In companies, AI models are developed by highly specialized technical teams, while business users benefit from the results to make informed decisions and perform their work effectively. However, a fundamental problem often arises: the lack of understanding of AI's internal decision-making mechanisms and the reasons behind its results. Although business teams have sophisticated visualization and analysis tools, these solutions are usually limited to showing basic metrics without including information about explainability or bias analysis. This forces business managers to constantly rely on the technical team to interpret results, creating a dependency that translates into significant economic costs in terms of time, meetings, and lost opportunities. Below, we explore an example that illustrates this issue:
Context
An insurance company implemented an artificial intelligence system to optimize risk assessment and determine insurance premiums. This system analyzes multiple variables (such as policyholder history, demographic data, and claims patterns) to provide accurate risk assessment and adjust premiums in a personalized manner.
The Business Tool
The business team has an indicator visualization platform designed to monitor the AI system's performance and risk assessment results. However, this tool is limited to presenting basic data and does not include relevant information about how decisions are made or the presence of potential biases in the model. As a result, the business team lacks a complete view of the "why" behind the results provided by the AI.
The Communication and Explainability Problem
Due to this lack of information, the business team faces constant uncertainty about the interpretation of results. To clarify doubts and justify the algorithm's decisions, they must repeatedly turn to the AI technical team, which generates several inconveniences:
- Frequency of Requests:Each adjustment in the commercial strategy or in the definition of new policies requires detailed reports explaining the model's behavior.
- Technical Team Overload:AI specialists already have a high workload due to the constant updating and maintenance of models, which causes delays in the preparation of requested reports.
Economic and Operational Impact
The disconnection between business and technical teams generates significant consequences:
- Delays in Decision Making:
- Premium and Strategy Adjustments:The delay in receiving critical reports prevents the business team from timely adjusting premiums and market strategies, which can translate into lost business opportunities.
- Competitiveness:The lack of response agility reduces the company's competitive capacity, risking losing customers to competitors who adopt more transparent and agile AI systems.
- High Operational Costs:
- Time and Resources:The constant need to coordinate meetings and analyze technical reports consumes valuable time from the management team, which could be used for growth initiatives and operational improvement.
- Resource Reallocation:The dependency on the technical team to generate these reports forces the diversion of resources from other critical tasks, increasing internal costs and affecting overall efficiency.
- Reputational and Regulatory Risk:
- Deficient Transparency:The lack of clear and timely information about AI's decision-making mechanisms generates doubts among both regulators and customers, affecting the company's reputation.
- Possible Sanctions:In an increasingly strict regulatory environment, the inability to demonstrate the transparency and fairness of AI models increases the risk of sanctions and litigation.
Conclusion
This case strongly illustrates how the dependency (sometimes disconnection) between the business team and the AI technical team can lead to significant economic and operational costs. A visualization tool that does not integrate information about explainability and bias analysis forces the business team to continuously rely on technical reports, which are generated late due to the AI team's workload. This situation delays strategic decision-making, increases operational costs, and puts both the company's competitiveness and reputation at risk. The experience underscores the urgent need to develop Explainable AI solutions that are intuitive and accessible to all levels of the organization, enabling fluid communication and informed decisions in real-time.