vijay gonsalves

Previous Stage – Pre AI deployment In a BPO payment processing process the Older KPIs were defined as per listed below. The assignment was done manually by allocating Invoices and processers used to issue the payment by validating the information manually and updating the information in the system. The performance was evaluated basis below listed KPIs with weightages · Number of payments issued (35% weightage) · Quality achieved (No. of errors received on the processed payment) (45% weightage) · Innovation (If any idea or kaizen implemented in the process resulting in efficiency gain or quality improvement) (10% weightage) · Professionalism which included planned leaves, Collaboration with the teams, helping team in completing the numbers (Overtime) etc. (10% weightage) Post doing R&D the management decided to implement a BOT for issuing the payments. The BOT would issue payments up to a specific threshold e.g.$ 2500 without anyone’s intervention or approval (The reason for determining this threshold was basis the past historical data where in 60% of invoices received for payments were within the threshold of $2500. However anything above this threshold would come to humans for intervention, they would validate and route the payment back to BOT for issuing the payment. Post AI Deployment Considering the automation of payments i.e. (60%) the productivity metric will need to be revised, and this will further have a domino effect on the quality metric too as majority of the payments are issued automatically. Considering these facts the metrics should be changed as per below. · Number of payments reviewed correctly and responded back to BOT timely within the TAT (The 40% payments coming for human review (Revised weightage 20%) · Creating a dashboard showing the comparison, of No. of payments issued by BOT vs human review, Incorrect recommendations provided by AI, Overriding done on the incorrect recommendations along with analysis done on fixing the issue (5% weightage) · Quality achieved (This would relate to No. of overrides done on the AI’s recommendations which was provided incorrectly by AI (Revised weightage 40 + 10% Total 50%). 10% incentive to be provided on the humans flagging off incorrect information provided by the AI · Innovation (If any idea or kaizen implemented in the process resulting in efficiency gain or quality improvement) (weightage 10%) – This would remain as it because continuous improvement is a very important metric and can be used to enhance the AI’s recommendations and efficiency further · Professionalism which included planned leaves, Collaboration with the teams, helping team in completing the numbers etc (OT). (5%). OT and leaves will not be a problem as majority of the payments are issued by BOT hence revising the weightage for this parameter · Handling exceptions: Resolving cases which are bit ambiguous and AI cannot classify (5% weightage) Along with the above defined metrics for KPIs there needs to be some behaviours that needs to be defined (Both encouraging and preventing) with a weightage of 5%. Below listed are the few behaviours. Behaviours that need to be encouraged Validating and documenting the recommendations provided by AI Using AI to reduce the workload which has repetitive and redundant steps but simultaneously reviewing it Documenting the exact reason whenever manual intervention is done Humans should take accountability and ownership of the outcomes even though AI is providing automated decisions Unintended behaviours to prevent Blindly following the AI’s recommendation and not reviewing it with human intel and logic Ignoring or overlooking compliance and regulatory standards Not documenting reasons for overriding AI recommendations Blaming AI for providing incorrect information

It is very critical and important to understand that Al doesn't think the way a human does. Al is like an automatic machine which is programmed, and its objective is to check thousands of files or activities faster and then provide the output the way it is programmed making it easier and convenient and enhancing the capability and efficiency quickly. Al is basically cross checking its own memory and functions in a way that are based on pre-defined rules and the way it is trained and programmed. This activity adds value, however if the rules are mis-configured or updated incorrectly it can have a severe damage on the business and company's reputation hence Al should be used to gain efficiency, but it should not replace human judgement. Real Scenario in my office - I would want to highlight one scenario specific for a chat process, Due to state regulatory compliance specific for one of the states in US ex. XYZ state, The medical information of the patient was not supposed to be provided due to HIPPA compliance. The BOT was programmed in such a way that it should route those chats to a service representative and in return the service rep. will assist by providing the information only if a signed authorization letter was available from the patient to disclose the medical records. A new update was received and the BOT was supposed to be programmed accordingly for a different state ex. ABC state, however the same rule was applied and programmed to process XYZ erroneously due to which the BOT started providing medical information of the patient which was incorrect and non-compliant further prone to fines and penalties along with a possible litigation when the patient finds that their medical information is provided without their consent. As part of the routine auditing work, QA picked up some random samples and was able to see the variance and highlighted it post which the concerned team was informed about the issue and the BOT was reprogrammed and overwritten as earlier Al recommendations and outputs need to be trusted where there are low risk scenarios and rules are clear; outcomes are predictable and no ambiguities and this should match with historical patterns. We need to be very mindful in maintaining the right balance between trusting the Al outcomes and have a understanding on when to intervene and this would require good judgement and strong risk governance. In the above scenario, the high-risk actions should require human review, and overrides should be monitored continuously to help improve the system and the output provided to be accurate. Al needs to operate as a help in making decision support and not a decision authority Cues, safeguards or thumb rules needed to maintain the right balance 1) The level of trust needs to be aligned with tasks that are simple and can easily rely on automation 2) As mentioned above on the scenario pertaining to state regulatory compliance. The tasks should be routed for a human review considering the severity and impact of these errors 3) Regular spot checks, targeted audits, sample checks need to be done to check if the rules are operating correctly as programmed As a thumb rule, it should be followed that the simple, repetitive tasks with clearly defined rules and lesser error impact can be automated using Al. However, errors pertaining to high impact and business reputational harm must remain under human control. This way Al remains an important support mechanism and tool without compromising compliance, trust or responsibility.

