Q 783. MBBs are trained to challenge assumptions, dig deep into root causes, and define problems with precision. But AI projects often jump straight to solution-building based on surface-level symptoms or available data. 

Think of a situation where an AI solution might deliver technically correct results — but still miss the mark because the problem wasn’t framed right. How can MBBs contribute at the problem-definition stage to avoid this trap?

 

The best answer will be selected on the basis of: 

• Clarity and relevance of the example  
• Insight into how misframing leads to suboptimal AI outcomes  
• Practical ways for MBBs to influence AI problem-framing

 

 

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An example of a situation where AI solution might deliver technically but miss the mark is; 

In a FMCG company situated in Nigeria, an AI-solution can be built to predict stockouts in a retail store. assume the model gave an accurate prediction technically(that is, it could tell which locations were likely to run out of stocks) but in reality, product availability didn't improve. This is because the real problem wasn't a lack of stock at the warehouse; it was the lack of good coordination between sales representatives, transporters, and distributors.

MBBs can contribute in this case, using tools like 5 whys or a process map, the MBBs will uncover issues like: 

• Sales representative not updating orders at the right time
• Drivers not getting to some outlets because of bad roads or a spike in fuel prices.
• There's no visibility in real-time delivery.

So instead of building a model that predicts stockouts, the problem will be reframed as below:

"How can coordination between Sales representatives, distributions, and logistics"

When an AI solves the wrong problem, it still gives the right answer to a question no one asked. 

It will still be running smoothly, giving accurate answers, and showing good performance metrics. however all that effort would be wasted as it would not be addressing what actually matters to the user or organisation. People might think the problem is solved, only to realize later that they are not anywhere close to real solution.

This can be avoided if we dont rush into building without having a clear understanding of the root cause of the problem. Practices like researching about users, VoC, and problem framing are so critical.

In Medical coding, AI solutions are usually applied to automate the coding process, reduce errors or enhance revenue cycle management. If problem is not clearly defined, even technically proficient AI may not deliver value, because it may focus on symptoms instead of root causes.

 

Master Black Belts (MBBs), with their knowledge of Lean Six Sigma methodologies, process enhancement, and data-driven problem-solving, are ideally positioned to ensure that the correct problem is identified.

Example: AI in Medical Coding Addressing the Incorrect Issue
Scenario:

A hospital aims to decrease claim denials within its medical coding process, blaming the problem on coder mistakes in assigning ICD-10 codes. An AI system is created to automate code assignments using historical patient data, achieving a 95% accuracy rate in code prediction. However, claim denials continue to be high, and coders express frustration with the AI overriding their expertise in complicated cases.
Why It Fails:

The issue was misidentified as "coder errors in ICD-10 assignment" when the actual cause is inadequate clinical documentation (for instance, vague or incomplete physician notes). The AI, trained on existing records, accurately assigns codes based on the data available but cannot rectify documentation deficiencies, which lead to denials.

 

The issue here is misinterpreted as "coder errors" instead of "poor clinical documentation". Hence, the claim denials are not reduced by AI effectively. When AI tackles a wrong issue, the resources are not able to provide any impact and there is dissatisfaction among stakeholders.

 

This can be prevented by Master Black Belts by implementing DMAIC, conducting root cause analysis, engaging key stakeholders, validating assumptions, ensuring data correctly, and integrating change management.

 

If the problem is reframed to focus on the quality of documentation, MBBs confirm AI solutions can give meaningful outcomes, such as reduction in denials and improved efficiency in the revenue cycle.

Problem statement framing and Project outcome is the basic to do for any problem solving. An experience I could share with one of my project which is of Customer Satisfaction Improvement.

 

Project team while working with the required stakeholders had collected the data required to improve the customer satisfaction which had all relevant information from the historical data. The problem statement framed here was what response from agent could lead to DSAT and the team worked on creating a prediction model which determined which statement is apt for CSAT and which could lead to DSAT.

 

It helped the team with sentence framing while responding to customer but the DSAT kept increasing. Since the project was to predict the outcome of sentence formation it did not determined if the overall satisfaction which had different factors involved apart from only communication.

 

MBB while working on the project could help in the problem statement identification by determining the causes of the problem, create factors which are highly contributing to DSAT and then frame an objective statement which should be recommended for problem solving of the project to improve customer satisfaction as a whole.

