Q 779. In Business Excellence, we often focus on eliminating waste, reducing variation, and improving flow. But with the rise of AI, some processes don’t just need improvement — they need to be completely rethought. Think of one process in your domain that has traditionally been optimized using Lean or Six Sigma methods. 

Now ask yourself: Should this process be improved further — or is it time to reimagine it entirely using AI? Explain your reasoning, and share what that reimagination might look like.

 

The best answer will be selected on the basis of: 

• Clarity in identifying the process and its improvement history  
• Depth of reasoning in deciding between improvement vs. reimagination  
• Creativity and practicality of the AI-enabled alternative

 

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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.

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.

Flour Bagging Variance - I would share my thoughts on my first DMAIC project back in 2014. 

Problem statement 

As a company, we are experiencing a problem with underweight flour packaging in 50 kilograms flour bags. Due to recent customer complaints in underweight in 50 kilograms flour bags. Since the problem has existed for at least two months. It has reduced customer orders which directly affected the profit of the company hence, the Six Sigma project is carried out to reduce the weight variance in flour bagging.

  When we started analyzing the historical data. The percentage of defects was 49%. It was 10 % underweight and 39% overweight and the problem are due to high standard deviation of 0.150 kilograms.

To achieve the 3-sigma limit in flour bagging with the overall defective percentage of 6.5%. 3.25% of defective on underweight and 3.25% of defective on overweight. Which requires standard deviation to be 0.054 kilograms. Since we have achieved the standard deviation 0.050 kilograms in one of the two scales. Our achievable target is 0.050 kilograms. Through Z entitlement our target is 0.050 kilograms.

 

Improvement Method - traditional approach (Daily checks, Weekly checks, Monthly checks)

- Calibration - Schedule, levelling, cleaning, load cell voltage checks and zeroing. 

- Sealing rings - Flour escape containment checks, Compressor pressure and Piston condition

- Bag conditions - Bag length, width, weight and type. 

- Weight checks - 60 bags per truck

 

Traditional improvement Vs Reimagination AI. 

- DMAIC approach traditionally reactive means problem occurred, solution unknown DMAIC process used.

- Facing issue in real time problems issue in high-speed operations.

- Customer demands high tolerance requires 50 kg because of batch process example noodle requires 350 kg per batch that's 7 bags. weight variance will have recipe issues in terms output volume and excessive consumption materials other than flour  

 

Re-Imagined AI in Flour bagging 

- Real time weight record and cumulative weight of bags and truck weights can be monitored through sensors and weight checkers and automation software interlinking weighbridge with scale data.

- Theft protection. 

- Adaptive tolerance errors and alerts. 

- Operator dependency for checks is limited.

- Prevention of weight issue and customer complaints due to Proactive action. 

 

Should this process be improved further or is it time to reimagine it entirely using AI?

 

Flour milling operations demand of High-speed operations, Minimal tolerance due to increased consumption & Sales.

- Traditional methods are good process improved further still will be reactive and still dependent on Manpower.  

- AI on other hand can produce Real time monitoring, automated alerts on weight control and to maintain compliance on 50 kg bag weights and to increase customer loyalty. 

 

This Process - it must be reimagined entirely using AI is necessary increase customer satisfaction, long term success and be competitive in the market.  

 

 

Edited June 20, 2025Jun 20 by Mohammed Jaffer
last question wasn't answered

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

 

 

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

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.

1. 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.

1. 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.

2. 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:

 

1. 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.

2. 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.

3. 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.

4. 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

5. 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

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.

 

In medical coding which is my domain, the one of the processes is manual chart abstraction and coding from medical record documentation. Traditionally, the medical coders review provider documentation to assign ICD and CPT codes which lead to coding errors.

Six Sigma projects focused on reducing coding errors and improving First Time Right (FTR). The error rates have been reduced, but still could not eliminate the root causes. This traditional improvement assume humans are prone to errors in interpreting the medical records to assign codes.

Imagine an AI-powered medical coding assistant which participates with medical record documentation. It reads and understands provider notes and recommends codes in real time.

