Sargun Diwan

As a part of the Business and process Excellence team’s across different organizations I have been involved in numerous projects involving process optimization and implementation of RPA’s. Having managed omni-channel contact centers, I have closely worked on implementations of ACD’s (automatic call distribution) and Autonomous IVR's for telephony set-ups. And have been fortunate to be a part of Live Chat implementation projects involving Chatbots in recent years. With the expansive capabilities of AI, what we had set out to implement was an interactive, self-learning, autonomous “Dynamic Packaging Module-DPM” for an OTA’s Holidays business. The idea was to integrate the DPM and Chatbot on the site, which would be able to handle Customer Inquiries related to the packages involving mostly customizations and any related questions to T&C’s, payments, offers, amendments and cancellations, upgrades, visa, insurance etc. immediately. Given, the fact that 60% of the inquiries were for “Customized Packages” it was important for our teams to be able to handle such customers by offering complete details “tailored” to their requirement in the First Interaction itself thereby improving conversions and customer experience, hence the decision to go ahead with the project. So, in addition, to building a Backend Product loading and managing tool involving the integrations of supplier API’s, Direct / Extranet contracts, GDS and Airline feeds, offline packages, ground transportation and ancillary services. We needed a Front end, which allowed both our teams and the End customers to be able to “Customize packages” as per their needs on real-time basis, while making use of the AI assistant. And finally, all interactions, requests, quotations to flow into the CRM and ERP tool for overall booking management, human agent and AI model training and retraining, Quality Audits using AI for all interaction summaries. The Front-end or the “DPM” was built using AI/ML algorithms, using on-premises LLM’s, which were trained on some 100,000+ itineraries for countries across the world. The AI QA tool had been trained on Sales scripts, product knowledge, soft skills and Quality monitoring sheets to score conversations. Both these tools had their respective KB’s. Let’s look at the project implementation in terms of restructuring of processes, new roles and responsibilities both for Human agents and the AI, handoff protocols laid down, Human-AI Collaboration framework and efforts, Phase-wise implementation plan, Training requirements for the team and Revised KPI’s. AI-Integrated Sales Process for Holiday Package OTA Revised Process Flow - Initial Customer Contact • The AI chatbot initiates the first interaction within 15 seconds of a customer visiting a Landing or Package page, offering assistance to “Customize a package” or offer the “off-the-shelf products”. • The AI qualifies the lead by gathering destination preference, departure city, travel dates, trip duration, no. of persons traveling, travel intent – couple, family, honeymoon etc. and budget, if any. • By using set parameters, the AI assesses the complexity of the inquiry. • Basis the complexity scores the system directs the next steps for the handling of the query. - Queries Handled by AI (70% of initial contacts) • Inquiries regarding package availability and pricing • Standard booking changes (dates, passenger details) • FAQ’s bout policies (cancellation, baggage, visa requirements) • Basic information on destinations and weather • Payment and confirmation support details • Post booking status updates and reminders - Human Agent Involvement (30% of initial contacts) • Multi-city itineraries that require customizations • Group bookings for 10 or more people • Special requests incl. accessibility, special medical needs, meals etc • Managing refunds that involve exceptional circumstances or exceed standard refund policies • Catering to High-value bookings above $X,XXX per person • Handling customer escalations or requests where customers explicitly ask for human assistance Evolution of Human Agents: From being Order Takers to Travel Consultants The agents are now responsible for offering strategic travel advice, instead of just processing standard transactions. While focusing on customer motivations, recommending upgrades, and creating WOW experiences, the agents now act as Travel Curators. - New Core Responsibilities of Human Agents • Curating complex itineraries involving multi-city routings • Developing long-lasting relationships with HNI and repeat customers • Promoting and upselling premium experiences and services • Handling sensitive customer service-related issues • Ensuring the quality of AI recommendations by regularly Auditing conversations • Training the AI system through feedback and updating information