Q 780. As AI adoption grows, Business Excellence MBBs and AI Solution Architects often find themselves working on the same transformation — but speaking different languages.
What specific role should an MBB play when an AI-powered solution is being developed for a critical business process? How can MBBs ensure that AI initiatives are aligned with process excellence, customer value, and organizational priorities?

 

The best answer will be selected on the basis of: 

•  Clarity in defining the MBB’s value-add in AI projects  
• Practical strategies for collaboration with AI teams  
• Insight into bridging the mindset and skill gap

 

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MBB should act as an intermediary between AI architecture and the Business. MBB should ensure that all AI solution is grounded in customer value and a measurable outcome for the business. 
MBB's role is to bring clarity on the existing processes, stating why the process exist, where the roadblocks are and how the success should be measured.
MBB, should

1. first clear process baselines before AI is developed.
2. ensure that the AI truly brings value
3. ensure feedback is collected after AI solutions have been deployed.

This is a fantastic opportunity for Master Black Belts (MBBs) to take lead and increase their influence by reinventing their role.

MBBs’ Unique Value-Add in AI Projects

MBBs infuse six sigma consistency, precision, and accuracy into the innovative disorder of AI

Effective strategies for collaborating with AI teams

Acting as a partner, focusing on value, rather than a controlling gatekeeper. Facilitate workshops with process owners and data scientists to ensure all are on same page related to objectives

Insight into bridging the mindset and skill gap

AI teams usually work with a "data-first" mindset, whereas MBBs do a "process-first" approach.

The AI teams Co-Create and Business Excellence Experts (MBBs) must collaborate from the beginning (not in silos or as hand-off partners) in order to provide better solutions for today's complex organizational issues. MBBs are aware of how tasks are completed. They concentrate on enhancing the functioning of people, systems, and procedures in order to address real-world issues. They pose the appropriate queries: What is the objective? What's causing the delays? What is generating confusion or waste? AI teams, on the other hand, provide the means to address these issues in novel ways by automating repetitive processes, accelerating decision-making, and predicting potential outcomes using data. However, AI is most effective when it is directed towards the correct issue, which leads to the discovery of quicker and more intelligent solutions. They may create solutions that are both inventive and practical, based on actual demands and supported by cutting-edge tools, if they collaborate from the start.

 

How they can work better together?
1. Begin with a common objective: MBBs and AI teams should jointly and concisely characterize the issue. 'Why' it matters is as important as 'what' has to be fixed.
2. Make use of actual facts, not conjecture: MBBs are able to identify process gaps and discrepancies. Data from such domains may then be used by AI teams to create clever solutions.
3. Build gradually rather than all at once: Begin modestly. Put the concept to the test in actual settings. While AI teams modify the model or tool to match what is effective, MBBs assist with feedback and outcome tracking.
4. Pay attention to people rather than simply technology: The best solutions are those that people use. MBBs are adept at leading change, developing teams, and ensuring that novel concepts are retained.
5. Continue learning along the way: Co-creation is a continuous process. It's important for MBBs and AI teams to maintain communication, continuously refining the solution, and facilitate scaling.

The true benefit occurs when the astute powers of AI combine with the practical expertise of MBBs to transform business challenges into significant, long-lasting advancements.

 

What Does Co-Creation Look Like?
1.Identifying the true issue should come first. MBBs are able to pinpoint instances in which a process is inefficient, inconsistent, or sluggish. In addition to solving technical problems, they also assist the team address the 'right' problem.
2. Utilize the appropriate data to comprehend the situation: AI teams may examine the data to identify trends, and MBBs assist in interpreting the data's meaning in the context of the actual world.
3. Test and collaborate to improve: Take little actions to build solutions. Pilots or trials are led by MBBs, who also assess the results and make adjustments depending on what is effective. In response, AI teams improve models or tools.
4. Design with humans in mind, not just machines: If no one uses an AI tool, even the most intelligent one will fail. MBBs make ensuring that the solution works with people's actual workflows.
5. Continue to learn and adjust: Over time, both business procedures and AI models require fine-tuning. Co-creation is a continuous collaboration that continues after a project is launched.

 

Real-World Example: Cutting Down on Manufacturing Plant Delays

• The problem: The production line of a sizable manufacturing business had regular delays. Delivery deadlines were being missed as a result of machines halting suddenly.
• MBB's Role: During a process analysis, an MBB discovered that equipment failures were occurring more frequently during particular shifts, but the maintenance team lacked a discernible pattern to follow. The MBB also found that planned maintenance did not correspond with the real patterns of wear and tear.
• Role of AI Team: Using data from machine sensors, the AI team developed a predictive maintenance model. Before a failure happened, it detected early warning indicators and sent out notifications as necessary, minimizing unscheduled downtime.