This is not only efficiency, but it's a change in how value is produced: instead of "interpreting" what a provider meant, coders are now "co-piloting" the AI, concentrating on edge cases, ensuring compliance, and constantly improving it.
This process needs more than just optimization; it's a textbook example of how AI allows reinvention. Additionally, it liberates human skill for tasks that are unique to humans, such as governing, displaying empathy, and making decisions.

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.

 

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.

Context:

There was a Software development product development, that I was aware of. The IT project(product development) had multiple technologies with multiple vendors. The Sponsor of the project (product development) was very much concerned about the delay in delivering the requirements/deliverables (functionalities) to his customers.  This issue was there for almost a year. The development team had always stretched itself to complete the deliverables on time, more often than not comprising on quality.  On an average it took 18 days(Cycle time) for a single requirement to be completed end-end (time taken to deliver a requirement to production).

 

That was the time, when a Six Sigma Black Belt (SSBB) Process consultant was roped in. She was requested to focus on the delayed delivery/release of requirements to Production environment 

Process: Cycle Time Reduction

1.        The cycle time for each of the requirements going to production (release) was taking time.   The Process consultant did a value stream mapping of the AS-IS process. She understood what are all the steps involved in from the Requirements stage to the Production stage and also which stage is owned by which vendor (as this is a multi-vendor environment).

2.        The lead and wait time in each step was noted

3.        The SSBB consultant found out few couple of steps (Development and Testing) where the wait time was more

a.        Code Review (6 days)

b.        Testing in staging (which mimics production environment) environment (4 days)

c.        Technology version Upgradation (3 days)

(Note: this was a challenge as multiple technologies were used in the project, and to provide rich features to the system (product), the latest versions of technologies were needed.. so there was a constant need for technology upgradation in some technologies)

4.        After finding these key steps (#3) that had more wait time.  Several root causes (as Vital Xs) came out such as

a.        Root causes for Code Review

                                                               i.      Lack of knowledge in Technology

                                                              ii.      Lack of awareness in Coding Standards and Best Practices

                                                            iii.      Delay in identifying the reviewer

                                                            iv.      Dependency on the availability of the identified reviewer

b.        Testing in Staging

                                                               i.      No coordination/cooperation between Vendor who does the development work and the Vendor who does the testing work

                                                              ii.      Vendor doing development work and Vendor doing testing do not adhere to a common product goal, resulting in siloed way of working

c.        Technology Upgradation

                                                               i.      Lack of awareness in latest version of technology

                                                              ii.      Lack of backward compatibility awareness on different versions of technology (For eg., how latest is compatible with the existing or current version used)

              

  With the help of Fishbone diagram and Pareto chart, the above Vital Xs were found out

                 How was this addressed/improved?

1.       Code Review:  The SSBB consultant facilitated a brainstorm session. The team used that session to understand how the root causes for code review can be addressed. It came with Pugh Matrix approach for arriving at a best solution for couple of problems related to technology learning by the team members (whether self-study, video learning, in-person training etc) and also did some Force field analysis for code reviewing for both Peer-Peer vs SME reviewing to address identifying reviewers and dependency on identified reviewers and accordingly arrived at a conclusion.  There was some initial investment (of days) especially when learning came into picture but over time, the overall wait time saved was 4 days (so job completed in 2 days!!)

 

2.       Testing in Staging: Up next, the SSBB consultant facilitated her next brainstorming session so that the team can address the issue of Testing in ‘staging’ environment (an environment which mimics the production environment).  The team ensured that there was a common product goal (based on SMART) and that all members of the team were aligned, irrespective of which vendor they belonged to. This ensured that there was collaborative effort and more focus. This resulted in 2 days saved.

 

3.       Technology Upgradation: Finally the SSBB consultant facilitated a brainstorming session for the team to address “Technology upgradation” issue.  The team had prepared a list of all technologies used in the project with the current version and the latest version.. It did a regression analysis which technology has a correlation with which other technology and what kind of impact it has when there is a technology upgradation and accordingly updated the corresponding technology.  This resulted again in 1 day saved

[Note: This happens when there are some libraries (collection of files/functions that are meant for addressing some purposes) that is called from one technology to another and then compatibility issues happen at times]

To see how the improvement was effective, lets compare the measurement of the process time+wait time for these steps in the overall scheme of things

