Alpana Sharma

Self-serving bias is a cognitive bias that leads individuals to attribute positive outcomes to their own actions while blaming external factors for negative outcomes. In the context of Lean Six Sigma projects, this bias can significantly impact the identification of root causes, as team members may overlook or dismiss their own contributions to problems, leading to incomplete or inaccurate analyses. Below are impact of Self-Serving Bias on Root Cause Identification: Misattribution of Causes: Team members may attribute failures to external factors (e.g., equipment failure, market conditions) rather than examining their own processes or decisions. This can result in a failure to address the actual root causes of issues. Resistance to Feedback: Individuals may resist constructive criticism or feedback that challenges their role in a problem, leading to a lack of accountability and hindering the improvement process. Group Dynamics: In team settings, self-serving bias can create an environment where individuals are reluctant to share their mistakes or shortcomings, which can stifle open communication and collaboration. Inaccurate Data Interpretation: Teams may selectively interpret data to support their views, leading to biased conclusions that do not reflect the true state of the process. Below are some of the strategies to mitigate Self-Serving Bias: Foster a Culture of Accountability: Encourage a culture where team members feel safe to admit mistakes and learn from them. This can be achieved through leadership modeling vulnerability and emphasizing the importance of collective learning. Use Data-Driven Decision Making: Rely on objective data and statistical analysis to guide discussions and decisions. This reduces the influence of personal biases and helps teams focus on facts rather than opinions. Conduct Root Cause Analysis Workshops: Facilitate structured workshops that focus on root cause analysis techniques (e.g., 5 Whys, Fishbone Diagrams). These methods encourage systematic exploration of causes and can help mitigate individual biases. Encourage Diverse Perspectives: Involve team members from different functions or levels in the analysis process. Diverse viewpoints can challenge assumptions and provide a more comprehensive understanding of issues. Implement Peer Reviews: Establish a process for peer reviews of findings and conclusions. Having others review the analysis can help identify biases and ensure a more objective assessment of root causes. Regular Reflection and Debriefing: After project milestones or completion, hold debriefing sessions to reflect on what worked, what didn’t, and why. Encourage honest discussions about individual and team contributions to outcomes. Training and Awareness: Provide training on cognitive biases, including self-serving bias, to raise awareness among team members. Understanding these biases can help individuals recognize and mitigate their effects in decision-making. By implementing these strategies, Lean Six Sigma teams can reduce the impact of self-serving bias, leading to more accurate identification of root causes and ultimately more effective process improvements.

Quantile-based analysis is a statistical technique that divides a dataset into equal-sized intervals, or quantiles, which helps in understanding the distribution of data. This method provides insights into the spread and central tendency of the data, allowing analysts to identify patterns, outliers, and the overall shape of the distribution. Benefits of Quantile-Based Analysis: Understanding Distribution: Quantiles provide a clear picture of how data is distributed. For example, the median (the 50th percentile) indicates the center of the data, while quartiles (25th and 75th percentiles) show the spread of the data. Identifying Outliers: By examining the tails of the distribution (e.g., the 1st and 99th percentiles), analysts can identify outliers or extreme values that warrant further investigation. Robustness to Non-Normality: Quantile analysis does not assume a normal distribution, making it useful for skewed or non-parametric data. This is particularly beneficial in fields like finance, where returns may not follow a normal distribution. Comparative Analysis: Quantiles allow for easy comparison between different datasets. For instance, comparing the income distribution of two different regions can be done effectively using quantiles. Data Segmentation: Quantiles can be used to segment data into groups for further analysis, such as identifying high-performing and low-performing segments in a business context. Limitations of Quantile-Based Analysis: Loss of Information: While quantiles summarize data, they can obscure details. For example, two datasets with the same quartiles can have very different distributions. Sensitivity to Sample Size: Small sample sizes can lead to unreliable quantile estimates, particularly for extreme quantiles. Interpretation Challenges: Understanding what quantiles represent in the context of the data can be challenging, especially for those unfamiliar with statistical concepts. Non-uniqueness: Different methods of calculating quantiles (e.g., linear interpolation vs. nearest rank) can yield different results, leading to potential confusion. Examples of When Quantile Analysis is Useful: Income Distribution: In economics, quantile analysis is often used to study income distribution, helping to identify income inequality by comparing the lower and upper quantiles. Performance Metrics: In business, companies may analyze sales data by quantiles to identify top performers (e.g., the top 10% of salespeople) and strategize accordingly. Risk Assessment: In finance, quantiles are used to assess risk by analyzing the worst-case scenarios (e.g., Value at Risk, which looks at the 5th percentile of potential losses). Health Data: In epidemiology, quantiles can help identify thresholds for health outcomes, such as determining the cutoff for obesity based on BMI percentiles. Challenges in Applying Quantile Analysis to Real-World Data: Data Quality: Real-world data can be messy, with missing values, outliers, and errors that can skew quantile estimates. Complex Distributions: Some datasets may have multimodal distributions, making it difficult to interpret quantiles meaningfully. Dynamic Data: In fields like finance or healthcare, data can change rapidly, requiring continuous updates to quantile analyses to remain relevant. Contextual Interpretation: Analysts must be careful to interpret quantiles in the context of the specific dataset and its characteristics, as the same quantile can have different implications in different contexts. In summary, quantile-based analysis is a powerful tool for understanding data distribution, offering several benefits while also presenting limitations and challenges. Its effectiveness largely depends on the context of the data and the specific questions being addressed.