The Result: 
Together, they revamped the maintenance procedure,
- Replacing set timetables with predictive warnings
- The signs for the maintenance crew were clearer and earlier
- Delivery on time increased by 25% and downtime decreased by 40%

 

This achievement was a result of both the AI model and the cooperation of the MBB, which made sure the solution was workable and in line with operations, and the AI team, which provided the technical understanding.

 

Conclusion: When AI teams' potent tools are paired with MBBs' extensive process understanding, great solutions are produced. It's about collaborating to create something better, quicker, and more beneficial than either could achieve on their own, not about picking one over the other.

AI adoption is a key focus area for most companies today if not all. In this scenario it has become very common that MBBs and AI Solution Architects often end up working on the same transformation initiative. Although both the teams have different approach to the same problem, here are a few strategies which MBBs can follow to ensure that AI initiatives are aligned with process excellence, customer value, and organizational priorities –

1.)    Business Case – Ensure every AI initiative is has a clearly defined business problem and is aligned with the strategic objectives of the organization.

2.)    Embed Process Excellence Methodologies –

a.      Apply established methodologies like DMAIC (Define, Measure, Analyze, Improve, Control) or DMADV (Define, Measure, Analyze, Design, Verify) to the AI development lifecycle.

b.      Utilize Value Stream Mapping (VSM) to identify areas of the process where AI can be applied.

3.)    Customer Journey Mapping - Create a map of the customer experience and pinpoint the precise points of contact where AI may enhance communication, lower barriers, or provide tailored value.

4.)    Establish a robust governance and collaboration framework with shared goals regular review/tollgates

5.)    Benefits Realization Management- Establish a structured procedure to monitor and document the true advantages of AI projects, guaranteeing responsibility and proving their worth.

I am not an MMB, so obviously I cannot speak with specificity or be a voice of authority of one who is.  But I would imagine this would be no different than an MMB being involved with other project or solution they would find themselves in. There's always been language barriers between those who work in the front of the business and those who work in the back of the business.  But they should both be driving and measured by the same objective and that is, at all times, to be customer-centric.  To try to answer this question, I had to do a reading on what an MMB role is.  I find that I functioned in that role, to a large degree, all my career and I attribute it as been one of the key factors in my success.  So, here is how I would answer the two part question.

1. MMB role in AI projects:

   Pre-Implementation:

     Define current process, uncover the pain-points, map the value streams, translate business problems into AI solvable problem, keeping the project always customer or business-centric, project requirements gathering, defining the metrics where the AI initiative can be measured as a success, identifying operational risks, ethical considerations, business continuity issues relate to the AI solution

  Implementation:

     Change management, preparing the organization for the new AI-powered  process, identifying operational risks, ethical considerations, business continuity issues relate to the AI solution

  Post-Implementation: 

      Helping to track and quantify the business value delivered by the AI solution against the initial objectives

 

2. Aligning AI initiatives with process excellence, customer value and organizational priorities

    Communication, communication, communication:

        - Translating between the two domains.  Being the "Rosetta Stone" between the business and technical teams

        - Holding joint workshops and helping each domain understand each others terminologies and objectives

        - Use frameworks that integrate both business process mapping and AI solution design

        - MBBs should be involved from the very inception of AI initiatives, co-creating with AI Architects, rather than 

           handing off requirements

       - Keep the initiative focused on providing the solution for the business problem, not just the technology

         and developing business cases that clearly articulate the ROI of AI solutions

       - Implementing governance structure by implementing steering committees from both business and technical sides 

       - Create a structured decision-making process for AI projects

       - Use Lean Six Sigma tools to manage the AI projects to keep things structured and towards continuous

          improvement

       - Conducting process mapping before AI implementation to identify optimization opportunities

       - ADVOCATING FOR CUSTOMER AND END-USER.

The MBB and AI team shall make a great synergy, MBB bring process improvement, statistic and analysis whereas AI team bring automation, forecasting and prediction. MBB and AI team together defined the problem with more precise way which is very much needed for process improvement, clearly defining the problem means we achieved half solution.  AI and MBB can together define the problem in more precise way by analyzing large volume of un structed data. So the AI model shall finetune the problem statement through data driven approach.