There are multiple outcomes of a AI solution solving a wrong problem

1.     Reflects poor leadership: It exposes accountability and seriousness of the management that approves the solution. The MBBs or Project managers that approve such a solution of OK such a goal statement which does not address the actual concerns, issues or underlying problems that an organization has. If the true problem is not accurately identified, the AI's outputs may fail to align with strategic goals, which can lead to confusion or mislead decision-makers.🥵

a.         , it can lead to wasted resources like human capital and other investments, affect CAPEX, OPEX and bottom-line in a project, good but ineffective solutions, distrust in AI solution as an optimising tool and missed opportunities for meaningful improvements — This highlights the importance of thorough framing of an issue in the define stage and alignment before leveraging AI.

2.     The actual issue remains unaddressed: The AI might focus on symptoms rather than root causes, leading to short-term improvements but failing to eliminate underlying root causes, bottlenecks and problems. 

3.     Another issue solving a wrong problem might create it build a bias in AI : pre-existing notions in the existing data and biases might be set. AI will definitely provide a solution without addressing issue. The thinking hat or use of human intelligence is extremely important to utiize the power of AI in optimising process or creating solutions. Defining it, planning it, predicting benefits and then implementing an AI solution and reap the benefits. 

If the question isn't framed with clarity, the AI will solve the wrong problem; for example, a doctor's office builds an AI model to predict which patients are likely to miss appointments, aiming to improve clinic efficiency. The model accurately displays the results. However, at the same time, the AI model may book double bookings, leading to patient dissatisfaction. As it misses the root cause of missed appointments, it fails to understand why patients miss them (e.g., poor communication, transportation issues, etc.).

This issue can be dealt with by asking subsequent questions, e.g.,

• What are we trying to achieve?
• Factors contributing to the issue?
• Is this a cause or a symptom

To address these situations, MBB can conduct interviews. This approach enables teams to move beyond symptoms and frame the problem precisely. The result will be, instead of solving the problem wrongly, which works well, but is not of real use

I have not been trained or certified as an MBB but I can apply what I have learned in this course.

 

Here's an example of an AI solution technically is working as it should but has become a part of the problem.

 

Consider a business who has a customer service center and their customers are experiencing long wait times.  In an effort to decrease the long wait times, they create a Chat Bot.  After implementing this AI solution they certainly can see the the call wait times has significantly decreased because the Chat Bot can "answer" them quickly.  So, technically, this AI solution is a success.   Wait times have drastically decreased.  But the company begins to hear from their customers how angry and frustrated they are, even more so than when they had to deal with long wait times.  The business failed to understand that what they should have been really trying to solve was increasing customer satisfaction, not merely the symptom of addressing long call wait times.  The Chat Bot caused greater unsatisfaction because customers now have to make repeated calls (even though they don't have to wait) because the many "simple" calls are often precursors to more complex issues and the Bot could not handle these, thus forcing customers to start over with an agent, which leads to more frustration.  Also, agents may now have to deal with more calls from customers because the Chat Bot did not properly diagnose the underlying problem.  This situation wasn't created by the Chat Bot, but by those who didn't have the foresight to really understand how they should have created the Chat Bot.  At the end of the day, technology or technical solutions, such as AI, will not be blamed for these problems that arise.  Those who created the AI solution will be.  You don't want to be that person.     

 

Back to the original thought of creating an AI solution.  The business thought it was to merely solve lowering long wait call times.  But the real root of their issue was customer frustration and dissatisfaction.  Their "AI solution" was focused on the wrong thing and it even caused a deeper problem for them 

 

How could this have been prevented?

Using the FRT process and documentation which captures the Desired Effects (DE), the Undesired Effects (UDE), and the Negative Injections (NI) of any AI project and solution.  FRTs can help to envision the ideal future state of an AI solution but also proactively identify negative consequences BEFORE a dime gets spent on creating the solution.  The FRT would have captured the root cause by addressing and thinking through the UDEs and also creating NIs to create answers for these UDEs.  Utilizing the FRT process and documentation, along with creating a very thorough and thoughtful BRD, would have greatly increased a proper AI solution that result not only in lowering call wait times, but mor importantly, raising customer satisfaction.

Answer: In the modern world of “Artificial Intelligence” and automation, things are going significantly faster however, this rapid pace sometime leads to skip the critical steps, although the final outcome appears right.

Let’s understand this with an example through creating a real scenario of pharmaceutical company who is going to launch their first ever ulcerative colitis medicine in the market:

So there are multiple critical steps needs to be followed before launching a medicine into the market which includes but not limited to pilot scale batches, commercial scale up, exhibit, PPQ batches. In every stage, multiple trials and testing are required to evaluate the quality, safety and efficacy of the medicine.