Differentiating between short-term benefits and long-term value creation when assessing ROI from AI solutions is crucial for businesses to ensure they are making informed decisions that align with their strategic goals. Here are several approaches to help businesses make this distinction: 1. Define Clear Objectives Short-Term Goals: Identify specific, measurable objectives that can be achieved quickly, such as reducing operational costs, improving efficiency, or increasing sales within a defined timeframe. Long-Term Goals: Establish broader strategic objectives that focus on sustainable growth, such as enhancing customer experience, building brand loyalty, or developing new products and services. 2. Use Different Metrics for Assessment Short-Term Metrics: Focus on immediate financial indicators like cost savings, revenue increases, or productivity improvements. Metrics could include: Payback period Immediate cost reductions Increased sales volume Long-Term Metrics: Evaluate metrics that reflect sustained impact over time, such as: Customer lifetime value (CLV) Market share growth Brand equity and reputation Innovation pipeline and new product development 3. Conduct a Time-Based ROI Analysis Short-Term ROI Analysis: Calculate ROI based on immediate financial returns from AI investments, typically within the first year. This can include quick wins from automation or efficiency gains. Long-Term ROI Analysis: Assess the potential future value generated by AI solutions over several years. This may involve forecasting future cash flows, considering factors like customer retention, market expansion, and ongoing innovation. 4. Consider Qualitative Benefits Short-Term Qualitative Benefits: Identify immediate qualitative improvements, such as enhanced decision-making speed or better data insights that lead to quick operational changes. Long-Term Qualitative Benefits: Evaluate the broader impact on organizational culture, employee engagement, and customer satisfaction, which may take time to manifest but are critical for long-term success. 5. Analyze Risk and Uncertainty Short-Term Risks: Assess risks associated with immediate implementation, such as technology adoption challenges or resistance to change. Long-Term Risks: Consider risks related to market dynamics, technological advancements, and evolving customer expectations that could affect the sustainability of AI investments. 6. Monitor and Adjust Strategies Regular Review: Implement a framework for ongoing assessment of AI initiatives, allowing businesses to track both short-term and long-term outcomes. This can help in adjusting strategies based on performance. Feedback Loops: Establish mechanisms for gathering feedback from stakeholders, including employees and customers, to understand the impact of AI solutions over time. 7. Align with Business Strategy Integration with Business Goals: Ensure that AI initiatives are aligned with the overall business strategy, considering how both short-term benefits and long-term value creation contribute to the organization’s mission and vision. 8. Case Studies and Benchmarking Learn from Others: Analyze case studies of other organizations that have implemented AI solutions, focusing on their short-term gains and long-term outcomes. Benchmarking against industry standards can provide insights into best practices. By employing these strategies, businesses can effectively differentiate between short-term benefits and long-term value creation when assessing the ROI of AI solutions, leading to more informed decision-making and strategic alignment.