Cycle Time Reduction “ Process Steps in Product Delivery for a Single Requirement:

Steps	Before the Process Implementation change (AS-IS) [ Process Time + wait Time ]	After the Process Implementation Change [ Process Time + wait Time ]	Remarks
Requirements Analysis	.5 day	.5 day
High Level Design	.5 day	.5 day
Low Level Design	1 day	1 day
Development (includes Code development,  Technology upgradation, Unit Test, Code Review)	Code Development (1.5 days) Unit Test (.5 day) Technical upgradation (3 days) Code Review (6 days)	Code Development (1.5 days) Unit Test (.5 day) Technology upgradation (1 days) Code Review (2 days)	Code Review, and Technology Upgradation part of the ‘Development’ process step was worked upon resulting in 6 days of overall saving
Functional Testing (System testing)	.5 day	.5 day	This was treated a separate process by the customer organization,  as it was done by the team
UAT Testing	.5	.5	This was treated a separate process by the customer organization as this was done by specific end users/few critical customer stakeholders
Staging	3 days	1 day	This was treated a separate process by the customer organization as it was done by people with special access to the environment that included senior management and few Enterprise and solution architects
Production (Deployment)	1 day	1 day
Total Cycle Time	18 days	10 days

 

Thus, you saw the difference in cycle time getting reduced. The SSBB consultant did a commendable job in bringing the cycle time from a 3.5 business weeks to 2 weeks, which was a great achievement

Reimagining how this process can be done using AI

Now in the current technological ecosystem, if we were to use AI to solve this problem, how can we do this.. There are multiple ways to approach this IMHO. There is no right or wrong approach

AI based Solution:

As an AI enthusiast,.  Few things that I will do are

1.        I will look at the problem statement first -

a.        Delay in deliverables (addressing Cycle time)

2.        Then capture the following details.

a.        Understand whether it is single vendor or multi-vendor project

b.        If multi-vendor, then who is owning which stage of Software Development Life Cycle (SDLC)

c.        Understand how workflow is happening as of now (AS-IS) based on the response provided by the user (some way of understanding the ecosystem is needed either observation or information feeding)

 

3.        Based on that will create an AI solution that will throw a Value Stream mapping model which will show what are the steps thats producing waste and what could be potential root causes and how to address them.

These are examples for more of a Chatbot/conversational ways of supporting the humans when AI   agents or AI systems do not know your challenges

AI based re-imagination of the problem/challenges

Now imagine that you know the problem statement and you also know the root causes (lets say you are in ‘analyze’ phase). At this point, when you want to use AI to address your challenges, then AI becomes a real game changer. Lets us see how those 3 key challenges are getting addressed with AI’s help

1.        Code Review – Using an AI tool like Codeium or any AI tool used for development, you may seamlessly more or less eliminate the need for a reviewer as it helps you in refining your code to the maximum hilt, though you may still need a reviewer (but the dependency is limited and time saved overall is much higher). This can happen from few days in the past, to few hours/minutes

 

2.        Testing in Staging – Based on the context and the product vision and roadmap, AI tools can provide SMART suggestions, saving in collaboration time amongst members from different vendors

 

3.        Technology Upgradation – An AI tool like GitHub CoPilot or any AI tool, may have the capability to identify or help developers in version compatibility issues and this can result in tremendous amount of time saving (from days to hours/minutes).

 

This clearly shows how AI can help us to resolve our challenges which we are dealing on days to perhaps, in hours/minutes.  

 

       Few things that we can learn from this whole discussion:

1.        Traditional way of working involves lot of processing time and waiting time (often in days) while doing the work, whereas AI processing time and wait time (between the steps) either do not happen or reduced to hours/minutes (in some cases)

2.        Standardization of   

Conclusion:

In this context, I find that reimagining the process with AI can be beneficial as we would be dealing the aforementioned challenges in hours/minutes and not on days. Therefore, I would recommend to the SSBB consultant in trying out this approach.

I have personally heard and seen, from many of the projects that Code Development (as an example) is rapid and it takes very few hours(even minutes in some projects) to complete the code quality, code refactoring, unit testing, review etcs.. for all requirements that are available, say in a code branch. 