 

MBB use tradition /  standard statistics to do root cause analysis, With the help of AI we can analyze mass data and uncover hidden pattern. The both approaches provide clear root cause analysis to narrow down the causes. Process mapping is one of the important step  in process improvement projects, Here MBB will decide with AI team how to automate the system, remove the redundant process, remove NAV. These all shall be done through AI team with the help of MBB. Computer vision can be used to inspect and collect data to speed up the process and to have accurate data during data collection stage, however there are some limitations.

 

Combination of AI team and MBB can perform advance statistical analysis, the output shall be interpreted by MBB for final conclusion where AI team play a key role to analyze mass data, AI team also suggest RPA (Robotic process automation)   to improve the process flow and speed, Such a RPA incorporation shall improve the throughput drastically with high precision. AI team has to have knowledge on Lean and six sigma practices, VSM, business pains and  and  adverse VOC whereas MBB shall know more about AI process, automation, higher volume data analysis and the limitation of AI. Team effort and training mutually between AI team and MBB shall bring more synergy to the organization. 

 

Gathering right data is key for analysis, The MBB can guide AI team for data collection strategy, sampling plan, governance to train the AI model to deliver unbiased result. This gap shall be minimized when they have common goal to achieve.

MBB can train AI on management of change, governance, defining the business problem where AI can train MBB on tools, algorithm and  automation  to have AI driven solutions.

 

MBB always look for opportunity to improve, The focus of MBB is what is the problem and why it is considered as problem, where as AI team always look for how to resolve the problem with appropriate way with the help of automation, AI tools, specific algorithm. so Combination of AI team with MBB or the individual having both expertise is great for organization to continually improve their process. The process improvement means less defect, improved quality which lead to high customer satisfaction and in turn it improve the market share drastically. 

 

 

 

Solutions have to be with today’s pace. They need to be more automated and less human dependent.

Initially data analysis like running normality test, or statistical inference studying trends  Ai can help and faster the process. It can also help with similar case studied and proving better solutions.

 

AI integrated black Ely would be a great imitative to process excellence

An MBB act as intermediary between the business requirement and AI capability. they act as a translator between the customer need and technology capability to deliver the same. MBBs role is important to ensure that applied AI solution is not just technically well but operationally apt as well and based on customer’s verbatim and requirement and strategically aligned.

 

They understand the business problem statement, articulate it well. 

 

They validate that developed AI solution fulfil user’s requirement and apt for their usage and not only technical

 

They help in getting adoption of the new solution and ensure the AI solution is aligned with existing standards

 

They take care of risk assessment and plan risk mitigation for the developed new solution. they ensure to deploy the relevant control plan for the sustainable solution and do not introduce any new risk in the process

 

In order to ensure that AI initiatives are aligned with process excellence, customer and company’s priorities, MBB should follow below mentioned points:-

1.      It should start with process and it’s core problem. the project that donot target the business problem improvement should be challenged and questioned

 

2.      Include process owner and AI technology folks and end users from the beginning to incorporate all view points and calibrations.

 

3.      Use these lean six sigma method to carve the structure for AI project by reflecting the clear problem statement, accuracy in measurement of KPI, root cause analysis and sustainable control plan

 

4.      Reflect AI solution as an ongoing and continuous improvement enabler and not as one time invention. Reflect the progress in daily calls/huddles and dashboard

 

Master Black Belts primary responsibility is to serve value to business and that starts from drawing the voice of business and requirements. Master Black Belt when working with stakeholders should act as a business consultant and start recording the voice of the business. Once recorded should align the VOB with Organizational OKR's and identify the delighters. The competency of Quality Tools like Kano Analysis to record VOB and categorize the delighters helps. Once done, the MBB could use the expertise to define the scope and identify the critical metrics which could drive value , reliability and process fit.

 

Deployment of AI solutions in the Pilot & Implementation phase of DMAIC, MBB plays their role in defining the business case, collect correct (best) data for measurement, define the metric, identify the top most error contributing factor correlate it with value and then work with stakeholders to plan the solution. Coordinate with the Transformation team or AI solution architect explaining the business pain point and together create BRD's. 

 

During the Pilot and Implementation phase, MBB could study and analyze the outcome data, give best class suggestion for improvement (if any). MBB could create the control plan and add AI solutions to the plan ensuring periodic revision of logics and fine tuning is performed. 

 

Lastly MBB could create a governance system through reporting and keep re-evaluating the improvement with time. 

MBB and AI can work in good combination.From MBBs we can use tools like DMAIC and Fishbone diagrams and we can use AI tools for data collection and mining.
  MBBs define CTQs (Critical to Quality) and process KPIs and AI models can *predict defects or process deviations* in real-time, enabling proactive interventions.

