps or resistance patterns • Refine the interface and user experience

This step reduces risk and builds internal advocates who can help champion broader rollout efforts.

4) Prepare and release into production with training and change management

Even the best tools will fail if users aren’t trained, supported, and bought in. Organizations must be transparent about what’s changing and why. Transparency reduces resistance and proactive communication builds trust. Training should be role-specific, practical, and outcome-driven. Rather than offering general AI overviews, training should walk employees through exactly how the tool works within their specific workflow. Resources might include hands-on workshops, interactive onboarding guides, peer-led training sessions and support channels for troubleshooting. The goal is not just to teach functionality but to show how the AI tool helps each employee do their job more effectively.

5) Post-release monitoring and reinforcement

Launch is not the finish line—it’s just the start of value realization. Post-implementation, the organization should continue tracking KPIs and monitoring usage patterns to ensure adoption. Many tools fail because

Generative AI: Transforming Insurance Practices

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