Sunny Prithviraj

The decision making by leaders has always needed data and insights as back up on which the causes and action points hinges upon. The leaders have always wanted to make their decision making faster but not without data and facts. But often they have hit the roadblock because of the inefficiency crept in the systems by virtue of process and technology deficiencies in the system. Usual approach by the leaders have been to focus on ways to improve the process and system through process improvement steps like Lean Six sigma approach and Business process reengineering. These improvement steps have most of the time benefited the process and technology enablement for the leaders to arrive at improvement projects and subsequent steps to improve. The only concern which leaders have around the time it takes to deliver the projects which ranges from months to sometimes years depending on the complexity of the problem statement. Leaders have always been on lookout for tools or methods through which they can work on the decision making tools which will help in the quicker decision making and increase in the Profit in P&L statements of the Company. E.g. in one of the manufacturing assembly line I worked with the COO knew that the quality parameters are in pretty much bad conditions and needed immediate drastic improvement steps but whenever he was chairing HoDs meeting, he used to come across different localized set of data which was being presented and thus leading to contradictory claims and counter claims resulting in no action points of significant impact. The problem statement was overcome by asking all the team members to work on the data cleansing and having a master data sheet by working together so that the data sanctity is not in question. COO was able to take some drastic steps in the quality improvement like implementation of lean and TOC concepts for further improvements which was able to help being down considerably. Now how I see AI as opportunity and how we could have done things differently back then or facing a similar situation, how differently could have approached the problem statement. Now with the agentic AI there could have been three different types of agentic AI which could have been developed and deployed viz. the agentic AI for the data cleansing and standardization, second agentic AI with analyzing the data and bringing in the insights for the subsequent analysis and decision making and third one which could have collaborated with other data tools and would have generated a lot of insights in collaboration with other ERP tools. The insights would have been generated much faster and would have lesser time as compared to conventional approach and would also have had the flexibility to provide details as per the change in requirement by COO or management/HoDs. Now is the question is how to ensure that the insights would have been correct and not wayward. So, by collaborating with the quality team would have set the mean and median in the numbers of say non conformance per unit and other parameters for us to arrive for any deviation in the insights which is being generated. Also, regular analysis of the data and insights should have been carried out by the AI data engineer entrusted with this work. Agentic AI would have been such an enabler for us to move ahead in the lean implementation journey and could have delivered more Profit in the P&L statements of the company at faster pace. 582 Words

Hi Tapati, I think starting point should be the data collection of the QA and QC, checking whether one source of truth for data is there or not? Also, some of the priority action points/inputs can come from the Internal Audit and Statutory Audit observation which will need your and your team member attention, actions. Hope the above inputs will help you. Thank You!

The biasness in any AI model can be attributed to primarily two reasons i.e. either because of the model design or the training data being used is having biasness. An ideal AI model is expected to be working without any biases but the biases can creep in even after the implementation of the model as the data through which it keeps on getting trained can bring on the biasness. Usually the AI agents are being deployed as chat bots in the service industry to free up the manual time being spent by the team members and to reduce the costs. But the responses and reply should be without any biasness. e.g. say there is AI agent which is being deployed to work on behalf of the Program managers for tasks like taking the minutes of meeting, being part of the PI epics rationalization, prioritization of the Epics, stories prioritization, technical resource mapping with the Epics and stories being prioritized. Now, with any of the biasness coming in terms of the Epics preferred for any of particular departments, Epics with less complexity being mapped with any particular team members based on gender will be wrong precedent and not ideal for the Program management or organization. There are two way with which this particular scenario can be addresses one is preventive measures where the data model is being examined at regular time interval (say weekly, bi-weekly, monthly) and then in case of any discrepancy observed, rectification steps being taken. Also, there can be a reactive step in which after observing discrepancies the model is being retrained or corrected. The monitoring work can be handled and handed over to the data professionals for regular audits as legal and compliance is required to be adhered to avoid any penalties or business impact.