The ways in which AI Team and MBB can create a better solutions are:

1. There should be clear roles and responsibilities divided between AI Team and MBB's so that they can work in a synergy and can contribute more. This can often prevent from conflicts between two and overall help in utilizing skills from both of the teams or individuals.

Following are the Role and Responsibilities of a MBB and AI Teams are:

MBB

1. Focus should be on improving and analyzing process for optimization and value generation.
2. Customer value should be on priority.
3. To align AI solutions with organization's goals.
4. Focus on structured problem solving.
5. Value generation for business in terms of financial success as well as customer outlook.

AI Team

1. Focus mainly on solution architecture.
2. Overall technicality of the solution.
3. Idea generation for problems in brainstorming sessions.
4. Implementing AI solutions.

2.    Proper Communication between both the sides by means of workshops, brainstorming sessions, so that they can have an idea of each others zone and the gap can be bridged between two. This will create a synergy between two and help the organization to be more efficient.

 

3. Focus on each others strength, As MBB's are more skilled in mapping the process, defining problem statements or analyzing process and identifying hidden problems and bottlenecks from the process. Using data for creating meaningful inferences and validating benefits for value realization and on other hand AI team is skilled for generating idea for automations and creating AI solution architecture for problems. They are skilled in implementation of solutions and providing technical support wherever required. So both the skill sets are important for organization to carry a sustained and a profitable business.   

The specific role of business excellent Master Black Belts (MBBs) during AI-Powered Solutions that build it for critical process like customer care are the following:

1) cooperation with Process Expertise:
MBBs should listen and use process expertise during the design/Mapping
Because they are fully understanding operations/Process
So, their recommendation/thought are very important during build AI-powered solutions for critical process

2) Setting clear Requirements for each critical process:
MBBs should fully understand Objectives/key performance indicators (KPIs) for critical process and then monitoring AI-powered solutions to ensure matched the requirements and goals

3) Ensure AI Solution Alignment with business strategy:
MBBs should received the approval for design/Mapping and AI Solution from the following before start/release:
Key person of process excellence
Key person of critical process
Key person of customer care
And top management

MBB should explained to the organization the following so they can support him:

- How AI-powered solutions can improve the following:

increase efficiency
reduce waste and cost
enhance/improve the quality

- MBB can validate AI-powered solutions and prove that Solution meets customer needs, process goals 

- monitoring the solution design/Mapping

- provide reports and evidence for improvement using dashboard and comparison graph

- can conduct survey (voice of customer) to prove the improvements for critical process

MBBs or Master Black Belts drive excellence using Lean Six Sigma framework and methods. MBBs are experts in implementing data and LSS frameworks to identify and improve wastage or inefficiencies in a process, driving change and transformation, and ensure the improvement is sustainable. AI Teams are experts in Gen AI capabilities, automation tools, predictive modelling using big data and advance analytics. They identify tools and capabilities to implement for a given use case.

MBBs and AI teams can do wonders when they work together. One can identify problems, use data to validate the RCA and solutions, and for the same problem AI team can implement appropriate solutions to make it faster, leaner, bolder and scalable.

For example, in SCM quality from organizations' suppliers is very critical to monitor and govern as poor quality leads to delay in production and increase in cost. Here MBB can apply root cause analysis (like Fishbone or 5 Whys) to trace defects originating from a specific supplier or process. And AI Team can create a predictive model that identify and flags suppliers which might cause problem wrt poor quality using historical performance data.

Answer: MBB’s plays crucial role in lean six sigma world. The key role of MBBs to identify the areas that needs attention and reduce process variability through applying two most important approaches i.e. DMAIC (Define, Measure, Analyse, Improve, Control) and DMADV (Define, Measure, Analyse, Design, Verify) – which gives a structured approach to solving problems.

Merger of both AI with Six Sigma provides a transformative platform to process optimization and improvement. During the development phase of AI-powered solution for critical business process, MBBs plays crucial role as outlined below:

1)    For strategic alignment which ensure translation of business issues into data driven opportunities.

2)    Identification of critical pain areas and provide guidance to AI tools like Failure mode effect analysis and SIPOC (suppliers, inputs, process, outputs, customers).

3)    Work together with data analytics personal to define different input factors and output responses and data cleansing approach.

4)    MBBs also provide direction to AI during integration and statistical model validation where different statistical tools like hypothesis testing, design of experiment, SPC and multiple linear regression are used to validate AI model assumptions.

