Anchal Parashar

With the evolution of human race, Self prioritization tactics and techniques have also evolved manifold. In earlier times it was often used to align with a concept, which was later coined as Darwin Theory of Survival OR Survival of the Fittest. It was first drafted keeping living organisms at center of it and various skills they require to prove their credibility within a ecosystem for superior sustainability. As time passed by, human knowledge, emotions, intellect and this rigor for sustainability grew too and various forms and shapes, which includes resorting to negative and positive means and ways. Till the time positive, meaningful and logical routes are used, it gives proportionate results. But resorting to negative, deceitful and maligning routes, leads to dis proportionate outcomes. Above analogy hold true not only for a household, a class of kindergarten kids, a highly acclaimed academic institution, a highly competitive sports arena but also in the immensely competitive corporate culture too, wherein sustainability depends on the ability of an individual to convince oneself that he is apt to survive in any situation. Adherence to the above tactic, at times results to a phenomenon called as SELF - SERVING BIAS, wherein, a person, for any positive outcome of an activity, takes the credit himself BUT if things go negative, he makes others factors responsible for it, with zero acceptance of his failure. In a typical DMAIC project, wherein decisions are taken basis the data, presence of Self-Service Bias, may lead to major blunders, potentially impacting the outcome of the project. Impact of Self - Service Bias in RCA – - It could result in un-uniformed causes identification, based on self-assurance thoughts of a person or group of persons - Unidirectional brainstorming sessions, led by team members who have a say in the system either because of experience or superiority - Probability of losing on insights through data is more, as decisions are more based on self-conviction - Reliability of identified root causes is less, as they are based on limited cognitive thinking - High solutioning cost, as often they are developed basis output collected from few individuals and not the entire group - Low sustainability of solutions developed to mitigate root causes, as they are result of ‘bracketed cognitive thinking’ Avoiding Self Service Bias – - Building and sustaining healthy corporate culture, wherein employees doesn’t feel ‘judged’ if a failure occurs and for successes credit goes to the entire team - Coaching people on how making mistakes and learning from them is actually a sign of strength - Acceptance of certain degree of Self - service bias, so that correct cautions can be bought in - Before any RCA session, letting members know, the expectations from outcome and the objective - Rewards and recognition for the team members who are most active in challenging the status quo, so as to motivate them for logical decisions - Striving for Data Driven Analysis, instead of opinionated ones

AI Implementation for any organization is seen as a critical part of business strategy in todays world. With plethora of activities happening around this buzzword ‘AI’, it quite easy for any Strategic Manager to get completely soaked and fall prey to current AI glare, without giving a serious thought to the short and long term value creation through it. It is of pertinent importance that to reap the real sustainable benefits from AI usage, organizations should weigh all possible factors, that can make AI implementation successful. AI strategy has to be long term in every sense as it takes time to integrate existing systems and create new ecosystem. But, there are numerous short term benefits too. Creating balance between these two is what should be focused on. Key Factors to be considered for AI strategy ROI Short Term Value Creating Factors – - % of routine repetitive tasks that can be automated via AI, leading to cost and efforts reduction - Identification of Quick to harness workforce who can easily adopt to AI method and practices basis their current expertise proximity to AI, this creating ready resources for future AI expansion - Facets of business handling large data manually can be processed through AI, hence minimizing the risk of errors - Small systems/processes already standardized can be synced with AI tools to make delivery faster and smoother - Better utilization of time made available due to AI usage on more critical and strategic activities Long Term Value Creating Factors – - Change in organization DNA in a slow and steady manner, with workforce becoming more aligned with usage of new ways of working - Thought process shift from basic to data driven decision making, thus helping in well calculated decisions - Innovation in Product & Service Life Cycle by using emerging AI tools - Competitive Robustness for better market sustainability - Faster business propagation and diversification by using proven AI tools

Hyper automation – As the term itself suggests to be an amalgamation of two words – Hyper & Automation, which basically indicates automation being done at its full force, negating OR completing minimizing the need of any human intervention to get a task performed. Its usually used for businesses or processes, wherein variety of applications or systems have to co-ordinate with one another to produce a desired outcome. These ecosystems are quite complex in nature and constant human eye checks are required to operate to generate a synchronized delivery. As the efforts are towards reducing the needs of human intellect and precision, AI & ML powered applications and systems are generally used as the base for hyper automation strategy. Large Language models (LLM), are designed and deployed to replicate the human decision making to run projects/businesses by synchronizing the communications amongst all the processes involved. Most important facet of Hyper automation is, it helps in integrating the processes working in isolation and thus creating a more intelligent and holistic enterprise level systems. Hyper automation & RPA – Robotic Process Automation (RPA) is a unique approach of automating the repetitive tasks by ‘mimicking’ human actions, which are performed otherwise. It can be said that RPA is a single technology with benefits being delivered in pockets within enterprise while hyper automation is a bouquet of multiple such technologies with enterprise-wide benefits. Usage and Benefits - RPA has a major challenge of being used for ‘Structured Data Set’ only. This functional limitation of RPA can easily be controlled by using AI powered tools and applications, which can work, both on structured and unstructured data. RPA strategy lacks ‘cognitive thinking capability’ and Hyper automation is based on LLM, through which tools and applications can perform cognitively. Hyper automation increases the ambit of automation strategy of organization unlike RPA which functions in isolation or pockets of business. RPA can only perform tasks based on set rules and thus needs to mimic them whereas Hyper automation uses AI &ML, Orchestration and workflow tools (PEGA, Appian,XTRAC) to perform tasks as efficiently as humans with decision making capabilities. Hyper automation is now widely being used in industries wherein large datasets have to be handled and scope of committing an error are minimal. Some of the industries wherein hyper automation is being practiced are Automobile, Retail, Healthcare, Banking and Financial Services.