AI usages and subsequent AI agents has forced Human resource function and executives to bring in new roles to have continuous monitoring mechanism for the AI agents decision making and output to be surveyed. There are certain cases in which AI agents are required to make decisions based on their learning from the model and data on which they have been trained upon. Think of an AI agents who are customer facing in an manufacturing company which manufactures final product for EPC Companies (which are Clients) and are providing the Clients with the updates on the progress of the their orders and the future dates of Estimated time of Delivery based on model and algorithm fed. Now this Agentic AI's role is very crucial as any biasness being crept in and estimated delivery dates beyond reasonable delay can cause a lot of concern at the Client side and there can be multiple escalations from the Client side. In this case, there has to be nominations of someone from the Company's project team who needs to continuously monitor the expected delivery dates and how the delivery dates are being arrived by the AI agents and also to chekc if the constraints logic being fed at the start of the AI agents are being followed or not? AI agents are expected to be unbiased and free from any misinterpretation of any context so that the results are without any ambiguity. There has to be certain job role which needs to be introduced like AI ethics officer, AI Business process excellence officer, Chief AI officer, AI performance manager etc. who can have SOPs in place for their constant monitoring of the output which is being provided by the Agents. Also, there can be dashboards for the constant monitoring of the data which is being churned out by the AI agents. In the start of the AI agents being deployed in any business processes needs to be continuously monitored for ensuring adherence to model design and to correct any unwanted deviations. 337 Words

The advent of AI has necessitated changes in the business processes. Earlier before the usages of AI in the Business processes, the team used to have standard operating procedures with the SOPs and user manuals having different versions to keep a record keeping of the progress in business solutions designs. For tracking, the versions name has to be defined in the business process flows so that they can be traced anytime and proper nomenclature is being taken by the Machine Algorithm while the upgrade is being done. Also, the AI model can be reversed to any of the previous versions which in case the prompting is not providing the desired results so that the development team can have the time for fixing the bug in prompting. So, while implementing a AI model, the data version control system will need to be deployed in Git based repository. The data version controller will be used for the continuous integration and continuous delivery of the development. Tools will be further used to have the metadata on the testing of the newly developed model as part of revision upgrade. Hyperparameters and metrics (as defined while defining the AI model) will be used to track the experimentation and development cycle for the upgrade in versions. Document versioning activity either can be automated with codes being written so that the traceability of the different versions can be available both at the development and post deployment. Also, the Business excellence needs to document and provide a chronology of different versions for the Internal Audit and Statutory Audits.

Now with the advent of AI technology and its usages there has to be changes in the business processes. We will have to compare how any new implementation used to happen without the AI features. In most of the cases the end users used to know of the business processes and in some of the cases the digitization of the business process used to happen or automation the Business end users used to be aware of the process and then after implementation steps i.e. hyper care, whom to report the bugs to, UAT loops, also getting the know how of the low code and no code tools, implementation to take care of minor developments. Now, with the altogether new technology which has bene introduced and is being used in the all the Business processes is AI and this is a challenging upskilling for all the Business processes end users as this has lot of new technology and in order for the team member to keep on training the model, checking on whether the biasness has crept in the model or not, Data ownership policies, steps to identify the drift in the model, Compliance check by the team to ensure that the standards adherence is in place, frequent data model checks and fixing the model (in case it is required) for maintaining the accuracy of the models. There can be multiple approaches to the problem statement of maintaining of the AI related process handover from the development team to Business/Operations team. First option is to hire someone in the team having the know how of the AI world and should be able to handle the post development, system maintenance works. Second option is to have the revisit of the job description as a lot of traditional business processes gets changed because of the AI features being introduced and the current team members who are doing their business process end to end are being trained as part of upskilling and being introduced to AI related skills so that the same team member can take up the post deployment work of AI maintenance and upgradation. The benefit of the second option is that in house team member can be the pioneer as AI champion driving other AI projects and helping other business processes too.