HyperAutomation is a business strategy that involves the use of advanced technologies to automate complex business processes and functions beyond traditional automation. It combines various automation tools and technologies, such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and process mining, to create a more comprehensive and efficient automation ecosystem. HyperAutomation enhances Robotic Process Automation (RPA) by integrating advanced technologies and capabilities that extend beyond traditional automation. Here are some key capabilities that HyperAutomation adds to RPA: Intelligent Decision-Making: HyperAutomation incorporates artificial intelligence (AI) and machine learning (ML) to enable systems to analyze data, learn from it, and make informed decisions. This allows for more complex and nuanced automation scenarios that RPA alone cannot handle. Natural Language Processing (NLP): By integrating NLP, HyperAutomation can process and understand human language, enabling automation of tasks that involve unstructured data, such as customer inquiries or document processing. Process Mining: HyperAutomation utilizes process mining tools to analyze existing workflows and identify inefficiencies. This capability allows organizations to optimize processes before automating them, ensuring that the automation is effective and efficient. End-to-End Automation: While RPA typically focuses on automating specific tasks, HyperAutomation aims to automate entire workflows and processes, providing a more comprehensive solution that enhances overall operational efficiency. Integration of Multiple Technologies: HyperAutomation combines RPA with other technologies such as AI, ML, and analytics, creating a more robust automation ecosystem that can handle a wider range of tasks and processes. Scalability and Flexibility: HyperAutomation solutions can be scaled across various departments and functions, allowing organizations to adapt quickly to changing business needs and market conditions. Enhanced User Experience: By automating complex processes and improving decision-making, HyperAutomation can lead to a better user experience for both employees and customers, as it reduces manual intervention and speeds up service delivery. Industries that are most likely to benefit from HyperAutomation include: Financial Services: Banks and financial institutions can automate processes such as loan approvals, compliance checks, and customer service, leading to faster transactions and improved customer satisfaction. Healthcare: HyperAutomation can streamline patient data management, billing processes, and appointment scheduling, enhancing operational efficiency and patient care. Manufacturing: Automation of supply chain management, inventory control, and quality assurance processes can lead to increased productivity and reduced operational costs. Retail: HyperAutomation can improve inventory management, customer service, and order fulfillment processes, enhancing the overall shopping experience. Telecommunications: Companies can automate customer support, billing, and network management, leading to improved service delivery and customer retention. Insurance: Automating claims processing, underwriting, and customer service can significantly reduce processing times and improve customer satisfaction. Overall, HyperAutomation provides organizations across various industries with the tools to enhance efficiency, reduce costs, and improve service delivery, making it a valuable strategy in today's competitive landscape.

Non-parametric analysis is a statistical method that do not o not reference specific parameters. Parameters are the values of Shape, Spread and Centering. For a normal distribution the shape is normal, the spread is s, the center is m. For every value of m and s, there is another normal distribution that is defined. A nonparametric hypothesis test looks at the sample data and calculates a test statistic based on the medians without reference to the parameters. It determines whether that test statistic is inside or outside of the chosen risk level (i.e. beyond the decision point). Below are some Industries where Non-parametric Analysis is commonly Used: Manufacturing: In manufacturing, non-parametric methods can be used to analyze defect rates, production times, and other quality metrics that may not follow a normal distribution. Healthcare: In healthcare, non-parametric tests can be applied to patient satisfaction surveys, treatment outcomes, and other data that do not meet parametric assumptions. Finance: Financial analysts may use non-parametric methods to assess risk and return distributions, especially when dealing with non-normally distributed financial data. Retail: Retailers can use non-parametric analysis to evaluate customer feedback and sales data Below are advantages of Non-parametric Analysis: Fewer Assumptions: Non-parametric methods do not require the assumption of normality, making them applicable to a wider range of data types. For example, if a manufacturing process produces a variety of defect types that do not follow a normal distribution, non-parametric tests like the Mann-Whitney U test can be used to compare defect rates between different production lines. Robustness to Outliers: Non-parametric methods are less sensitive to outliers, which can skew results in parametric tests. For instance, in a healthcare study analyzing patient recovery times, if a few patients had exceptionally long recovery periods, a non-parametric test like the Kruskal-Wallis test would provide a more reliable comparison of recovery times across different treatment groups. Applicability to Ordinal Data: Non-parametric methods are ideal for analyzing ordinal data, such as survey responses. For example, in a customer satisfaction survey where responses are rated on a scale from 1 to 5, non-parametric tests can effectively analyze the differences in satisfaction levels across different demographics without assuming equal intervals between ratings. Simplicity: Non-parametric tests are often simpler to compute and interpret, making them accessible for teams without extensive statistical training. For example, using the Wilcoxon signed-rank test to compare pre- and post-intervention scores in a quality improvement project can be straightforward and effective. Below are some of the Non-parametric Tests: Mann-Whitney U Test: Used to compare differences between two independent groups when the data is not normally distributed. For instance, comparing the defect rates of two different suppliers. Kruskal-Wallis Test: An extension of the Mann-Whitney U test for comparing more than two groups. This could be used to analyze customer satisfaction ratings across multiple product lines. Wilcoxon Signed-Rank Test: Used for comparing two related samples, such as measuring the impact of a process change on defect rates before and after the change. One of the key things to keep in mind is that nonparametric tests tend to have less power (to detect a difference) than the parametric tests.