5)    Integration of AI in the Six Sigma DMAIC which provides organizations to improve accuracy, effectiveness, and sustainability in continuous process improvement at primary phase when critical business process is under development phase. Let’s discuss with one example:

Define (D): During this phase, teach AI to identify the problem areas and project goals by utilizing advanced data mining techniques, such as clustering and dimensionality reduction algorithms like Principal Component Analysis (PCA). In addition to that, also teach AI to use NPL to improve customer voice analysis which allows automatic extraction of information from customer feedback found in social media, and online reviews.

Measure (M): In this phase requires precise and fast data gathering. Teach the AI in such a way that both organized and unstructured data can be gathered, like operational environmental parameters

Analyse (A): During this phase teach AI to speeding up cause analysis. Machine learning techniques like support vector machines (SVMs) can filter massive volumes of data to discover the important factors causing errors or inefficiencies resulting in faster discovery of root cause.

Improve (I): During this phase, teach AI w.r.t virtual testing before implementation into real operation.

Control (C): During this phase, teach the AI in such a way that sustainability of improvements can be enhance. MBBs has to create a universe in such a way that systems can automatically detect deviations if any parameter goes out of limit, enabling quick corrective actions. And accordingly Statistical Process Control chart can proactively update using on real-time information, so that manual interventions can be control.

· During Practical strategies for collaboration with AI teams, Its important to use simple language and try to translate operation, six sigma and business languages in such as way that AI can understand the such terminologies.

· Aligned the AI with business goal, so that understanding should be clear that what is the problem we are trying to solve and how success looks.

· It is important to teach the clear objective of organization like cycle time reduction and cost saving etc.

· We can provide training to AI team regarding lean six sigma and operational GMP procedure in order to bridging the mind-set and skill gap.

· Always use modern platform like Miro and Power BI with interfaces which are business friendly.

· Conduct regular workshop for both AI and Six sigma team, where different case studies and discovery sessions can be discussed.

Through considering all the above approaches, MBBs and AI team can co-create a better solution.  

 

Effective solutions can be created by Lean Six Sigma MBBs and AI teams by bring into line the business goals and combining structured problem-solving with data-driven insights. Lean Six Sigma MBBs add value by defining the scope and improve processes using DMAIC and other process improvement methodologies, whereas AI improves these with predictive insights and automations and deeper root cause understanding. Collaboration of both Lean Six Sigma MBBs and AI teams ensures effective hybrid solutions that improves process efficiency and intelligence.

MBBs and AI teams collaboration contributes to:

1) Better data-driven decision making can be enabled by using AI insights on frequent patterns of concern related and to make more informed decisions based on the data collected by Lean MBBs and prioritize the areas of improvement by identifying process inefficiencies and root causes of problems

2) Optimize the process by automating repetitive tasks identified by the Lean MBBs to reduce variability, eliminate waste and streamline processes

3) Improved Efficiency, Workflow and Productivity through automation based on process inefficiencies identified by MBBs

4) Enhanced Customer Experience by understanding customer needs, delivering value, offer personalize experiences and predict future needs using AI

Edited June 25, 2025Jun 25 by Dharanesh Mysore
Typo error

In this AI world, the role of a MBB should be like a coach guiding the team that builds the AI-powered solution for a critical business process

 

Imagine you want to go from city A to city B which is a considerable distance and now you are go in this combination - an Express Highway and a F1 race car. So how quick that would be. The combination of MBB and AI Solution Architect is akin to this!!  The MBB letting know what her thought process/ideas on doing a transformation and the AI Solution Architect complementing that thought process/ideas with concrete implementations

 

I personally saw the power of this in my AI enabled Business Excellence MBB program organised by BenchmarkSixSigma.com.  There were many modules in that program.  When there was a discussion on a Monte-Carlo Simulation problem, while doing in a conventional manner, the outcome took some time to arrive at for all of our batch mates in the program.

 

With the help of AI prompts (as part of the program), when the same problem was fed to an AI model (say ChatGPT), it threw good suggestive approaches which expedited us to get the right set of parameters (necessary for Monte-Carlo simulation) which can result in the right solution. This was a classic case of a MBB and an AI system working together.  Imagine that AI system being engineered by an AI Solution Architect!! This is just a proof of how a MBB and an AI Solutions Architect can work together and achieve great results

 

Imagine the MBB picks a transformation project that talks about cost reduction for the customer in one of its critical process. The work demands a typical Black belt project.  By conventional means, the Six Sigma Black Belt project would take quite some time. Shrewdly the MBB decides to leverage her colleague an AI Solution Architect. She tells him about the project needs and customer expectation.  He hears out the problem and comes up with a solution which is relevant to the ask. How he does that solutioning?