 

Having said few cautions to be made,

1.        AI Usage should have a proper governance

2.        Ensure mandatory regulatory compliance is met

3.        Ethical AI usage has to be followed

4.        intent of AI usage should not be focused on workforce reduction rather improving the processes

5.        Psychological safety of the employees is important for AI tool usage to be effective. Else Employee emotions/sentiments may run high

6.        AI Upskilling required for AI tools to be effectively used

 

-           

 

 

 

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

Process improvement is required when the process is well defined, documented and working well. However there could be instances e.g. in Supplier onboarding process chances of manual data entry error or error in validation of supplier document could occur. in such situations, process improvement can be kicked off using lean six sigma tools or using OCR to capture and validate the content of a document.

Process reimagining with AI in situations where process is very old, and require frequent adjustments to suit to current products/deliverables. AI capability can be used to transform the current outdated process by utlizing available innovations/digital capabilities like smart workflows with robots/ML to handle manual regularly performed tasks or smart iOCR technology to validate the authenticity of supplier provided document, validate the content using 3rd party tools, taking action like rejecting or accepting a supplier onboarding and making outcome/insights available to stakeholders as smart customizable analytics.       

AI-Enhanced Lean Six Sigma: Transforming Travel Industry Operations

The travel industry can enhance operational efficiency, customer satisfaction, and competitiveness by integrating Artificial Intelligence with Lean Six Sigma methodologies.

We will explore how AI can enhance process improvement frameworks for superior operational excellence.

Let’s first look at processes that have over the years been optimized using Lean Six Sigma Methodologies in the Travel Agency Operations.

A.      Traditional Lean Six Sigma Optimized Processes in Travel

Process	Application of Lean	Application of Six Sigma
Booking and Reservation Systems	- Reduced booking errors through standardization - Eliminated redundant steps and simplifying the booking flows to reduce abandonment	- Measured booking error rates - Reduced variability in confirmation accuracy
Customer Service Resolution	- Developed standardized scripts for FAQs (SOPs), by mapping customer query resolution process bottlenecks to improve FTR rates - Applied Kaizen for continual service enhancements	- Analyzed defect rates in complaint resolution - Reduced customer wait time variation
Supplier & Partner Coordination	- Eliminated duplicated procurement tasks - Optimized contracts through Just-In-Time practices	- Measured variance in supplier performance - Used SPC for delivery time consistency
WFM and Demand Forecasting	- Standardized and continuously improved SLAs for important metrics resulting in better C-SAT’s - Analysis of call volume patterns for more accurate forecasting of staffing needs	- Improved service quality using VOC, RCA + CTQ metrics allowing for targeted solutions - Reduced defects in service delivery through streamlined processes allowing for increased customer loyalty and improved employee performance and job satisfaction

 

 

B.      AI and Automation Integration Opportunities

Though Lean Six Sigma offers a solid base for process improvement, AI and automation can significantly enhance performance. AI analyzes large datasets in real-time, identifies patterns unseen by humans, and automates complex decisions, overcoming traditional methods' limitations in a dynamic environment.

 