Apart from above, for consistent process and outcome multiple troubleshooting initiatives are also required.

To implement, all the above scenario privacy of documents plays an important role.

There is a possibility that by using advanced analytics and machine learning algorithms, significant amount of historical data like outcomes of the trials, different troubleshooting phase and stages can helps to make better decisions and improve process performance and as a result effective manufacturing of the medicine even significantly faster and end result can be technically correct.    

But missing of some critical information like data privacy/security, ethical consideration, regulatory compliance can leads to disclose the information to other pharmaceutical competitors which are also worked on same molecule.

At this stage, MBB plays a crucial role to ensure successful implementation of AI. Following are the steps should considered for effective implementation:

1)    Data privacy/security: Companies should implement strict rules with respect to data governance policies /procedures, such as data encryption, access controls and robust security protocols.

2)    AI model interpretation: Some time it is difficult for human to understand the complex and opaque language of AI model resulting wrong interpretation. At this stage, MBB should use some techniques such as feature visualization. Explainable AI and Model interpretability techniques such as Local Interpretable Model-agnostic explanations, and Shapley Additive explanations, so that model can be transparent.

3)    Consideration of ethics: It is important for MBB to establish a strong governance framework and ethical guidelines to ensure that their AI systems are aligned with their values and principles.  

 

 Practically MBBs influence AI problem-framing through following:

1)    Define clear goal: It is important to clearly define the objectives, so that goals are aligned with the business requirement.

2)    CFT collaboration: It is responsibility of MBB to collaborate with CFTs, so that divers thoughts can be considered, as a result comprehensive problem can be defined.

3)    Regular validation of AI Model: It is important to validate the model and process time to time to ensure that the model is aligned with the problem statement. So it is key responsibility of MBB to review the regular updates, feedback and new data.

Through considering all the above aspects, MBB can significantly enhance the efficiency,  effectiveness and influence AI problem-framing.

 

MBBs have deep expertise in process optimization and structured problem-solving, and their role in structured problem-framing approach is paramount, let’s understand this especially in AI solution implementations in high-touch environments like Contact Centers.

The problems of Mis-framing leading to ineffective Solutions

Contact Center Chatbot Deployment

Scenario:

A contact center faces long customer waiting times. To quickly reduce Average Speed of Answer (ASA), leadership launches an AI chatbot to handle frequently asked inquiries, aiming to ease pressure on live agents and thereby reducing ASA.

Surface Level Problem statement:

The project team stated the problem as “We have long wait times because our agents are overwhelmed. Let’s implement a chatbot to handle FAQ’s and reduce wait times by 50%.”

While investing 100,000’s of dollars to develop an AI chatbot, train it on FAQ’s, and deploying it as a FPOC for all customer inquiries. The chatbot in itself was technically proficient, using NLP and ML algorithms to interpret customer requests.

What was missed:

The team did not perform a thorough root cause analysis. Key problems included understaffing staffing during peak hours (only 60% of required agents), inadequate training programs that left agents unprepared for complex product inquiries, fragmented knowledge management systems that forced agents to search multiple databases, and high employee churn (45% annually) from workplace stress and limited career advancement opportunities.

The Effects of Mis-Framing on AI Performance

Following the chatbot deployment, AI gave generic responses to complex customer issues, causing greater frustration among those needing detailed technical support. Instead of reducing call volume, the chatbot generated additional calls from customers seeking clarification on the AI’s responses or requested immediate escalation to human agents.

Findings based on BSI Analysis:

The pre-implementation baseline, calculated with the Bottleneck Severity Index formula (BSI = Volume × Cycle Time × (1 - First Time Right%) × Severity), showed:

•              Volume: 1,200 calls per day

•              Cycle Time: 8.5 minutes average handle time

•              First Time Right: 65%

•              Severity: 3.2 (scale of 1-5)

•              Baseline BSI: 11,424

 

Post-chatbot implementation revealed:

•              Volume: 1,350 calls per day (increased due to chatbot escalations)

•              Cycle Time: 11.2 minutes (longer due to frustrated customers)

•              First Time Right: 58% (decreased due to inadequate agent preparation)

•              Severity: 3.8 (higher customer frustration)

•              New BSI: 20,365 (78% increase)

The AI solution made matters worse: with customer complaints increased, call deflection remained below 15%, and net promoter score (NPS) declined further, and the organization having to face increased operational costs due to higher call volumes and longer resolution times.