AI agents have been able to revolutionize the business with helping the business team with the usages of AI and bringing in a lot of efficiency. While building the AI agents, there are several methods to train the AI agents through the historical datasets, supervised learning through Natural language processing (NLP) and unsupervised learning by the AI agents on their own trying to find a pattern and gaining knowledge from the information available from the datasets. AI agents needing the continuous learning feedback is specific to the Business case or processes where it is being intended to work in i.e. whether there is a API based agents linking the multiple ERPs, CRM tools together in data analytics tool, or AI agent deployed for interacting with customer agents and working as chatbot etc. In our case, we had worked on deploying the AI agents for the advanced planning and scheduling which before introducing the AI agents were being worked out manually through the excel based working where the data dimensions like the manpower availability, machine availability, machine breakdown reports, customer requirement date (i.e. Sales order date) were all maintained manually and fetched by planner from all the individually maintained datasets and then the scenario and constraint based planning was being done. Now the previous historical datasets related to the constraints based planning and scheduling was being provided to perform the supervised learning and then the unsupervised learning aspects were also checked. After multiple testing cycle, the AI agents were deployed and we were able to achieve good accuracy but the AI agents in this case needed to perform the continuous learning feedback to to improve as there are multiple new constraints arising because of the change in the business scenario were coming up like the introduction of new product required the planning to be kept in mind that there will be a changeover time for the setting up of the jigs and fixtures and machines. Also, the AI agents needed to learn the continuous unsupervised learning through the cues like if the planner is not completing the advanced planning and scheduling even after running multiple simulations that might mean there are certain requirement which the planner is looking to achieve and not being able to get from the planning and scheduling. In this case the AI agents needs to learn through natural language processing and considering these as feedback for further improvement. Also, while planning and learning from continuous feedback, the AI agents needs to mimic the human behavior through the NLP. AI agents needs to use the machine learning to work on incorporating the inputs received through the NLP and customer/users feedback and next action by the AI agents is to interpret the feedback and self improve the response (in this case to improve the planning and scheduling of the machine usages). AI gents needs to find the pattern in the information provided by the usages of NLP and process it further. There can be instances like addition of new ERP system and AI agents requiring the access connect with the new ERP system introduced to being in the Client feedback on the order delivery date and Advanced planning and scheduling requiring to re run the simulations to achieve the optimal planning to integrate real time Client feedback. While we have introduced the AI agent based Advanced planning and scheduling in our machining operation process flow diagram and have replaced the manual planning with the AI agents based Planning. Still, we are looking ahead to provide the supervised learning and monitor the plans prepared through its own learning and usages of the NLP, Machine learning to improve upon the planning capabilities. We are hopeful to achieve the maximum performance through the AI usages of AI agents in the day to day operations.

The AI learning is very important in the changing landscape for all the team members as the management and Companies are gearing up to introduce the AI tools into their day to day Business processes. The clarity with which the prompts are provided will be resulting in the more accurate answers from the systems. The prompts need not be cluttered with too many queries clubbed together in one prompt as the AI models are defined and designed in a certain way to provide answers to queries through a prompt. For example, say there is being AI used in a CRM process where team member is working on the lead generation and there are historical data available related to the leads which came and processed and then the leads which got generated through the leads. The prompt provided by the Sales team member is as below: "Find out similar timeline lead with similar value and similar OEM from where the enquiries got generated and got converted to Order and received the Sales Order" Now the intent of the team member is to arrive at Final answer where the team member wants to know about the below and the prompts has to be sequential with more specific queries and should have been prompted as below: First Prompt i) Can you find a Lead from the OEM (name i.e. say Siemens, as there can be multiple OEMs i.e. Siemens, GE, Fanuk etc.) in the past? Answer: Below mentioned Leads are from Siemens Siemens - 2023 - 200 Cr. - 2 Years Siemens - 2020 - 100 Cr. - 5 Years Siemens - 2021 - 50 Cr. - 2 Years Siemens - 2022 - 75 Cr. - 3 Years Siemens - 2023 - 100 Cr. - 6 Years Siemens - 1998 - 100 Cr. - 7 Years Siemens - 2020 - 100 Cr. - 8 Years Siemens - 2023 - 200 Cr. - 2.5 Years Siemens - 2023 - 200 Cr. - 2.5 Years Second Prompt ii) Can you provide Lead with similar value? Siemens - 2023 - 200 Cr. - 2 Years Siemens - 2023 - 200 Cr. - 2.5 Years Siemens - 2023 - 200 Cr. - 2.5 Years Third Prompt iii) Can you provide lead with similar timeline for the completion? Siemens - 2023 - 200 Cr. - 2.5 Years Siemens - 2023 - 200 Cr. - 2.5 Years With the effective use of the clear, precise, sequential and specific prompt the right answers can be obtained from the AI model. The right training and the with the practice the team members can get the right answer from the right prompt. Prompt engineering needs to be learnt by the team members in order to use the AI to the fullest.