Conformity Bias is a phenomenon of syncing of our own thoughts and behavior with that of a bigger team or group, to make a decision. It normally occurs when one is influenced too much by the group, due to variety of reasons. While handling or managing projects, unsolicited presence of Conformity Bias can lead to value seepage, if not identified and managed properly. For Example, in a Stocks and Trading company, wherein a large clientele is handled, and funds are supposed to be segregated, suggested and allocated, basis some market analysis, which has to be independently performed by the form itself. If this firm starts allocation of funds basis the way its competitors are doing, there is high probability of failure. Though it may turn out in either way also. But eventually, this will lead to loss in firms' market credibility and sustainability, as it is not taking informed and data driven decisions. For Project Managers, its very important to have sustainable guardrails to be placed to keep a check on Conformity Bias. Few things which can be considered while doing so are, - Artefact Collection: Gather relevant artefacts which can support your decision. Don't go with what is being heard. It has to supported with hard evidence - Open-mindedness: Always keep your mind receptive to new thoughts and ideas, as this will help you in creating a concrete roadblock against Conformity Bias - Study Conflicting Views: Collect and analyze conflicting arguments then the most obvious one resulting out of Confirmit Bias. It assists in clearing the mental fog and will help in selecting the right path of informed decision making

Persona Profiling - It’s a process of demarcating and segregating customers basis their behavioral pattern, which is created basis studies and data collected from market. It helps organizations to devise their customer reach strategy by aligning it as per the customer requirements. It could include gathering information on customer demographics, intent, issues, expectations etc, but needs to be supported by concrete data evidence. In the DMAIC approach, it is prominently used during the Define and Measure phase, wherein VOC is being analyzed and studied. By creating Customer Persona's its becomes easy to assign a particular data output with a relevant persona created. It helps in better identification and mitigation of any issues which concerning customer behavior. Study of Customer personas prepare the organizations to be flexible in approach as per the requirements collected and refine output accordingly. Some of the examples of Customer Persons mappings could be, - Mutual Funds providers have various categories of funds like Higher Risks, Medium Risk and Low Risk Funds, basis investors Risk Taking appetite - Promotional Activities are planned as per the demographic segregation basis spending pattern for a particular location, thus saving on the cost incurred in advertisements

In generic terms, justification of the factors which lead to a purchase decision for certain product or service, but found to be untrue later, can be bracketed as 'Post Purchase rationalization' phenomenon or behavior. It triggers because the Purchaser or Customer try to prove the correctness of their decision, no matter, how illogical it may sound in reality. Every effort is made to ignore the actual positive aspects for the service and appreciate the aspects basis which decision has been made for purchase This phenomenon greatly impacts the Customer Satisfaction Metrics like C-SAT & NPS. Data collected from the customers for aforementioned metrics won't be accurate, as it is shared with an intent of proving their decision. And when such data is led down to draw a histogram basis the responses collected, it would be more positively skewed, which may lead to flawed business strategy making for the entity collecting the data. Also a ripple effect would be felt on the entire supply chain !!! Various logical methods can be deployed to counter Post Purchase Rationalization effects on Customer Metrics as listed below, - Time of Data Collection : Avoid collecting data during festive or holiday season, as purchases made during this time could be of impulsive nature, which means customer may not weigh various factors of product while making a purchase - Segregated Data Collection: Collect data at equal time intervals during a particular cycle. This will help in touching all purchasing behavioral patterns which varies with time - Improvised Customer Connects - Deploy structured ways to reach to customers on regular basis to make them understand about the product or service, which will help them to make informed decisions, which will eventually enhance the data quality at the time for actual collection