In addition to the above consequences, wastage of resources and loss of stakeholder trusts add to the negative impact of mis-framing on AI effectiveness.

Suggested Practical strategies for MBBs to improve problem framing in AI projects

a.       Engaging in structure problem statement development using LSS thinking and tools

o    Use SIPOC and VOC to clarify process boundaries and understand demand drivers

o    Defining CTQ’s and linking them to customer pain points rather than convenience metrics like ASA.

b.       Apply BSI for comprehensive bottleneck assessment

o    Train the project teams in evaluating each BSI component

Component	Key MBB Questions
Volume	Is the call volume avoidable or failure demand (e.g., repeat issues, unclear policies)?
Cycle Time	Are agents slowed down due to poor tools or unclear procedures?
First Time Right %	What’s the root cause of low FTR? Training, systems, or information gaps?
Severity	Are we prioritizing automation for high-impact or low-impact queries?

 

o    Trend Analysis: Ongoing BSI monitoring to spot patterns and predict bottlenecks before they become critical. This enables teams to address root causes proactively instead of reacting to symptoms.

o    Use Pareto analysis of BSI to identify Top drivers and guide the AI strategy accordingly.

 

c.        Facilitating structured problem definition workshops and fostering stakeholder engagement

o    Run problem framing workshops that bring together diverse perspectives and stakeholders (operations, IT, HR, training and customer experience.)

o    Use tools like affinity diagrams and root cause analysis techniques to identify underlying issues that may not be apparent to any single stakeholder group and before confirming the need for AI.

o    Translating insights into well-structured problem statements (what is wrong, where, when, to what extent and impact on CTQ.)

o    Making use of the RACI matrix to ensure comprehensive problem understanding.

• Inform: Keep executive leadership aware of project progress and findings

• Consult: Gather input from frontline agents, customers, and IT teams

• Responsible: Include customer service managers, training coordinators along with operations teams and customer experience specialists in problem definition sessions

• Accountable: Work closely with the project sponsor on the project approvals.

d.       Deploy Control Measures Before Automating

o    Test hypotheses through small-scale pilots that test technical functionality and business impact of the proposed solution before scaling AI.

o    The pilots need to monitor impact on Leading Indicators (FTR, Escalation Rate, Post-Chat Survey Scores) to validate alignment of proposed solution with identified root causes.

 

Hence the mis-framing of problems in AI initiatives may lead to technically accurate but operationally ineffective solutions, wherein MBBs are mandated with the task of diagnosis with discipline.

Using BSI as a key metric identifies real process friction points and thereby guiding the organization to ask the right questions before investing in AI, and ensuring the final solution addresses the true constraints, improve customer experience, and deliver sustainable business value.

 

For easy understanding let’s take one example of common development projects,

Project has the following challenges,

• Failed to meet or continuous slippage in the delivery time timeline.
• Customer faces quality issues in the delivery of medium and complex requirements.

Just looking at surface-level symptoms, projects teams will always tend to strengthen QA teams and decide to employ automation approaches. To fast track this automation initiative, they will tend to buy automation tools with AI augmentation capabilities or add more QA experts to support the current crisis.

 

After deploying the new approach, teams ended up with more internal quality issues in the developments, there will be again delivery timeline delays, and the customer is going to be unhappy as there are a lot of backlogs.

Let’s assume that actual root cause is compromised engineering timeline or faulty estimation approaches or critical resources shared across multiple sprints to support less experienced team.

 

So, without understanding the actual root cause, just by looking into surface level symptoms it always leads to failure in complex or scaled projects. The role of Process excellence owner (MBB) can help to go deep and identify the actual bottlenecks to deliver the required business value.

 

AI has been trained to resolve the issues basis the historical data and symptoms which is solving the issue at the surface level however not solving at the root cause level. Well, it optimized prediction but not prevention.

A best example can be forecasting/ predicting employee attrition in an organization

 

AI tool can flag the early leaver basis the historical data and the anticipation basis the tenure, performance rating and engagement level, their survey results and the salary. however no one deep dives into the reason for disengagement and does not solve the actual issues solving the culture and other professional concern related to growth, employers, role etc

MBB analyse and help in framing the right problem statement. they use SIPOC, Voice of employee and critical to quality tools to bring into the relevant KPI to solve the problem basis employees’ view

MBB utilizes LSS tools like fishbone and 5 whys to get to the root cause

MBB can question the past data and ensures the validity of the data and no seasonal data included to influence or provide bias

MBB indicate how the process and its achievable result looks like before the AI model is deployed in solution.