With the advent of any new processes, technologies there has to be change in the Business process flow. Impact of the AI introduction in the Business process will vary depending on how much changes have been brought in the As-Is process in terms of the role change, job description etc. So, while working in the Program management team, our first encounter with the AI related term or development was through the introduction of Notemaker powered by Gemini (G-suite). Earlier before the addition/introduction of AI, the Program managers were tasked with leading the meeting discussions and at same time keeping a tab with the Minutes of meeting and then ensuring that we are sending the reminders, doing follow ups with the swim lane team members and ensuring that the actions are closed. Before the introduction of the AI in our Program management team there were primarily below roles of Program managers: 1. Collection of requirements i.e. Epics 2. Rationalization of the Epics with Product managers 3. Daily sprints and sprint retros review 4. MVP product demos 5. Program Increment review With the introduction of AI in G suite i.e. Gemini and Atlassian Intelligence (JIRA) there was considerable change in the Job description for us as Program managers as the AI team members were able to keep note of the meetings during the PI planning, daily sprints, sprint retrospective and then the Stories and Epics progress. So, the role of Program managers shifted from facilitator to being more inclusive in the problem understanding of the subject and then being part of the decision making for the completion of the tasks. The time which was earlier spent on action tracking and monitoring of the actions for closure shifted to being able to spend time on the creativity, strategy, Risks and focussing on the risks mitigations. We were able to focus on derisking the negative risks and working on the positive risks. Job description changes were brought in by the Human resource department to document the changes in the job role as there were considerable changes in the job description and subsequently the changes needed to be done in the Goal settings, balance scorecard for the performance appraisal. The Process Excellence team also needed to update the SOPs and Process flow diagram to adopt the changes which were brought in because of the introduction of AI and changes in the role descriptions of the team members. The roles of say Program managers, Human resource and Process Excellence and other team members (part of Swimlane) changed to more strategic thinking and action oriented from the previous one operational activities and challenges to being part of the creative and strategic contributor. So, the introduction of the AI and job description changes have been a boon for many as this has resulted in the skill development and skill enhancement of many of the team members. In certain other cases there are redundancy of certain roles and new role requirements needed to be created like the Business Analyst, Financial Analysts and other roles no longer required and in place of that someone from the Financial Planning and Analysis needed to be aware of the AI usages for the analysis part of the work. HR needed to work on the Job description for most of the role and this was a larger project where certain roles become redundant and certain roles needed to have job enlargement.

Now a days with the introduction of the Artificial Intelligence in Business processes has made the need of the Audit for the AI infused process more and more relevant. The significance of Audit in the Business processes has always been there as the adherence to the standard operating process in normal audit process is checked and this is the way Organizations re able to maintain the standardization in the process. The problem came when we introduced the Generative AI in our existing Business process of Sales and Marketing where we had aimed to utilize the AI abilities to read through multiple previous executed RFQ and RFPs so that the previous data and pricing points can be utilized. When we had onboarded the Customer relationship management tool, Our Sales & marketing team had specific need to make the process for RFP submission faster and immediate need to include the Agentic AI capabilities of the tool to help them with identify past data points from any of the submitted RFPs and to help them analyze the failure point (of not receiving the order from client as winning bid). Also, Gen AI was supposed to help the design team in reading through the specification documents and help in identifying the Client requirements so that design team can also ascertain the dimensions and hence the materials requirement for the SCM team to arrive at the BOM quantity and price. The Audit team had this task of revisiting their Audit approach in which they had traditionally attempted the Audit processes of identification of the SOPs and then validating through random data points and requesting for the validating documentations. The Audit team had to first understand the AI working, how does a AI model is prepared, its testing procedure and then validation procedure before which the AI capabilities are ready to be used by different users and then the revisited audit process of the Sales and Marketing business process enabled with AI capabilities needed to be revisited. The Audit team was able to come up with the revised Audit process in which it had split its scope in four different categories: 1. Business process without AI 2. Business process with AI 3. AI process/suggestions/prompt 4. AI model and data source build ups 1. The First category was being audited with the help of manuals and SOPs. 2. The second category needed to test the reply suggested by AI for the prompts which were asked by the team members. e.g. The prompt entered by team members like 'Suggest me similar type of RFP submitted earlier with profit margin less than 5% in the total bid of 50 lakhs?' The Audit team needed to check the Reponses suggested by the AI tool with the database and cross check and verify if the AI reposes are in line with the database and providing the same reply which could have been provided otherwise by manual searches (may be time taken would have been a tad longer ~10x). 3. Audit team also used their own prompts to check on the responses and used to validate wrt the standard documents, organizations guidelines, SOPs, user manuals, Companies Policy etc. 4. The most important parameters of testing needed to check the AI model and data sources and data validation, biasness in the system and AI ethical practices. Audit had taken help of Chief Information officer to identify the right person from Information system who could validate the AI model as to how the model has been structured, has the model been in built with certain biases in terms of socio political Reponses to be filtered out as per the country of jurisdiction or is independent, whether the responses are biased or not and was providing scores (scoring methodology already defined) and then the team used to arrive at the individual rating for the four sub processes and then the final score of the Audit process. Based on the Audit score there were three categorize of satisfied, needs improvement and needs immediate attention and improvement. All the recommendations by the Audit team were explained in details so that the overall process adherence remains high across organizations including AI infused processes. The Audit team was confident that even though they have put in a place a process to Audit the AI infused process but still this will be a continuous improvement process as the AI model itself keeps on evolving through various developments day by day.