AI can help us in identifying patterns, trends and analysis however DMAIC still stands strong and helps the improvement idea more structured and converts insights into meaningful solutions Ex: In Claims processing, the issue is related to turn around time, and the primary reason is missing documents. We might use AI to help us identify transactions with missing documents however when we take this idea forward using the DMAIC methodology it becomes imperative to verify and understand the data as to why the documents are missing (Root cause analysis). Post identifying the root cause simple controls and preventive measures can be applied by the project leader to avoid further misses. This simple example reflects how AI and Humans can work hand in hand. I would want to propose and improvement idea along with AI as per below Problem statement: The processing time for Auto claims is taking more time i.e. 72 hrs. Goal Statement : Reduce the TAT from 72 Hrs. to 24 Hrs. Define Phase : The define phase is totally dependent on humans. The problem is defined basis the customer complaints received, SLA misses, Escalations, VOC. It is also very important that the project sponsor is aligned with the idea and approves it Measure Phase : In the measure phase, we can use AI to help pull data to track cycle time, rework if any, SOP gaps and miscommunication that is hands off between the teams. It also helps in measuring the data using statistical analysis. Human intervention is required to understand the cause and effects ex. Pareto analysis Analyze Phase: AI can help us in scanning large volumes and highlight hidden process bottlenecks, delays, Outliars, Duplicate entries, Missing documents and trends that is time consuming to analyze manually. However human intervention and judgement is still needed to understand compliance and regulatory issues, Policy requirements and real operational issues Improve Phase: In the improve phase the AI can help us in testing different scenarios and providing inputs on what could be the best possible solution for us to apply. It also helps in predicting impact of the proposed changes and also helps us in providing inputs towards robust workflows. The solutions provided still needs to be validated by the human to check the feasibility of implementing the solutions in conjunction with the budget approval, effort required etc. Control Phase : AI can help us in monitoring process performance through dashboards, reports and flagging early triggers of delay. It also helps us in providing timely updates and alerts if the controls are not working so that it can be fixed immediately. However, we would still need human intervention on periodic reviews and check on the controls applied to ensure the controls are running smoothly. Overall in DMAIC - AI helps in improving speed, automation, accuracy and visibility while human judgements are required in areas related to Finance, compliance, underwriting and decisions where risk is involved and impact on the customers

The first step is leaders should lead by example and by learning and understanding what artificial intelligence is and focus on mainly learning the prompts that needs to be provided to get a much more accurate output. It should be used in conjunction with human logic and experience. While, Using AI should enhance us with improving the productivity and reduce manual intervention, it should be used carefully by not providing confidential information which might impact the business and company and causing legal issues. AIs should be used by leaders for analyzing date, Automating, reports and drafting emails which can save significant time and the trends can help in making data driven outcomes and solutions. The primary use of AI should be for automating repetitive and redundant tasks and help in decision making. One important thing to be followed by the leaders is to inform their team members about data privacy and use the AI responsibly, Educate team about the impact of misusing sensitive data. All the judgements should not be easily accepted provided by AI but making a conscious and cognizant effort to validate the data before using it. leadership practice proposed - As part of the quality team. I asked my team to do a value stream mapping activity because the company in which I work has lot of smaller sub processes doing the same type of repetitive work. While, capturing the data in the old traditional method in excel was too time consuming and was not user friendly. I used AI and fetched an automated report where in the team members just need to add the description in the excel with the steps and mention VA and NVA along with the minutes, Automatically the report will calculate the PCT/TAKT time, VA/NVA and provides a detailed summary along with the time spent in VA and NVA activities additionally providing the AS is and TO be process map. This has helped the entire department impacting 500 QAs.