The Wave of AI Adoption Transforming the Insurance Industry
Mitsui Sumitomo Insurance has announced plans to introduce AI for evaluating its insurance agents. This initiative automates and enhances the performance evaluations and risk analyses of agents, which were previously done manually by staff.
At first glance, this might seem like a simple case of operational efficiency. However, this move holds important implications for business leaders. Automating evaluations with AI has the potential not just to cut costs, but to fundamentally change the “quality of evaluations” themselves.
In the non-life insurance industry, there are approximately 200,000 agents nationwide, and each company evaluates them based on factors like sales target achievement and compliance status. This evaluation process requires collecting and analyzing vast amounts of data, placing an increasing burden on staff year after year.
To address this challenge, Mitsui Sumitomo Insurance has built an automated evaluation system using AI. By learning from past evaluation data, transaction records, and risk indicators, the system aims to deliver objective and rapid evaluations.
The Core Value of Introducing AI Evaluations
Enhancing Fairness and Transparency
Human evaluations are inevitably influenced by subjectivity. It’s not uncommon for the same performance to be rated differently by different evaluators. AI-based evaluations, on the other hand, make decisions mechanically based on pre-set criteria, reducing variability in assessments.
In my own experience automating contract reviews with AI, I’ve found that AI can consistently check for clauses that humans might overlook. Similar benefits can be expected in evaluation tasks.
Speeding Up Evaluation Processes and Reducing Workload
Agent evaluations require collecting and analyzing a wide range of data, including sales figures, customer satisfaction survey results, and compliance training completion rates. With AI, this data can be automatically gathered, integrated, and used to generate evaluation reports instantly.
In Mitsui Sumitomo Insurance’s case, it’s estimated that the time spent on evaluations can be reduced by about 70%. This frees up staff to focus on supporting agents and strategic planning based on the evaluation results, rather than the evaluation process itself.
Key Points for Business Leaders When Introducing AI Evaluations
Designing Evaluation Criteria is Key to Success
When implementing AI evaluations, the most critical factor is designing the evaluation criteria. Without clearly defining which metrics to prioritize and how to weight them, the AI cannot make proper assessments.
In Mitsui Sumitomo Insurance’s case, the goal is a comprehensive evaluation that combines multiple indicators, including not just sales target achievement but also compliance status and customer satisfaction. In this way, introducing AI also presents an excellent opportunity to review the evaluation criteria themselves.
Data Quality and Quantity Determine Outcomes
The accuracy of AI evaluations heavily depends on the quality and quantity of training data. If past evaluation data is insufficient or biased, the AI may learn incorrect evaluation standards.
Before implementation, it’s crucial to check the types and quality of available data and, if necessary, establish a system for data collection. In particular, a mechanism to verify the validity of evaluation results (such as human spot checks) is essential.
Estimating Cost-Benefit
The cost of introducing an AI evaluation system varies greatly depending on the type of AI service used and the degree of customization. For large companies like Mitsui Sumitomo Insurance, in-house development or extensive customization may be necessary. However, for small and medium-sized enterprises, it’s possible to add AI features to existing SaaS-based evaluation systems.
Monthly costs can range from approximately $350 to $700 for small-scale implementations, to tens of thousands of dollars for full-scale customization. Before implementation, it’s advisable to estimate the expected benefits (such as reduced labor costs or increased sales from improved evaluation accuracy) to make an informed investment decision.
Secondary Benefits of Introducing AI Evaluations
Visualization and Analysis of Evaluation Data
AI-based evaluations not only produce results but also visualize the reasoning behind them. By clearly showing why a particular evaluation was given and which indicators influenced it, the quality of feedback to those being evaluated improves.
Additionally, analyzing accumulated evaluation data can help review and refine evaluation criteria. AI may uncover trends and patterns in evaluations that are difficult for humans to see.
Eliminating Reliance on Specific Individuals
When evaluation tasks depend on specific staff members, their departure can lead to a loss of evaluation know-how. Building an AI-based evaluation system allows the organization to share and pass on evaluation expertise.
In my own work on operational efficiency, eliminating reliance on specific individuals is a major theme. By teaching AI the rules and judgment criteria, anyone can perform evaluations of the same quality.
The Next Step in Leveraging AI
The case of Mitsui Sumitomo Insurance shows that AI is not just a tool for operational efficiency but a management resource that improves the very quality of evaluations. Automating evaluations with AI is a method applicable not only to the insurance industry but to all industries and business types.
For example, AI can be introduced for various evaluation tasks, such as sales performance reviews, personnel evaluations, supplier assessments, and customer satisfaction surveys. The key is to view AI adoption not as “cost reduction” but as “improving the quality of evaluations.”
Automating evaluations with AI is still in its early stages. However, once a system is built, it can dramatically improve the fairness, transparency, and efficiency of evaluations. As a business leader, why not consider whether introducing AI into your company’s evaluation processes is worthwhile?
As a first step toward AI adoption, start by visualizing the current state of your evaluation tasks and identifying which ones could benefit from AI. By accumulating small successes, you can accelerate AI adoption across your organization.
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