 

By providing an AI solution that focuses on

- Data driven insights

- Automation of process

 

With the above focus, he (AI Solution Architect) is able to

- innovate the organization,

- making the leaders/MBB (in this context) arrive at informed strategic decisions that can impact business outcome,

- reduce the operational costs involved

- Also, in general, the costs,  as we improve the overall efficiency & productivity of the system

- help the customer organisation to outperform its competitors through quick adaptation to changing market needs

- extracting valuable insights from data turning into actionable strategies which drives business success

 

This really helps her (MBB). 

 

During this transformation journey, there may be several touchpoints for both of them. For instance, if there is an insightful data (Coming out from the AI solution) that can bring a new perspective to the MBB. Then she may have a discussion with the AI Solution Architect.

 

To have some clear-cut strategy for a good discussion with an AI Solution Architect or to an AI based team would be

- It would be always good to have a cadence setup between the MBB and the AI Solution  Architect.

- Have good discussions with the AI team which can help the team to write better prompts for a better solution

 

By reducing the costs associated with the project, we can showcase the value that can be provided to the customer and also focus on satisfying customer needs and expectations, as part of our organisational priorities

 

There are few things as a MBB should do, to improve upon his/her AI knowledge.  

1. Learn basics of AI and also Gen AI which is good enough to explain your problem statement that you want to convert into an AI based system

2. Learn basics of Prompt Engineering and  

3. Understand fundamentals of ethical AI Governance and understand how it impacts organisational standards and Data Privacy compliance

 

As an AI Solution Architect (other than AI specific skills), you need to know

1. The vision/idea (intended purpose) of the problem statement and what is the expected outcome so that the solution can be more contextual and precise

2. Development of skills in preparing comprehensive System Requirements or Business Requirements document that will map the objectives of the AI based project with the business needs

 

Thus, you can see how a MBB and AI Solution Architect can get in sync with a transformation and can really speeden up the transformation and bring benefits to the customer.

 

So does this mean that you need a MBB and an AI Solution Architect together. Is it possible that a MBB also becomes an AI Solution Architect?  Well, you can be a MBB and also be skilled with AI Solution Architect just like how I plan to be now (thanks to the CAISA program).  This is similar to a Tennis player being the captain (of the squad) and also being a player while playing Davis/Fed cup. He/she will be knowing whom to select as player and also understand the strength and weakness of each player and also the playing conditions. Same way, the MBB with AI skilled would be able to understand which transformation to do and what to do and how to do it through AI. This is where these sort of programs – AI Enabled Business Excellence MBB and CAISA programs when leveraged effectively can be game changers.  

 

Reference Material Source for AI: Benchmark Six Sigma CAISA Course program

I see AI as a tool, and the role of an AI solution architect as an enabler to improve processes. The role of MBB/process consultant is still valid as they possess in-depth understanding of the process nuances. The AI tools can make the MBB role less prominent or diluted, as many statistical analyses/methodologies can be driven by AI. In that case, the MBB role needs to evolve from a hardcore process improvement expert to that of a value facilitator/integrator who ensures AI solutions are adopted across the right problems in the workflow/processes. MBB's in-depth knowledge of business processes still provides an upper edge to decide if the AI initiatives are aligned with process excellence, customer value, and organizational priorities. The unique value of an MBB in an AI project lies in their ability to bridge the gap between business strategy and the technical intricacies of AI implementation. Their role shifts from a traditional process mapping and optimization to become one of a 'value orchestrator.'

Practical strategy to collaborate with AI teams

1. Define & Design Phase—While MBBs can lead the identification of high-impact business problems (using DMAIC methodologies), AI architects can focus on the right AI solutions/models and architectural POVs/solutions.

2. Develop & Test Phase—AI solution architects can lead the solutioning and train the right AI models while MBBs identify critical data sources and elements required for the AI model based on process knowledge and ensure data quality and integrity from a business process standpoint.

3. Implement & Change Management—MBBs lead the change management, value realization, and stakeholder management. The AI team can lead the deployment and oversee the technical performance and stability of the solution.

 

 

Role of MBBs in AI Projects

MBBs are uniquely positioned to drive value in AI-powered transformations due to their systemic view of operations, deep expertise in process improvement, and proven ability to align cross-functional stakeholders around measurable business outcomes. While AI teams often focus on model development, data science, and technological execution, MBBs contribute by ensuring that AI initiatives:

• Are business problem-driven, not technology-driven.
• Target critical process performance gaps aligned with Voice of the Customer (VOC).
• Are embedded in repeatable, sustainable, and value-creating workflows.

