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Well in my opinion, every organization want to function more efficiently, effectively, and successfully. At times, this involves modifying and improving an existing procedure. In other cases, it entails taking a step back and radically rethinking how the job is done, particularly in light of the latest developments in AI, which provide new approaches to activities that previously required human intervention. When to Improve the Existing Process: Start by determining whether this procedure still accomplishes its goals but simply needs to be more effective. If so, conventional techniques of improvement, such as eliminating pointless procedures, cutting down on wait times, and standardizing activities, might be beneficial. Such modifications are beneficial when: -Although there are delays or inefficiencies, the process is steady. -Employees are following the procedures, yet the results differ. -Though the experience might be more seamless, customers are typically happy. -Though insights are difficult to act upon, data is already being used. In these situations, we enhance what is currently in place by streamlining operations, educating employees, modifying schedules, or streamlining chores. When to rethink the Process Using AI: But sometime, the procedure is outdates, many things are manual, or designed to solve non-existent problems. Simple improvement won't result in significant change in certain situations. We then pose the question: Can AI enable us to radically rethink the way this task is carried out? Consider rethinking with AI when: -People devote too much time to commonplace, repetitive jobs. -Despite producing a large amount of data, the process is not being used efficiently. -Consumers anticipate quicker, more individualized service that is impossible for people to provide alone. -The present procedure wasn't created to meet the demands of the modern world years ago. AI can anticipate human needs, make judgements in real time, automate replies, and recognize patterns that humans would overlook. This has the power to change a process in a way that adds more value as well as making it faster. AI is capable of making judgements in real time, automating replies, anticipating human needs, and seeing patterns that humans would overlook. In addition to making a process faster, this can fundamentally change it in a way that adds value. An Example: A hospital could wish to shorten patient wait times. How? Option-1: Process enhancement might entail quicker check-ins, clearer communication, or improved staff scheduling. Option-2: Conversely, AI-driven redesign may incorporate systems that prioritize patients according to urgency and historical health data, predictive models to identify busy periods, or virtual assistants to respond to enquiries instantly. Though they meet distinct needs, both strategies are beneficial. One last observation: Before taking any action, need apply critical thinking: -Is this procedure still appropriate? -Will it make enough of a difference to be improved? -Or is it time to start over, using AI to help us accomplish things in a more intelligent, contemporary manner? Enhancing and rethinking are methods for different contexts and are not mutually exclusive. Making the correct choice is essential for sustained success.

HR Helpdesk is a process which we initially tried to optimize using Lean and Six Sigma methodologies. Problem faced was C-SAT survey which resulted in a score as less as 40%, where the root cause was multiple knowledge base articles referred to, to provide a solution which was extensively tedious for Helpdesk associates to browse through during the call. To simplify the process a team of 72 associates was split into 4 separate teams (Cellular Based System) – a) Payroll b) Relocation c) Deceased Affairs and d) Expense Reimbursements. This solution helped associates to focus on KB articles pertaining to the query type which they are assigned to resolve. This assisted in resolving the root cause but due to increasing volumes, management was forced to add resources which resulted in increased cost, impacting margins. With the help of AI this process can be completely reimagined. Using LLM, linked with the KB articles, AI can help provide answers within seconds and only the escalated cases would require human intervention. This solution would assist to browse through extensive KB articles in seconds which will result in good C-SAT survey. To ensure AI is providing the accurate solutions we need to ensure that the KB articles are updated and periodically audited.

One of the processes that was optimized using Lean six sigma tools in finance was month end activity of reconciliation. Lean six sigma projects helped in optimizing the processing time by reducing manual working for reconciliation process by creating standardized format for reconciliation, reducing the manual journey entries and reducing number of approvals Now this process can be optimized further by applying the AI and below mentioned are the points that AI may help with · by finding real time differences (probably by data entry or interface issues) · by implementing rule-based logic and reconciling transactions among different systems · by integrating Copilot to assist professionals in drafting adjustment entries and validating balance and providing summary of the performance · by applying OCR or NLP to read or extract the backup documentation

Let’s take an example of a healthcare process which handles provider enquiries. Traditionally, Lean Six Sigma have been used to optimize this process. For example, use of DMAIC Projects to reduce AHT or improve FCR. However, in today’s highly dynamic contact center environment Lean Six Sigma methods might face some limitations – 1.) Lean Six Sigma primarily focus on incremental improvements and are not designed for radical reimagination or innovation. 2.) Reliance on historical data – This makes it mostly a reactive intervention and not best suited for proactive identification of potential issues. 3.) Limited by human centric bottlenecks like - a. Limited speed of processing information b. Scalability challenges c. Cognitive load d. Human Bias Here are some examples of AI based interventions for the given process – 1.) AI powered self-service portal – Handing provider queries in natural language (voice or text) and provide immediate resolution like claim status, Eligibility Enquires and Pre Auth requirements. 2.) Intelligent Virtual Assistance – To handle routine and simple calls. 3.) Assisted Call Handling – Provide ‘next best action’ prompts, pull relevant provider history and auto populate documentation 4.) Realtime customer emotion and sentiment analysis and live feedback with relevant scripts to guide the agents. 5.) AI powered knowledge bases for easy retrieval of information and customized capsule training modules based on agent interactions.