Change management is indeed one of the key factors in deciding whether the team is able to succeed in implementing the DMAIC Projects. MBBs are the key in helping the team to be able to Define the problem statement. In one of our Projects in my previous organization where there was a strategic ask from Customer to ramp up the production 2x and we knew of the problem statements which we have work upon because of multiple Non conformances, Internal and Statutory Audits pointing out the action points which we have to work upon. The problem in front of all of us was we knew of the Problem statement, final goal and the Tool (in this case Lean Production implementation) but how to start and which all KPIs to be tracked for achieving the End Goal? This is the place where we had approached MBB (external consultant) and the MBB consultant conducted workshops with Head of the Department and Black belt holders and was able to guide in defining the Problem statement, Project Charter and arrive at KPIs viz. the Throughput, Quality (Non Conformance per unit), Lead Time and Man Hours spent. One thing which was highlighted from the workshop was the Change management which needs to be driven in the Organization for implementing the lean project. We were able to focus on improvement actions in order to achieve the target KPIs which was set in for achieving the 2x ask in the throughput by the Client. We selected the most critical work station and did the value stream mapping to identify the bottleneck points and the reason for the same. MBB was able to steer the conversation and discussion with Head of the Departments from the ask of 2x output is impossible to lean driven approach in which the problems were identified through VSM and voice of the customer i.e. different cross functional teams like (SCM, Production, Quality Control, Production Planning and Control, Manufacturing Engineering). Next step was most crucial step of Measure where we had special focus on arriving at the right data set as the whole exercise is dependent to have one source of truth for the data points and then able to analyze and figure out the pain points where the focus was needed to be done. Our special focus was to cross check and verify multiple versions of the data which was available as localized data and which needed to be cross checked to have One common data sheet to initiate the discussion amongst the cross functional team which was to be acted upon as baseline for measuring the impact which the Project actions were to have for us to achieve the defined target numbers in the KPIs. MBB had played an important role in the measure phase as able to convince the heads on putting in man effort to go through the historical data which was being recorded by the team as the team members felt that questions were being raised on the way they were carrying out the work and had apprehensions that there can be some actions against them if there are discrepancies which will be found out in the reported and actual data. MBB had to play an important role in the next phase of Analyze too as most of the team members (including Heads) had a understanding that they are Subject matter experts as they are executing their work day in day out. The MBB was able to have shown the right approach of arriving at the root cause of any problem statement. MBB had guided with one problem statement where the non conformance were being reported to be too much related to Foreign Object Debris and the team was convinced at the start that the problem is with approach from one of the team members. But with tools suggested by MBB i.e. the Fish bone diagram and 5 Whys we were able to report the Standard Operating procedure were not properly defined for the operators (details like what all tools to be carried while inspecting the unit at final assembly point). So, the SOP was revisited and that is how the team was convinced on the change in approach to look towards any problem to find out the root cause. MBB had highlighted that the biggest change management is to be done for the Improve phase in the lean project. The team had initiated the Pilot project for the most critical work station in which the team members were to be cross trained in other functional area for them to become self dependent in taking the key decision which required medium expertise in the respective domain (e.g. a Production lead used to wait for the Quality team members for them to check the measurement of the project as per the dimensions called out in the standards. After cross functional training and passing the evaluation test (to test the understanding the new function area), the same Production lead were expected to get the work done from the production team and then able to test on their own based on the training received. This require a massive change in approach by the team members to come out of their comfort zone and challenges themselves with new learnings and its correct applications. MBB had highlighted that the most important step in the DMAIC cycle is to able to sustain the changes which have been implemented and also monitoring the progress of the changes which were brought in. In this particular case, the progress of the critical workstation was being monitored on the non conformances, lead time, throughput, regular assessment and then management monitoring through the defined KPIs. I have experienced that the MBB has to be involved a mentor to be able to bring the change as adopting any new approach and too withe a critical approach faces resistance because of the inertia which creeps in the way team work and have to guide the team throughout the DMAIC process.