MBB define the actionable and intervention needed at al level and not just retention tacts only

MBB bring in all the cross functional teams and stakeholders to avoid the silo approach and work collaboratively to resolve the problem with engagement

 

Difference

AI	MBB
AI detects and solves issues at surface level	MBB deep dive and solves the problem by identifying the underneath root cause of the issue
AI provides prediction accuracy basis the historical data & trends	MBB ensures the business outcomes meeting the ned user/customer requirement and creating positive impact within customer’s life
Generates speedy results via automation	Generates values and take risk into account while delivering the outcome
Biases basis the historical data	Applies accuracy basis the current situation and fairness

 

MBB act as a eyes and ears of the process and ensures that the solution deployed by AI fits in well and resolves the root cause and eliminate the process waste to optimize the process efficiently and effectively

AI solution solving the wrong problem is due to the failure of managing the scope correctly. It delivers results that is irrelevant or misleading, wasting time, resources and poor decision-making. Hence, defining issues accurately is essential.
For instance, a bank that utilizes AI to predict which customers shut their accounts. The AI spots those customers with accuracy, but the fix doesn't cut down on customer loss. Because the real issue was not about identifying who would leave the bank, it was getting to the bottom of why they were going and how to keep them around?
When determining the problem, ask who will be departing instead of asking why they are departing and how we can keep them. AI answers might be spot-on, but they don't help the company achieve its true aim. The main cause gets missed, so any steps taken based on what the AI predicts don't deal with the actual problem.
MBBs can play a crucial role by:
1. Asking the right question and probing, Are we dealing with the actual problem?
2. Using root cause techniques such as 5 Whys and Fishbone diagrams to examine more before rushing into AI modeling
3. Setting clear success criteria and focusing on results like keeping customers instead of just how well forecasts work
MBBs ensure that AI solutions are both technically correct and practical by helping teams frame problems correctly.

Let us explain this question with an example.

 

Example:

In an IT environment, there were multiple teams situated across different locations. Most of the teams were struggling to deliver the goods (requirements) on time.. The development effort, were more due to several factors such as complexity involved, lack of coordination/alignment amongst the team members who are from different vendors. This means that every team had team members with different vendors.  

The Problem statement framed was to improve the development cycle time of a requirement, for these teams.  Based on the problem statement, one team was piloted for understanding, the current ecosystem and was taken, for implementing the changes.

 

But the solution provided by AI, did not provide the expectation that the business owner wanted. There were multiple teams, each team was unique – One was a component based team, another a cross-functional agile team, another distributed agile team (members from different places), another team was collocated… So with such diverse teams, the result was not having any improvement on the development cycle time for all the teams. The piloted team was a collocated team and the improvement shown was virtually nothing, as this was a well-knit team and the team operated at a better cycle time.

 

This is where a MBB helped in rebranding the problem statement. With the help of a Project Charter, the MBB helped to write a proper business case and Problem Statement.  The business case stated the impact of the cycle time taking long reasoning out the why part (as mentioned above) and which types of teams (distributed - cross-functional teams, component teams) are contributing (where it is happening) to this problem and how long it has been there.  Then the Problem statement was defined as “Lot of Development effort goes on due to the complexity involved and lack of coordination amongst team members, in distributed component-based and cross-functional teams”.  Then SMART Goal was developed stating 2 pilots would be done – one on Distributed Cross-functional team and one on Distributed Component-based team – within a month’s timeline .  Out of Scope will be Collocated teams

Once this clear-cut strategy was established, then it was clear to all the stakeholders as what to do and then the execution became laser-focused and the teams were able to improve (Reduce) their cycle teams

 

Conclusion:

It is essential to frame the problem statement right, irrespective of whether AI is used or not. 

 

AS an AI Solution Architect, we can encourage the person who provides the problem statement to use prompt engineering/fine tuning (depending upon how depth you want to explore something).

 

We can also help them in using Chain of Thoughts, if they don’t know (assuming we also don’t know), how to structure our thoughts and leverage the problem statement further

 

As a MBB, its important to ask insightful queries while going through a problem statement. The MBB has to know from the Business Owner/Problem Statement provider

-  Who are the impacted stakeholders

-  The impact/implications of the problem need to be understood

-  How long the problem exists

-  In-Scope/Out-of-scope

-  Tangible/in-tangible benefits need to be understood.

 

Apart from this, the MBB need to know with the AI Solution Architect/AI teams

-  What is the AI solution trying to do

-  Will the AI solution be short term/long term

 

Based on this response, the MBB will be able to make the AI Solution Architect design the solution catering to these needs.