Process	Traditional Method Shortcomings	AI/Automation Integration and Rationale
Hotel Operations	Guest personalization is often based on broad segmentation (e.g., business vs. leisure) and past stays, lacking real-time context. Operational decisions like staffing are based on forecasts that may not capture sudden demand shifts.	Hyper-Personalization and AI-Driven Revenue/Operations Management: AI can analyze booking history, social media sentiment, and real-time preferences to create dynamic guest profiles for personalized experiences (e.g., adjusting room settings upon arrival, suggesting activities based on calendars). AI-powered revenue management systems can adjust pricing in real-time based on competitor rates, local events, and demand forecasts, while optimizing staffing and inventory levels.
Booking & Reservations	The booking process, while simplified, is still largely a one-size-fits-all search experience. The options presented are based on explicit user inputs, not on their underlying intent or preferences. Traditional Process: Search → Compare → Select → Book → Confirm	Conversational AI and Generative Itineraries: AI-powered travel assistants, can move beyond simple search queries by offering Predictive booking, Dynamic bundling, Contextual pricing and Autonomous rebooking. For e.g. A user can state, "I want a relaxing beach vacation for a week next month for a family of four with a budget of $5,000. We like historical sites but need kid-friendly activities." The AI can then generate a complete, bookable itinerary with flights, hotels, and activities, saving hours of planning. This transforms the booking process from a transaction to a personalized consultation with the pricing based on value perception and urgency. AI automatically adjusts itineraries for disruptions allowing to minimize the recovery time from hours to minutes in the event of a disruption.
Customer Service	Human agents, even when well-trained, have limitations in their ability to process vast amounts of information instantly. Escalations and wait times are still common for complex issues. Traditional Process: Issue occurs → Customer complains → Investigation → Resolution → Follow-up	AI-Powered Customer Service Agents, Proactive Support and Sentiment-Driven Intervention: AI chatbots and virtual assistants can handle a vast majority of routine queries instantly and 24/7 with multi-linguistic capabilities. More importantly, AI can analyze a customer's journey, and identify issues before customers experience them. For example, if a flight is delayed, an AI agent can proactively rebook the passenger on the next available flight, book a hotel room if needed, and send them a notification with their new itinerary and a meal voucher—often before the customer even thinks to call. This shifts the paradigm from reactive problem-solving to proactive care. Enabled with Real-time emotional analysis triggering preemptive support, AI learns from each interaction to prevent future issues. This leads to Continuous Experience optimization and increased Customer Lifetime Value.
Supplier Performance Management	Patterns with respect to SL’s may be missed by Six Sigma due to real-time, large-volume data.	Predictive analytics allows for dynamic pricing and risk analysis
Demand Forecasting	May struggle with external variability (weather, geo-events, market conditions, economic indicators etc.). May result in unnecessary overstaffing or understaffing.	AI/ML Models are more accurate based on real time analysis of booking patterns, pricing data and external factors to predict demand, thereby optimizing revenue. AI-powered predictive analytics ensure that the right number of agents are available at the right time.

 

C.      “Reimagining” Processes with AI: The Future State

Process	CRT	AI-Driven Future Workflow (FRT)	Human Role
Reimagined Reservation Management	Rigid booking flows Static packages Manual upselling/cross-selling	Dynamic Personalization: Based on user behavior, preferences, and contextual inputs (e.g., weather, search trends). Conversational Booking Agents: AI assistants manage the end-to-end booking process through voice/text. Predictive Pricing Models: Suggest booking times with optimal pricing—integrated with yield management systems.	Focus shifts to high-value concierge services, managing escalations, and curating experiences.
Reimagined Customer Service Resolution	High variability in service levels Long queues during disruptions Language & cultural mismatch	AI Chatbots: Resolve 80%+ of tier-1 queries autonomously. Sentiment Analysis Engines: Detect tone/mood and reroute dissatisfied customers to human reps proactively. Voice Analytics: Train agents with real-time feedback on empathy, tone, and script adherence.	Focus on complex resolutions, emotional intelligence-driven interventions, and training AI systems with context-rich feedback.
Reimagined Supplier and Partner Management	Manual scorecards Limited predictive capabilities One-size-fits-all contracts	Predictive Analytics: Evaluate supplier risk in real time (e.g., geo-political, environmental). Contract Automation Engines: Optimize procurement based on performance data and market conditions. Blockchain Integration: Enhance transparency and streamline payments/reconciliations.	Strategic partnership development and data interpretation for competitive advantage.

 

 

 

D.      Balancing Lean Six Sigma with AI Transformation

The integration of AI with traditional Lean Six Sigma methodologies represents a paradigm shift in travel industry operations, offering unprecedented opportunities for efficiency, quality, and customer satisfaction improvements. While traditional methodologies provide essential foundational frameworks, AI integration addresses critical limitations including slow adaptation cycles, static process optimization, and scalability constraints.

Dimension	Lean Six Sigma Focus	AI Re-imagination
Process Optimization	Eliminates waste, reduces variation, improves flow	AI makes real-time, predictive decisions to improve precision and adaptability
Customer Experience	VOC, CTQ, root cause analysis	AI personalizes, contextualizes, and scales service across platforms
Decision-Making	Data-driven but retrospective	Predictive and adaptive decision-making in real time
Workforce Role	Task-driven, standardized	Strategic, creative, empathetic, and high-complexity problem-solving

                       

 

Domain: Air Cargo Logistics

Process: Door to Door delivery.