In today’s rapidly evolving business environment, organizations are constantly under pressure to deliver faster, better, and more personalized services while controlling costs and navigating complexity. In the context of Healthcare BPO / RCM industry, where operational excellence is critical to ensuring timely reimbursements, accurate claim processing, compliance, and customer satisfaction. Traditional process improvement tools like Lean, Six Sigma, and RPA have helped eliminate bottlenecks and improve accuracy across front-end (example: patient access), mid-cycle (coding) and backend (AR follow-up, payment posting) functions. However, increasing complexity in payer rules, claim denials, unstructured documents (like medical records), and rising patient expectations are pushing the limits of traditional process improvement. AI technologies such as machine learning and NLP now offer an opportunity to not just improve, but completely reimagine how certain RCM processes work. The key challenge for leaders is knowing when to fix an existing process versus when to rethink it entirely using AI. Making the right call ensures optimal investment of time, technology and talent. When should a process be improved: Scenario Description and Example Stable and repeatable processes High volume, repetitive processes with consistent rules. Eligibility verification done via standard portals. Use RPA to automate screen scraping. Performance gaps exist Clear root causes and scope for waste reduction. Claim rejections due to incorrect modifier use. Use Six Sigma to reduce variation. Higher interdependencies Changes may affect upstream/downstream teams. Payment posting tied to specific clearinghouse formats. Standardize the templates to avoid disruptions. Low investment, High ROI Small optimizations yield fast results. Reduce manual touches in authorization status checks via scripting. Existing tools work well Tools like dashboards, audits, macros can solve the problem. Use Power BI to visualize claim age buckets and identify backlog trends. When should a process be reimagined with AI: Scenario Description and Example High cognitive workload Decisions involve data interpretation or unstructured content. Use NLP to extract denial reasons from payer PDFs or EOBs. Scalability barrier More volume = more headcount. Predict high-risk claims for denials using ML, instead of adding more resources in Quality team. Too many exceptions in the process Traditional rules-based automation fails. Claim status checks with multiple payer logics. Use AI bots that learn payer behavior. Process where real-time decisions needed The process must react quickly to data or events, delay in response might lead to missed opportunities. AI model to auto-route medical records for prior authorizations based on urgency/severity. AI can add unique value These are situations where AI does something that traditional process improvement or rule-based automation simply cannot because, a) the patterns are too complex for human detection, b) the data is too large or unstructured to process manually, c) the value comes from learning and adapting over time, which static systems can't do. In such cases, AI does not just make a task faster, it delivers new insights, predictions or capabilities that were not possible before. Using ML to identify root causes of denial trends before they spike.

I can very well relate to an active problem statement I am working on - Disabilty Claims allocation to Examiners. LLS approach to improvise allocation of claims : Process was as below- Indexing > Claim Set-up > Determination of O/S requirements > Claim Examiner review > Ask/follow-up on O/S requireemnts > Claim Adjudication > Payment I was realized that there were several hand-offs in the process, lack of expertise of resources at different stages - Indexer, Set-Up associate and Examiner. This lead to lack of determination of requirements early on in the process. The process was improved to reduced cycle time and improved process SOPs/documentation at early stages of the process. Reimagination : We are aiming to replace manual triage of dcuments to an OCR/NLP based Input layer at indexing stage, which will 1. auto index the claims. 2. determine all outstanding requirements via a Rule Engine (AI enabled) 3. Automatic Claim Set-up and follow-up emails to claimants for O/S requirements 4. Assigning claims to Examiners basis their Skill-set, Territory, Capacity, avalabilty, etc. (AI enabled, along with integration of ERP system) 5. STP for simpler claims Eg. Materninty. Additionally, AI can also help in generation patterens in data like- Rejection rate, Over/Under payouts, Escalations, Follow-ups needed per claim, etc.