The specific value-add of the Master Black Belt can be categorized into four key areas:

Capability	Contribution to AI Projects
Process Architecture and Problem Framing	Identifies where AI can reduce waste, variation, and complexity by deconstructing complex business challenges into well-defined problems that are suitable for an AI solution. Using tools like SIPOC and VSM, MBB can identify the precise process steps with the most significant bottlenecks, waste, or variation
Data Integrity and Bus. Context	AI models are entirely dependent on the quality and relevance of the data they are trained on. The MBB acts as the bridge between raw data and business reality. By conducting an MSA on the data collection process itself MBBs ensure accuracy and reliability, by asking critical questions like “Does this data represent the VOC? Are there known process shifts that would skew this historical data?” This understanding prevents the “garbage in, garbage out” pitfall that affects many AI projects.
Stakeholder Engagement	Bridges executive vision with operational execution through facilitation and influence.
Advanced Data Analysis	Offers statistical modeling and hypothesis testing skills to validate AI outcomes.
Benefits realization and control	MBB ensures that the AI solutions delivers measurable improvement against a KPI. MBBs are responsible for ensuring these benefits are not only achieved but sustained. The MBB designs the Control plan for the new AI enhanced process, for monitoring Critical Business KPI’s (the “Y”), and the key process inputs (the “x’s”) incl. the AI model’s output, to ensure incase the model’s performance degrades or the process deviates, there is a clear response plan.

 

 

 

 

Collaboration Strategies with AI Teams

Effective collaboration hinges on bridging the gap between the process-centric language of Lean Six Sigma and the technology-centric language of AI development. The MBB must proactively facilitate this alignment.

-          Establishing a Common Language: Misunderstandings often arise from differing terminology for similar concepts. The MBB can create a "translation matrix" to foster clear communication.

Lean Six Sigma Term	AI/Data Science Term	Collaborative Interpretation
Voice of the Customer (VoC)	Training Data Labels / Target Variable	The desired outcome or classification that the AI model needs to predict, defined by customer value.
Critical to Quality (CTQ)	Key Features / Predictors	The measurable process inputs that are hypothesized to have the greatest impact on the outcome.
Root Cause Analysis	Feature Importance / Exploratory Data Analysis	The joint exercise of using process knowledge and data science to identify the true drivers of a problem.
Process Control Plan	Model Monitoring / MLOps	A system to ensure the AI-powered process continues to perform as expected and trigger alerts for retraining or intervention.

 

-          Align Project Objectives and Success Criteria:

Begin AI initiatives with Define and Measure phases from DMAIC -

• • What problem is AI solving?
  • What is the current baseline performance?
  • What process KPIs and customer metrics will define success?
• Use CTQ Trees to connect AI model outputs to business-critical outcomes.

-          Integrate AI Capabilities into Existing Processes:

Use FMEA + Process Mapping to identify -

• • Where automation can replace manual judgment.
  • Where human oversight remains essential.
• Support pilot programs with structured experiments (e.g., Design of Experiments for comparing human-only vs. AI-augmented processes).

Example: In a sales forecasting process, use control charts to validate AI-predicted vs. actual demand across different market segments.

 

Bridging the Mindset and Skill Gap

Long-term success requires a cultural and educational commitment to bridge the gap between process excellence and AI.

• Fostering a Culture of Continuous Experimentation/Innovation:

The MBB can help shift the organizational mindset from large, monolithic projects to a more agile approach of continuous improvement. This aligns perfectly with the iterative nature of AI model development (build - measure - learn). By promoting a culture where it is safe to assess hypotheses and learn from failures, the organization can innovate more rapidly.

• Promoting Cross-Functional Training Initiatives:
  • For Master Black Belts: MBBs must become "AI Literate." Organizations should invest in training for MBBs that covers:
    • Foundations of AI/ML: Understanding the difference between supervised, unsupervised, and reinforcement learning.
    • AI Project Lifecycle: Learning the key stages of data acquisition, model training, and deployment.
    • Asking the Right Questions: Knowing how to probe an AI team on data sources, potential biases, model explainability, and scalability.
  • For AI Teams: AI and data scientists often lack deep context on the business processes they are trying to impact. MBBs can lead "Process Immersion" workshops that cover:
    • Gemba Walks: Taking the AI team to the "real place" where the work happens.
    • Value Stream Mapping Sessions: Helping the AI team visualize the end-to-end process and understand its complexities and constraints.
    • Voice of the Customer Reviews: Sharing customer feedback and pain points to ground the technical work in real-world value.
• Creating Integrated, Cross-Functional Teams:

Instead of having a separate "Process Excellence" team and "AI Team," organizations should form cross-functional "teams" or "pods" dedicated to solving a specific business problem and break down organizational silos entirely. An MBB should be a core, embedded member of such a team, working alongside the AI Architect, data scientists, and business stakeholders from project inception to completion.