Sub process: Picking the material from customer > Review and complete the documentation > Delivering to nearby warehouse.

Traditionally lean and six sigma is used for identifying delays, reduce wait time, optimizing the needs for additional vehicles, routes and identifying bottleneck during peak demand times.

 

Currently most of the times it is reactive problem solving.

Example : There will be delays in pick point where additional documents needed for transporting exceptional medical supplies need to be cleared. Even though clear process guidelines are shared still 10 to 15 % mismatch occurs.

Here more than improvement we can reimagine by deploying AI Powered Agent which can support customer on getting the documents filled by getting required data as attachments.

Sample Solution could be, customer can provide(drag & drop) material documentations available with him ,AI powered agent can fill the required forms and documents required for custom clearance and request for additional details or support for missing information’s and share the final document for review .

When a process is not well defined, inconsistent and not standardized, then process improvement must be done. Use of AI will not be effective in such cases.

AI can be used on standardized, consistent processes to optimize, automate and transform

When we understand that a standardized process is saturated in terms of capability, capacity, efficiency and effectiveness and can no longer transform from its existing state, then we can reimagine new ways of working to transform the process using AI..

In my organisation, when an issue arises in ERP, the end user captures the error details and raise a ticket manually. The ticket automatically goes to support team and will be assigned to someone. The support executive will analyse the prob and fix it, record the resolution in known error data base(KEDB)then asks confirmation from the end user and closes the ticket manually. This will take approximately on an average 4 hours per ticket,. If that issue is fixed but root cause is still not found, then it will be parked as a CI initiative and will be worked upon by a different team, who will do RCA and  fix it permanently.This will take approx a month. This process of raising a issue , resolution ,RCA and closure of ticket is a well defined process. To bring about improvement in this process is possible by automating the resolution of known issues by designing few automated orchestrations that help support executive ..but it will not result in radical transformation of the process.

Ideal process is that which has no errors which is not probable in real world.

So to transform the process of issue resolution the following can be done

Integrate an AI agent with the ERP which has cognitive ability to analyse and fix the issue permanently, without having to go through the current laborious process

This AI agent will be trained with the KEDB ( Known error data base for the past 5 years) so that it can analyse and identify the root cause and have the power to correct.

So the imagined process would be like the following

When an user gets an error msg, automatically the AI agent window pops up and asks if this issue needs fixing. If the response is Yes, AI does the analysis and communicates the root cause and asks for a confirmation to proceed to fix.If yes the issue will be fixed based on the KEDB and informs the user to proceed.

 

This way, the user need not spend time to capture the error msg , raise a ticket and wait untill it gets resolved..Every known errors can be fixed with couple of minutes..This makes the process more productive, clears up time for the user to focus on his job and the support function need not have too many people to do breakdown maintenance, instead they can spend time to design and develop more intelligence to train the AI agent to make it more robust..

I have had an experience to work on a CSAT improvement project with my mentor. We started with the regular approach of VOC translation to identify the CTQ metrics and then understand the most impacted point and then prepare a project and control plan of improvement. With time the activity of VOC translation turned out to be a regular process for quality assurance team and prepare a reporting for leadership. This is when the entire process was re-imagined to be used with AI Automation to utilize the bandwidth of resources to other critical areas. 

 

We had identified the "Keywords" enabled Sentiment Analysis to determine the CTQ and then automated the reporting. The manual process of performing each VOC has been reduced by a big margin. 

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.

The answer to this question lies in the needs and appetite of the business, which can range from incrementally improving a process, to redesigning it for breakthrough improvement, or even radically reimagining it using AI.

The success of such actions depends not only on what the business needs and is ready to invest, but also on the maturity of the process. Without this, one risks making the rookie mistake of improving the waste.

In any industry, technology should follow business strategy—not the other way around. To quote David L. Rogers:
“Don’t simply chase technology—focus on transforming your strategy, culture, and operating model. Use digital tools only to solve prioritized problems, validate them with experiments, and scale what demonstrably delivers value.”