Gamification can greatly enhance the effectiveness of Lean Six Sigma trainings by making the learning process more engaging, interactive, and enjoyable. By incorporating game elements into the training, participants are motivated to actively participate, retain information better, and apply their knowledge effectively. Here are some examples of how gamification can be implemented in Lean Six Sigma trainings: Leaderboards and Points System: Implementing a leaderboard and points system can create a sense of competition among participants. They can earn points for completing training modules, solving problems, or achieving specific milestones. This encourages active participation and motivates individuals to strive for better performance. Simulation Games: Using simulation games allows participants to apply Lean Six Sigma concepts in a virtual environment. For example, a manufacturing simulation game can simulate a production line where participants have to identify and eliminate waste, optimize processes, and improve quality. This hands-on experience helps reinforce learning and develop problem-solving skills. Scenario-based Challenges: Presenting participants with real-life scenarios and challenges related to Lean Six Sigma principles can be an effective way to apply their knowledge. For instance, participants can be given a case study where they need to identify process inefficiencies, analyze data, and propose improvement strategies. By solving these challenges, participants gain practical experience and a deeper understanding of Lean Six Sigma concepts. Badges and Rewards: Awarding badges or rewards for completing specific tasks or achieving milestones can provide a sense of accomplishment and recognition. For example, participants can earn badges for successfully completing training modules, demonstrating proficiency in statistical analysis, or leading improvement projects. These rewards serve as incentives and encourage continuous learning and improvement. Team-based Collaboration: Encouraging teamwork and collaboration through gamification can foster a sense of camaraderie and engagement. Participants can be divided into teams and compete against each other in solving problems or improving processes. This promotes knowledge sharing, communication, and collective problem-solving skills. Progress Tracking and Feedback: Providing participants with real-time progress tracking and feedback on their performance can help them gauge their improvement and identify areas for further development. This can be done through interactive dashboards or personalized reports that show their achievements, areas of strength, and areas that need improvement. By incorporating gamification elements into Lean Six Sigma trainings, participants are more likely to stay motivated, actively engage in the learning process, and apply their knowledge effectively in real-world situations. This ultimately enhances the overall effectiveness of the training program.

In the context of organizational management, the concept of "Manage the Metric" refers to a shift in focus from simply tracking and monitoring metrics to actively managing and influencing those metrics to achieve desired outcomes. Instead of passively observing metrics as indicators of performance, organizations that "Manage the Metric" take proactive steps to drive improvements and optimize their performance based on the metrics they track. When organizations adopt a "Manage the Metric" approach, they recognize that metrics are not just measurements of past performance but also powerful tools for driving future success. They use metrics as a means to identify areas for improvement, set targets, and align their efforts towards achieving specific goals. This approach involves analyzing the metrics, identifying trends and patterns, and taking action to address any issues or opportunities that arise. Managing the metric can have a significant impact on customer satisfaction and business growth. By actively managing metrics, organizations can identify and address customer pain points, improve product or service quality, and enhance the overall customer experience. For example, if a company tracks customer satisfaction scores and identifies a decline in ratings, they can take proactive steps to investigate the root causes, implement corrective actions, and monitor the impact of those actions on customer satisfaction. Similarly, managing the metric can drive business growth by enabling organizations to identify and capitalize on opportunities for improvement. By closely monitoring key performance indicators (KPIs) related to revenue, market share, or customer acquisition, organizations can identify areas where they can optimize their strategies, allocate resources more effectively, and make data-driven decisions to drive growth. To prevent the shift from "Manage by Metric" to "Manage the Metric" from negatively impacting customer satisfaction and business growth, organizations can consider the following approaches: Balanced approach: While it is important to actively manage metrics, organizations should also ensure that they do not become overly fixated on a single metric or set of metrics. It is crucial to maintain a balanced view and consider a range of metrics that provide a comprehensive understanding of performance. Customer-centricity: Organizations should prioritize customer satisfaction and align their metrics with customer-centric goals. By focusing on metrics that directly impact customer experience and incorporating customer feedback into performance measurement, organizations can ensure that their efforts are aligned with customer needs and expectations. Continuous improvement: Organizations should view metrics as a means for continuous improvement rather than static targets. By regularly reviewing and analyzing metrics, organizations can identify areas for improvement, implement changes, and monitor the impact of those changes on performance. Employee engagement: Engaged employees play a crucial role in driving performance and achieving desired metrics. Organizations should foster a culture of ownership and accountability, where employees are empowered to actively contribute to improving metrics and are recognized for their efforts. Regular communication and feedback: Transparent communication and feedback loops are essential to ensure that metrics are effectively managed. Regularly sharing performance data, discussing trends, and seeking input from employees and customers can help identify potential issues and opportunities for improvement. By adopting these approaches, organizations can strike a balance between managing metrics and focusing on customer satisfaction and business growth, ensuring that the shift to "Manage the Metric" is beneficial rather than detrimental.

