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While designing an audit plan for my span, we always end up doing fewer audits than agreed at the beginning of the year. The reduction is due to some exceptions taken by the account owners for exemption from audits as their respective processes don't deal with sensitive information of customers and are inherently low in risk. Some exceptions are due to higher call volumes and band width issues of the spocs involved in the audit. Currently when these exceptions are generated, we have to manually check for the validity of the reasons considering the risk involved, the headcount and revenue of the process etc and approve the exceptions. Considering the last two to three years worth of data along with the latest risk report, AI should be able to track common patterns like exceptions being taken due to low risk, low revenue generating accounts, call volumes as per seasonality etc and decide to learn common practices. We can have some guidelines to ignore exceptions due to band width as this may not always be a factor. However, AI can learn the risk patterns and implement while creation of future audit plans.

Our internal team has developed an AI-based platform to manage the internal queries raised by team members as part of managing their activities. The knowledge base (KB) is collated from training material, standard operating processes, client feedback, and other relevant sources. However, the AI may need to escalate scenario-based questions that are not part of the KB to humans for resolution. In such cases, the AI can learn from these provided solutions by: Analyzing the given responses. Identifying processes where such exceptions occur frequently. Measuring the time taken for responses. Detecting any patterns or rules in the responses collected through such exceptions. Tracking the frequency of these exceptions. Assessing the need to update the KB based on the frequency and nature of exceptions. Monitoring further queries that arise post such exception cases. This approach ensures continuous improvement of the AI system, enhancing its ability to handle a wider range of queries autonomously over time.