It is imperative that technology-led interventions be introduced only when they enable strategic outcomes, enhance customer experience, or drive operational efficiency—especially when incremental improvement or re-engineering fail to meet strategic requirements, and when investing in technology (particularly AI) makes clear business and revenue sense.

Processes can be incrementally improved when they align with strategic goals but suffer from inefficiencies. In contrast, AI-led reimagination is warranted when customer needs, delivery models, or value propositions have fundamentally shifted.

Customer service ticket triaging and resolution - This process traditionally optimized using lean and Six sigma but now its ready to be re imagined with AI.

Customer service doesn't need faster handling, it needs smarter and proactive resolution.
Instead of using the traditional method, AI transform how the system understands and resolve tickets:

* AI powered auto triage - NLP can understand the urgency of the ticket and resolve it instantly.
* Self resolution BOTS like ChatGPT.
* Sentiment analysis by detecting customer frustration if the words are not direct.
* Proactive support by analyzing pattern in customer issues.

Process can be improved when:

 

1. There are repetative tasks and standard process exists withiout significant exceptions or deviations in the tasks. 

2. There are lot of manual intervention required which can be processed or automated with the help of a tool.

3. Process tasks are measureable in terms of time, savings, etc.

4. They are rule based which can be understood.

 

Example:

 

Automating bank reconciliation process by applying formulas / VBA Macros to eliminate maunal tasks of matching trasactions one by one.

 

 Process can be re-imagined when:

 

1. It is complex in nature and lot of exceptions are involved.

2. When there are dependant real time sources involved while performing tasks.

3. When existing process does ot meet the expectations / required / correct output.

4. When performing tasks by a human is significantly time consuming with copromise on quality.

5. When external enviorment / market is evolving but processes we are following are out dated.

 

Example:

 

Market analysis report before launching a new product which can be comparitively easier to prepare, less time consuming, more detailed as comapred to prepared by a consultant. 

 

When should a process be improved?

Improving the process when the process is working but inefficient due to being outdated.

Deliver meaningful value with incremental gains

Optimize a process through automation

Regulatory requirements need to be improved

Example: Voice-controlled phone in cars

 

Reimagine a Process with AI.

If a process does not meet the current fast-paced environment and heavily relies on humans, it is error-prone.

To achieve results and performance which was not attained previously, e.g, Predictive analysis, personalization, and decision making.

User experience needs an overhaul, and 24/7 availability to improve user experience

New way of doing business.

Example: Using  AR Smart Glasses for Maintenance and Operations

The efficiency of Processes in a Company often defines its success in terms of better customer experience, faster services and a better quality

 

The traditional methods of improvement of processes involves digitalization and rule-based automation; but with growing business complexity these approaches can struggle.

 

In these cases, AI driven approach can be used. With machine learning and generative AI organisations can analyse vast amounts of data to continuously improve the processes and find out the inefficiencies. This would have been impossible with the traditional approach.

As a result, AI could be used not just to improve work processes but to reshape how the work is getting done. It will help systems to get smarter with time and not just make them to run faster.

 

To find out the Processes that are good for AI driven approach

·        Discover the processes that give greatest potential for AI driven gains For eg : processes that show delays or bottlenecks or repetitions

·        Select processes that will give genuine impact on the finances

The benefits of AI integration could be

·        Increased efficiency

·        Reduced errors

·        Better customer experience

·        Cost reduction

·        Better decision making due to use of data analytics

Examples in which processes could be reimagined with AI could be

·        E-commerce

·        Manufacturing and supply chain management

·        Health care

So, if Companies are looking at achieving  long term growth, improve decision making , give a better experience to their customers and stay ahead in today competitive digital landscape then I think such companies should definitely start to reimagine their processes with AI instead of looking at improving it with a traditional approach.

In one of my process improvement IT project, we had used FMEA, Fishbone diagram and Pareto analysis to understand which are the major problems/issues faced by 7 IT work streams by interviewing key stakeholders and understanding the most important 20% issues which led to 80% failures. This was a very time consuming activity to gather and analyse the root causes of problem and discuss the points in meeting to find solutions as part of process improvement. 

Now, in AI world for reimagining the solution, we can automate and use conversational AI using Agentic chatbots to ask relevant questions and answer the questions using effective AI models which can save time and provide better outcomes to our stakeholders.