It’s a general human nature to give prominence and importance to events, tasks or issues which resonates with a particular thought process or hypothesis and are very widely published for consumption. This generic human thinking phenomenon often leads to mis - calculated decisions, as they are made, basis information which is readily available but often negates those bits and pieces of information thread, which gets worn out during the process and thus are non-existent before an outcome is delivered. Thus, leading to decisions based on the information which 'survived' the process, and their existence becomes prominent. Hence the term 'Survivorship Bias'. Business Processes comprises of various resources which can be broadly categorized as Man, Machine, Material, Methods and Measurements. 'Survivorship Bias' phenomenon can easily impact decisions based around these resources or process as a whole, if expert governance is not present. For Example - If a 'Selection Process' is followed to hire a Project Team with specific skillset, with a target of 10% Hiring Conversion rate (People targeted to be hired for Project Team from the number of applications received). Once Selection process is over and results are analyzed, its identified that only 8% applicants have successfully passed it and can be considered for the role. Obvious inference which can be drawn from this, - 'APPLICANTS WERE NOT OF REQUIRED QUALITY WHICH LEAD TO 2% LESS HIRING'. But something which is overlooked is 'WERE THE STANDARD OF THE SELECTIONS PROCESS TOO TOUGH TO BE FULLFILLED BY EVEN THE WELL SKILLED APPLICANTS'. This Survivorship Bias phenomenon played its role!!! Whenever process is being governed on various tangents, every effort should be made to make conscious and well-informed decisions by erecting necessary guardrails against Survivorship Bias. Some basis pre checks which can be performed to avoid survivorship bias are - Try to collect every possible data to be subjected to analysis - Stringent controls should be placed to check the data for any bias's - Brainstorm to identify any data which could have been missed in the process of collection, if yes, collect it - Deploy Data Experts and tenured resources from the process for Data collection as they have more insights on the possible data seepage - If possible and applicable, put bots in place to collect datasets for study - Use Generative AI platforms for more well-informed decisions

Generative AI emerged as a concept and technology which can be easily assessed and leveraged by an average human. With its capability to provide first hand responses to various queries in the form of texts, images, audio, video and content creation, it has undoubtedly helped in minimizing the human efforts and that too with ease of usage. Generative AI & Lean Six Sigma(LSS) - LSS initiatives are used for bringing in improvement in either existing processes (DMAIC) or when a new process or service is being set up (DFSS or DMADV). Its easy to accept the fact that most processes involve repetitive activities, which regularly undergoes changes in an efforts to continuously evolve for betterment in operational effectiveness. To monitor, govern and communicate these changes, does requires lot of human efforts. G-AI has helped LSS domain a lot, wherein by leveraging on its tools, cost and efforts involved in deploying and managing these changes becomes more profitable, from business and customer perspective. Following example would make the above statement more clearer. In DMAIC approach, each phase follows certain specific steps, and AI can be used to optimize those steps to save time and efforts and generate a more sustainable output. DEFINE and Usage of AI - When SIPOC is being created, existing digital records (Various IT and ERP Systems)for a process/org can be used to map the Supplier, Inputs, Process and Output. MEASURE & Usage of AI - To map the current state of the process, data captured via digital tools already existing the system can be captured and be leveraged upon. By using open AI chat bots, best industry benchmark can be searched and be used for baselining the process performance ANALYSE & Usage of AI - AI is based on large algorithms, which can process data and can give quick and best output to identify the various logical roots causes to be looked at. Available datasets can be subjected to any AI tool like OpenAI or ChatGPT to provide best root causes for a problem. Moreover, these AI Tools can also create various data Models and give us an opportunity to select the most suitable one for the study IMPROVE & Usage of AI - In the solutioning stage, available AI tools can give us the multiple options to pick a solution. It depends on the expertise of the LSS professional which tool to be picked up for implementation basis existing business scenarios CONTROL & Usage of AI - An effective AI Bot can be used to monitor the performance of the solution under the Control Limits. These BOTS can raise ALARMS & NOTIFICATIONS, if process goes beyond permissible limits. Its important to understand that AI tools work on certain triggers or prompts given to them and quality of output is directly proportional to the quality of prompts given. Business Excellence professionals can always use AI and contribute in making it more effective. Both domains can reap the best outcomes with collaboration!!!

Audit By Design - It’s a practice or mechanisum deployed in or across organisational process/ es, which enables the system to give real time comparison of the various performing KPI's, thus , minimizing or negating the requirement of periodic reviews to ascertain the flaws or defects in a system. All the artefacts pertaining to each and every step in the process gets recorded and traces are left at the right junctions to be picked for auditing in real time. In other words, Audit by Design phenomenon, helps processes to be self-sufficient 'structurally' , to give real time movement of various business KPI's, which enables the system to make relevant corrective measures, if required. Example - In a typical Call Centre/BPO set up, Audit By Design phenomenon is practiced religiously, wherein real time data is fetched and tracked around various KPI's like - Call Volume, Average Handling Time, Active & Inactive Operators, Minimum waiting time amongst calls etc. These KPI's are constantly monitored to make necessary and real time interventions, if the process goes beyond the Controllable Limits Business Excellence and Audit By Design, in every means and ways, should go hand in hand. Business Excellence core aim is to drive improvements and enhancing customer satisfaction to create value. This aim gets strengthening by following phenomenon of Audit by design as each and every step is being tracked and relevant traces are left to picked up in real time. With constant and frequent changes being made witnessed in the regulatory environments, what's essential is, to keep adapting new elements in the process design, while constantly relying on the basic principles of the phenomenon.

Innovation and Invention both focuses on the creative skillset required to drive excellence in each and every aspect of life, be it personal or professional. Invention primarily revolves around creating something which is completely new, for example, when Lean Six Sigma got established as core basic of bringing improvement at Toyota motors, it was Invention in Operations Methodology. Over the years. LSS has encompassed multiple facets, as per the changing times and emerging ways of working, thus, leading to Innovation in LSS Philosophy