Persona profiling is the process of creating detailed profiles or representations of specific target audience segments or customer personas. It involves gathering and analyzing data about the characteristics, behaviors, needs, preferences, and goals of individuals within a particular group. The purpose of persona profiling is to gain a deeper understanding of the target audience and to tailor marketing strategies, products, or services to meet their specific needs and preferences. Persona profiling typically involves conducting research, such as surveys, interviews, and data analysis, to gather information about the target audience. This information may include demographic data (age, gender, location), psychographic data (values, attitudes, interests), behavioral data (purchasing habits, online behavior), and other relevant factors. Once the data is collected, it is used to create fictional personas that represent different segments of the target audience. These personas are given names, backgrounds, and detailed descriptions that capture the key characteristics and motivations of the individuals they represent. Persona profiles often include information such as age, occupation, goals, challenges, preferences, and buying behaviors. By developing persona profiles, businesses can better understand their target audience and make informed decisions about product development, marketing strategies, messaging, and customer experience. Persona profiling helps businesses to create more personalized and targeted marketing campaigns, improve customer engagement, and enhance overall customer satisfaction. Persona profiling can be used in Lean Six Sigma project at different stages based on the requirements. It can be used in Define phase while doing the Stakeholder analysis to ensure that we capture we capture different goals, motivations, challenges and preferences of different stakeholders and accordingly can develop an engagement plan to gain the optimum level of support from them. Personal profiling is a vital ingredient while developing a new product or service in Design phase. It helps to create different persona profiles of the probable users based on their age, gender, job profile, preferences, behaviors and many other demographic and psychographic traits. This in turn help us to do right segmentation to create and deliver more personalized product or service for the users for better customer experience. Below are some of the examples where Persona Profiling are used extensively: Starbucks: Starbucks uses persona profiling to understand the preferences and lifestyles of their customers. They create personas such as 'Busy Professionals' and 'Coffee Connoisseurs' to tailor their menu offerings and store layouts. Netflix: Netflix uses persona profiling to understand the viewing habits and preferences of their subscribers. They create personas such as 'Binge Watchers' and 'Movie Buffs' to personalize recommendations and improve the user experience. Nike: Nike uses persona profiling to understand the motivations and behaviors of their target audience, such as 'Athletic Performance Seekers' and 'Lifestyle Fitness Enthusiasts'. This helps them develop products and marketing campaigns that resonate with their customers. Apple: Apple uses persona profiling to understand the needs and preferences of their target audience. They create personas such as 'Creative Professionals' and 'Tech Enthusiasts' to tailor their marketing messages and product features. It is important to note that persona profiling is an ongoing process that requires regular updates and adjustments as customer preferences and behaviors evolve over time.