 

Thus you can see how with proper framing of the problem can yield the right results and how not doing the right framing of the problem, resulted in no improvement (in this case). But in general it can be like you may loose customer confidence, stakeholder dissatisfaction etc.. when you get a solution which does not provide value..

 

An analogy could be - while ordering food through online, it is like you get an eatery which you have not ordered, when you are hungry but looking for your ordered item!!

Example - Think of a situation in Human Resource Management domain where AI model is built to analyze and reduce employee turnover. In the absence of a MBB, automation/transformation team defines the problem as “predict which employees are likely to leave”. The model accurately predicted attrition rate basis the available data but failed to point out the root cause of the problem and reduce attrition. Only later by involving a Master Black Belt and post thorough root cause analysis it was tabled that the primary cause of attrition was lack of career growth opportunities within the organization which led to high attrition. The primary root cause was not a part of the HR data that the AI model was built on, hence incorrect problem statement will not result in the desired output.

 

MBBs contribution to problem definition stage –

 

1.      MBBs will ensure that the project goal aligns with the Business Case

2.      CTQ drill down – Business Case will then be linked to operational objective and post baselining the current performance RCA will be conducted

3.      RCA – Using techniques such as 5-WHY, Fishbone analysis , Affinity etc. root cause of the problem can be arrived at

4.      Hypothesis Testing – Testing the hypothesis to identify the true trend before full scale deployment of the model is as imperative as the pre-work like clear problem definition

 

Conclusion – To ensure successful deployment of AI model which gives desirable and effective output the pre-work done involving clearly defining the problem statement, it’s link to the strategic and operational objective, CTQ drill down, RCA etc. is imperative for MBBs to ensure the project is directed in the right direction.

Following are the problems that are surfaced when AI solution solves a wrong problem.

1. Misleading results - When AI solutions are not aligned with the real or exact problems, They will give results bit will not solve the actual problem,.

2. Inefficient use of resources - Wastage of efforts invested in building, designing and architecting the solution. As the solution is inefficient in solving the real issue all these efforts are a waste which results efforts invested in terms of Man Months, which can directly impact cost.

3. Loss of Trust - As Solutions addressed wrong issue's stakeholders start to loose confidence on AI solutions that can start losing trust and affect sponsorship for budget allocation and project execution.

4. Consequential Harm - If AI solutions solves wrong issues in critical areas like healthcare, Legal & compliance it can lead to serious consequences that can harm the reputation of the organization and will result in negative impact on brand.

Following are the some of the ways in MBB's can help in identifying correct or real problem statements for deploying AI solutions.

1. In Depth Analysis - MBB's go exactly deep for finding out the actual problem with relevant data analysis for backing the problem statement.

2. Stakeholder engagement - MBB acts as facilitator for driving engagement 

3. Data driven insights - MBB uses data to drive statistical inferences for validating results and decisions taken during execution of the AI solutions.

4. Benefit Analysis - MBB helps in visualizing the benefits in terms of value, cost and efforts invested.

In 2022, Klarna launched a full-speed AI deployment automating most of its processes using AI solution and realized cost savings equivalent to 700 FTE. One of the processes they automated was their Customer Service Support.

 

After a while, customer complaints and dissatisfaction ballooned. Customers claimed that AI responses were too generic and unhelpful when dealing with real-life problems. While AI solution like chatbots can handle simple and repetitive queries, emotions or complex issues were not addressed.

 

Klarna realized that while AI solutions promise speed and cost savings, it can compromise service quality and customer satisfaction.

 

Klarna decided to rehire employees to address poor service quality and customer complaints. This is a testament that AI solution isn’t about replacing humans, but rather, enhancing the human workforce with smarter tools and better support system.

 

As an MBB, following were my recommendation:

1.       Use VOC to identify critical customer requirements (CCR) where complex issues and customers needing to talk to human to solve their concerns will surface.

2.       AI solution aims to enhance customer experience leveraging on personalized interaction for higher engagement. This was not apparent in case of Klarna. It is recommended to take advantage on Deep Learning capabilities of AI solution. Such model can identify complex patterns, making it suitable in image recognition, voice recognition, and natural language processing.

3.       Lastly, while drawing the to-be process map, HILT (human-in-the loop) principle is recommended. In cases of complex customer concern, AI can escalate the concern to its human counterpart to further assess the given concern and provide necessary resolution.

Some interesting answers to the question. There are 2 winners for this question - Mark Wexelberg and Sargun Diwan.