 

 

Traditionally MBBs improve efficiency, quality and reduce waste but in AI powered transformation - we can redesign the workflows where AI does part of decision making and automation.

Below are the points for MBBs role in AI powered solution

* Bridge the gap between business stake holders, operations and AI architects, translates the organizational priorities and customer value drivers into technical requirements.
* Mention the critical and pain points
* Establish control mechanisms and KPIs to monitor the performance.
* Train and coach teams on how to interpret AI outputs.

It can be ensured by applying lean six sigma, Strategic priorities, Design for customer and employee experience, and Governance framework.

The role of a Master black belt in a company is to evaluate and investigate the processes in a company and then further direct the course of action in processes where there is defect. The MBBs have a deep knowledge of the processes of a company and dictate the  strategy for the direction to be taken.

AI team’s approach is more data driven and they would want to look for automation of the processes. They would want to analyze the data and identify patterns and give insights which the MBBs might have missed.

 

If they work together, they will lead to more optimized processes, better decision making and a more optimized process.  

MBBs can contribute by:

·        Sharing Process expertise: They have deep knowledge of processes.

·        Guiding strategically: They can guide the AI team with organizational goals.

·        By Handling Change Management: By helping the teams to adapt to AI driven processes.

·        Understanding of context: Ensuring that AI solutions are practical and relevant.

AI Teams can contribute by:

·        Automation: They are experts in Automation. It can help to automate repetitive tasks.

·        Predictive modelling: They can use the Predictive models to forecast the potential issues.

·        Simulation and optimization: they can simulate the new processes for MBBs to test before implementing them actually.

·        Data Analysis: They can use data analysis tools to identify patterns and anomalies that the MBBs might have missed.

 

How MBBs and AI Team can work together:

1.        MBBs and AI teams should try to learn from each other and always stay updated on the latest technologies, trends and best practices.

2.        They should use and iterative approach where the solutions are continuously improved based on the feedback.

3.        They should clearly understand each others’ roles and responsibilities so that there is no conflict and each team can contribute effectively.

 Master Black Belts are the experts to analyze the root cause of the problems faced by a process and can very well fetch the best suited solutions that will address the root cause of the problem directly. This will ensure that AI initiatives are targeting the areas which will result in maximum benefits.

 

Example –

 

Master Black Belt will start by drawing a Value Stream Map of the process to identify bottlenecks in the process. Bottleneck Severity Index = (Volume*Cycle Time) * (1-FTR%). This will help us prioritize the sub-process which requires AI solution to give accurate and faster output.

 

Post this Current Reality Tree could be used to highlight the root cause of the problem, this will ensure the AI solution targets to resolve the issues highlighted.

 

Solutions designed in this fashion will assist the AI team to take a targeted approach so that we are able to deliver maximum value to the client by doing the best operationally.

To create better solutions, MBBs and AI teams need to work together to bring their strengths & positive values, in turn combining process excellence and the science to create transformation.

Master Black Belts will have in depth understanding of the processes and map the value to the Business. While AI teams assist in creating automation or prediction of how things will turn up while improving the current scenario. MBBs will look for SIPOC or VOC to visualize the problem where AI teams convert the data and the requirements into a model to define the objectives, but both the teams should have a clear understanding on the problem and define the problem statement appropriately.

Both MBBs & AI teams should have a common purpose on the objectives to be met with understanding on either of the languages including understanding of CTQs, Control charts or other concepts of Six Sigma and exploring basic AI competencies.

Both the teams to create solutions together with a proper documentation starting with mapping the process, identify values, conduct root cause analysis, and identify the potential cause, while keeping all the required stakeholders aligned with the update & progress. In parallel AI teams will help with the data to bisect & dissect to bring out meaningful insights from the data and create matching simulations for the solutions and validate the results to understand the end objectives are met.

AI teams to be working hand in hand to better handle the process to have a happy flow where the variations are less which meets the requirements of the Business.

Risk management and mitigation of risks will be a key factor during solution generation where  both the teams should be trained and create necessary controls & mitigation in the solutions to ensure sustainability in long term.

To summarize, we need to create a synergy between both the teams where we Define the Problem, Measure with the data, do necessary analysis with simulations. Create insightful solution with AI and controls with visualizations.

Interesting answers to a seemingly easy question.

 

The best answer is from R Rajesh. Well done.

 

Answers from Sargun, Sachin and Mark are also an interesting read. Do check them out as well.