At some level Invention and Innovation seem same or interchangeable but when we do a deeper dive it’s not. Inventions are typically creation of a new product which wasn’t existent and is being created. History is full of such examples; invention of Airplane by Wright Brothers changed the way one could travel or ship products from one place to another. Similarly, invention of Telephone completely revolutionized the mode of communication forever. Inventions are usually patented by their inventors or organizations. They are breakthrough or a novel solution to a human problem or need for advancement. Innovation on other hand is different from invention in regards of being completely non-existent. Rather innovation is about improving an existing product. It is a result of incessant pursuit of excellence by keeping the vision & customer needs in vision, coming up with new ideas and implementing them to an existing product to add new features to make it better and efficient. Great example of innovations are the smart phones, smart watches and digital cameras. While the concept of a mobile phone was already invented as keypad mobiles in early 90s, companies like Apple and Samsung innovated it and taking absolutely up to a different level in 2000s by adding features like touchscreens, internet connectivity, and mobile apps, transforming the way we communicate and access information. In fact, innovation is one of the prominent Theories of Profit in Microeconomics. Overall, we can say, invention is about creating something entirely new which wasn’t existent earlier, while innovation is about improving or building upon existing products. Both invention and innovation play crucial roles in human evolution and business excellence in any sort of business. Lean Six Sigma principles are more focused on improvement of existing product/business. To invent new thing these principles may not work completely as we can go for Design for Six Sigma, but it will still help to an extend not completely.

Artificial Intelligence is a cutting-edge technology shaping the world around us on an unprecedented pace. By 2027, 85M jobs are expected to be displaced globally because of AI, and 60% of employees will need reskilling or upskilling. At the same time, AI will not replace scientists, lawyers, actuaries or consultants. Scientists, lawyers, actuaries and consultants who use AI will replace those who do not. AI phases appear linear; in fact, disruption will occur when someone leapfrogs legacy ways of working to make an industry-wide impact. AI like internet has enormous potential to transform our lives in many aspects. It can be very well used in a Lean Six Sigma DMAIC project, below are phase wise potential uses and some of the Use Cases: Define – Using GEN AI (preferably an in-house developed) elements like Business Case, Problem Statement from Project Charter can be enhanced without spending too much time along with a recommended list of CTQs by learning from and extrapolating existing datasets. It can also help to refine or perform a thorough Project Risk Assessment provided a specific Prompt is given. Measure – Its always better to have an additional mind and here we can leverage the GEN AI linked to years of data base from different clients, services, practices and regions to generate ideas about a specific business problem along with prioritization. GEN AI can also help to generate / estimate Sample Size, Sigma Level and Process stability following SPC principals by providing right set of data and specific commands. Analyze – In this phase Gen AI can help to establish initial Hypothesis and Predictive Analytics can help us verify them by analyzing historical and real-time data to forecast future events, trends, or behaviors, enhancing decision-making and strategic planning. This can help us to narrow down a list of potential factors to vital few significant factors. Improve – GEN AI can be used to generate possible solutions or refine / prioritize the ideated solutions. There are number of solutions that can be developed using AI for given business problems. Some of the examples as below: Contact Center and Front Office: GPT can be used to help customer support staff answer real time complex questions to customer Copilot can help to generate after call notes Predictive Analytics can help identify a Caller who has the potential for being a Repeat Customer or also can highlight and prioritize a Repeat Customer AI can also be used to for call audits, feedback and colleagues coaching / feedback with minimal human intervention Besides these AI can also be used for Virtual Agents who perform tasks, make decisions, interact with their environment independently, based on programming and learned experiences, without human intervention. Business Development: GEN AI can be very helpful to perform Client study / analysis before a RFP an also to generate RFP responses if it is linked with a rich data source. This could save a lot of time of Client Facing colleagues which could be used for other more value add works. HR: GEN AI can be very useful in creating Job Architecture and Job Description besides using historical and real-time data to forecast future events, trends, or behaviors, enhancing decision-making and strategic planning. Client Administration: Sound OCR tool combined with Predictive Analytics and GEN AI can be used to automatically process and integrate synthesized or enhanced data from various sources into a central system, improving data quality and insights for faster and better decision making. There could be many more Use Case for GEN AI even in the field of Legal, Medical Science or Hospitality industry. What we have seen for now is the tip of iceberg and sky is the limit. GEN AI along with Predictive Analytics using historical and real time data can help differentiate the Significant Improvements / Solutions from the trivial ones. Control: GEN AI and Predictive Analytics can help us assess the sustainability of improvements in this phase using historical and real time data. GEN AI can also help us identify key Risk areas around the improvement and recommend sound controls around them given specific prompts. It can also help design robust Change Management plans given it is linked to decent data sources and provided specific prompt. At the end GEN AI can also suggest other parts of businesses where the best practices from a given project can be leverage with little tweaks.