SundarNag

In the Analyze phase we need to do a Fish bone analysis to identify all the causes that are impacting the outcome. By using the Pareto analysis we can identify what are the major causes that have an impact on the output. We can then do a Why - Why analysis for each cause to identify the root causes. Basis the data collected in the earlier phases of the DMAIC approach, we will use hypothesis testing on the high impacting root causes. This will confirm statistically if the root cause we have identified is indeed causing the impact. The Why Why analysis to identify the root causes and a hypothesis testing usually confirm what kind of relationship exists between the identified Xs and final output Y. Taking a data driven approach and accepting the statistical outcome will always confirm what are the real causes and what is just a symptom.

In a BPO environment where utility bills are generated and payments are received, below are various AI agents that can be a part of . 1) First AI agent calls the customer, guides them to take a picture of their electricity meter and submit it on the website 2) Second AI agent will process this and updates the records 3) 3rd AI agent should generate a bill 4) 4th agent will request for a payment from customers bank details 5) 5th agent will obtain the amount and assign the payment to the correct customer's profile 6) 6th agent should share reminders in case the payments are not received Below are a couple of scenarios where there could be challenges in their co-ordination Scenario 1 There may be issues with the picture being shared by the customer which the 2nd agent may not be able to read clearly. It should identify in case of any inconsistent readings and highlight this to the customer instead of generating a bill. To avoid this we should train the agent to read the previous values and compare the current one against it. If the current reading is lower than the previous one then the agent should throw an exception and stop the next AI agent from generating a bill Scenario 2 Once the bill is generated then the 3rd AI agent should take the payment from the correct bank details. In case there is more than one bank detail the agent should know how to select the correct bank account. In case the payments are not received then 6th AI agent should start sending reminders highlighting what went wrong while attempting to get the payment

While doing Quality Assurance checks, where a Quality checker has to check the details updated by agents against the correct values to be selected while supporting the customer, there are many screens to be reviewed in a CRM. For some screens especially where the money refund options are dependent on the designation/grade of the employee, AI should be able to detect if the right amount was processed as per the requirement. When errors happen the requirement would be to check internally who has committed the error, whether this is reversible or not, the magnitude of the issue (financial impact), time taken to fix the issue before these are escalated to the team leaders first, then to operations managers and then to the client. Relevance: In BPOs supporting the utilities industry where these kind of refund transactions happen very frequently we face a number of challenges in identifying the right bank accounts the amount is transferred, the right amount that is transferred and if the person transferring has required authority or not . This often leads to delays in processing as there are many manual checks that are included at every stage. Solution & Benefits: AI can be built to do a Quality Assurance work by validating the employee IDs against their authority matrix. Relevant manual blocks can be applied until AI can learn every scenario that is available wherein AI can trigger a human response requests. By clearly defining the necessary triggers as per the refund amounts over a period of time AI can learn what possible pitfalls can be encountered and how to navigate these. Any faulty decisions taken by AI to be fed back in the database with the root cause of the issues and possible actions to be taken in such an occurrence in future. These faulty decisions involves communicating to the wrong people while within the timelines or not communicating beyond the timelines etc The value addition in this process starts when AI leverages outcomes of various possible scenarios by learning what kind of transactions can be internally handled within the team, which ones need to be escalated to the internal management, which ones to higher leadership and eventually to client. This involves understanding the risks of not highlighting the breaches to the higher management immediately, the timelines to be adhered to in case of transactions that can't be rectified from the supplier's end. While the chances of AI making poor decisions in the initial phases are high, as time progresses it will learn to communicate to the right people and in time to ensure correct resolutions.

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.

As an internal audit function we could use AI in performing the compliance checks as per the contractual agreements, company's standard requirements, regulatory and statutory checks. AI should be able to validate the adherence to these requirements based on the evidences submitted. While checking the compliance to identify a Minor Non conformance and observations as per ISO standards is straight forward, identifying a Major Non conformance is not. This involves identifying the impact of a specific non conformance, it's relevance to the client/supplier's current situation, any financial impact this may have and if any process breakdown happened or if no process has been created in the first place. Most of these scenarios don't have documented evidences as these should be understood post discussion with the relevant stakeholders, approvals from the finance teams etc. So below are the possible scenarios to bring a human into picture. In all scenarios where a contractual breach has happened, there should be a trigger to the Auditor, Auditee requesting any further details or approvals taken to prevent the Major NC In cases where a fraud/financial breach has been identified, there should be a trigger to the stakeholders requesting for the amount of impact, any exceptions taken from the clients, action plans put in place

Tracking of completion of the mandatory trainings by all employees in my organisation can be entrusted to AI as they are completely driven by logic (like when they were last completed, whether an employees passed in the current assessment etc). At this point in time, I think with enough data, AI can enforce any rule or policy in an organization. With minimum data the outcome could be unpredictable and with more data accuracy of AI decisions will improve.

Hello Sir, As an internal lead auditor I need to discuss our audit schedule with the respective stakeholders. We have, on an average 80 audits to be performed every year in my span. I feel creation of this audit schedule is too manual or human as this involves understanding the team's availability (there are many functions like Operations, Quality, Training etc), their client requirements (client deliverables like MBR, QBRs, client visits and client escalations), the closure of the findings from the previous audit. As all these are too dynamic in nature we need to speak with the respective stakeholders which makes this a manual job. As this is a repetitive task, we should find a way to seek AI's help at least to some extent. By feeding the last 3 year's worth of audit plans, previous audit finding closure status we may ask the AI tool to come up with a generic audit plan where the client involvements are minimum, accounts are following a certain pattern and those accounts that have closed a certain % of previous findings. As AI can understand patterns basis the past data, at least 20 to 30% of the manual work can be minimised. Also, AI can be asked to predict how soon some of the processes may be able to close their current open findings basis their past behavior so that we can use these as recommended timelines for the audit plan. Although this can never be 100% foolproof, I think AI can contribute to reduce at least 40% of the effort.