We work in the transactional quality management domain within a call center setup for the travel industry. Maintaining service excellence through structured audits, dissemination of product updates in a timely manner, refresher trainings targeted to key individuals, and performance insights is our focus We keep a close watch on performance trends, study the process health, and publish various reports, key insights on findings to leadership, helping them with data-driven decisions. Our day-to-day operations use tools like Excel, Smartsheets, PowerPoint, Sway, & Power BI, this helps us manage data efficiently and make real-time insights into process performance. Yes, traditionally a process can be improved by Lean/Six Sigma that focuses on Eliminating waste, reducing variation, and improving process flow These are powerful, but they assume the process itself is fundamentally sound and only needs tuning. However, AI introduces capabilities that can fundamentally change the nature of the work, not just optimize it. While these are traditional and powerful ways to fix and straighten processes, they assume that the process itself is fundamentally sound and only needs tuning. However, with the introduction of AI, various capability avenues open that can fundamentally change the day-to-day nature of work, not just limiting it to optimization alone. Automating day-to-day cognitive tasks Our team conducts audits, disseminates updates, and identifies training needs; these usually involve pattern recognition, decision-making, & communication. These tasks can be handled by an AI: Natural Language Processing (NLP) can analyze call transcripts for quality. Generative AI can draft personalized refresher content. Predictive analytics can forecast training needs before issues arise. Helping with real-time decision-making Currently used Power BI showcases and is focused on visibility, but AI can go further: Prescriptive analytics can suggest actions based on trends. Anomaly detection can flag outliers in real-time, not just report them. Scalability and Adaptability Upon deploying and fed with a larger database, AI systems can adapt to new client updates, changes in the business rules, & evolving customer expectations quicker than manual dissemination and training cycles. Suggestive vision on re-imagination for AI-powered transformation: Quality Audits conducted by an AI Deploying speech-to-text & natural language processing to automatically audit calls/transactions. Score transactions based on sentiment, compliance, and resolution quality. Flag calls/transitions for human auditor’s review only when it is necessary. Dynamic Knowledge Management Introduce AI chatbot or AI assistant trained on client updates & SOPs. This will be available 24/7 to answer agents'/processor's queries. The database will be automatically updated, and the same will be disseminated with the new product info to the pre-defined group. Predictive Training Needs The introduction of machine learning will help in analyzing audit scores, test results, and call/transaction metadata. Predict which processors are likely to need refresher training. Auto-schedule microlearning modules tailored to individual gaps. AI-Augmented Reporting These dashboards will not only show performance trends but also explain them. Natural language summaries of performance for senior executives/senior leadership Conversational Interfaces for Leadership Senior leaders interact with a voice or chat interface to ask: How did the team in Boston perform last week? What are the top 3 training gaps across delivery sites

In E-Commerce sector, One process traditionally optimised using Lean Sigma is Processing of Order returns from Customers. This process have been optimised using Lean (eliminating/ reducing unnecessary return steps) and six sigma methodology (Reducing defects like incorrect refunds etc.,). Some of the common improvements like: Standardised Return labels & automated RPA workflows Establishing a Centralised return centre for faster and effective Return processing. Operation Data Analytics to identify frequent return reasons However, these improvements don't address the core inefficiencies like High Return Rates, Fraud and Manual Inspection bottlenecks. Hence, improving the process further wont benefit in fixing these inefficiencies. So, this process ripe for "AI Driven Re-Imagination". Here, the AI re-imagination becomes essential which focus on: Preventing the predictive returns based on data analysis on customer behaviour (like browsing patterns, order history) and flag the high risk orders before shipping. And also, Generative AI Chatbots can suggest correct sizes and products pre-purchase to reduce returns. Smart Automated Inspection can instantly assess the returned item condition, auto detecting the wear/ damage without human touch. Machine learning models auto verify and approve/deny refunds based in Fraud patterns (ex: Serial returners) From above AI re-imagined process, Return centres become Profit centres instead of Cost sink with AI slashing processing cost to significant extent proactively So, Lean Six Sigma can't predict or prevent returns. It only optimises existing process slightly better and make the process faster. On another side, AI flips the model entirely from Reactive to predictive

After i had completed lean six sigma with benchmark company i had started developed and implement Statistical Process control chart for the following stages: - Monitoring Receiving testing - Monitoring In Process Quality control - Monitoring Before dispatched the final Products to the customers QC Team monitoring testing/inspections records through SPC using Minitab software my issue is that some time the QC Team didn't recognize the trend or shift so production will keep continue manufacture until outliers observed after CAIPO Course we can apply n8n so we can received alerts early so i strongly recommended and i will start applying n8n for above process so, we can notify the production team early and a void the rejection

Improve a process when it works okay but could be faster, cheaper, or better. You just make small changes to do things more efficiently. Reimagine with AI when the process is slow, very manual, or not working well — and AI can do it in a totally new, smarter way.