in AI KB for retraining AI – Human Handoff Protocols • Full conversation history and customer profile to be provided by AI to the agent with a ‘Warm transfer process’ to be followed. • The system marks the ‘Priority’ to define the urgency of the inquiry and provides the previous interaction sentiment. • AI provides recommendation to the agent on the suggested action items based on customer type. • AI to ensure customers don’t have to repeat themselves through smooth context transfer to the human agent. • Handoff scenarios include – Complex itinerary requests, emotional or sensitive situations, HNI customers, technical issues or complications. Collaboration Framework b/w AI-Human AI support by Human Agents - AI training and feedback • Agents flagging incorrect AI responses through live monitoring and post-chat audits. • Updating new product knowledge directly into AI KB. • Creating templates for recurring complex scenarios. - Quality Assurance and Risk management • Reviewing AI conversation transcripts on regular basis for accuracy. • Providing feedback for adjusting AI tone and persona. • Identifying and recommending cultural nuances for regional and international customers, which AI might miss. • Ensuring AI responses comply with industry regulations, company policies and meets customer privacy standards. - Data enhancement • Tagging successful booking and upselling conversations for AI learning. • Provide inputs on travel seasons (peak, off-season, shoulder) and booking patterns for AI to incorporate. • Highlighting objection handling patterns to assist with AI’s response improvement. • Providing SWOT analysis against competitors for improving AI pricing algorithms. • Regularly calibrating to improve AI response accuracy. Real-Time AI Support for Agents - Dynamic Sales Assistance • Recommending agents with relevant packages during live conversations. • Alerting agents of any ongoing flash sales or inventory updates mid-conversation. • Offering real-time access to competitor pricing during conversations. • Identifying and recommending potential upsell opportunities based on customer profile and sentiment analysis. • Offering alternatives in case the customer rejects the initial recommendation. • Providing real-time destination specific expertise when agents handle requests for unfamiliar destinations. - Quality and Administrative Task Automation • 100% QA of customer conversations. • Monitoring conversation sentiments and raising flags when necessary. • Identifying knowledge gaps and recommending relevant training’s. • Summarizing of customer conversations and updating CRM records. • Scheduling follow-up emails, callback reminders based on the conversation and commitments made by the agents. - Booking flow optimization • Automating mailers to customers with booking and document checklist. • Pre-filling of booking forms with customer data received during query and quotation stage. • Validating Customer’s travel documents for their visa, immigration and validity requirements, ticketing and booking confirmations etc. and alerting agents for any identified issues. • Generating the payment and cancellation schedules basis booking and travel dates • Populating instalment and discount offers recommendations. - Performance Optimization • Tracking conversion rates by agents and recommending relevant trainings for improvement. • Sharing insights into best practices and successful sales patterns. • Recommending staffing for aligning Work force according to peak intervals. • Tracking C-SAT Scores depending on the type of interaction and customer feedback. Gradual Phase-Wise Implementation Strategy • Phase 1: AI manages only basic FAQs and availability checks. • Phase 2: Introducing booking processing and payment handling. • Phase 3: Adding multi-city, complex package bundling and recommendation algorithms. • Phase 4: Achieving the complete integration with personalization features. Training Requirements for Agents • Tech skills for navigating through the AI systems. • Advanced consultative selling techniques training. • Training on cultural sensitivity for regional and international markets – New markets. • Advanced CX training for crisis management and de-escalation techniques. Introducing a Dual layer of KPI’s - AI KPI’s • Response time – Less than 5 seconds for 95%. • Deflection rate – 75% or more of routine inquiries handled by AI. • Accuracy rate – 95% correct information provided for each interaction. • Conversion% - Glide path targets for the first 06 months starting from 05% to 10%. • Availability – 99% uptime during business hours. • Handoff efficiency – 95% of AI to human transfers happen without customer repeating themselves or getting frustrated. • C-SAT scores - Above 4.25 out of 5. • Average order value – Increase in AOV’s by 15% MoM. - Agents KPI’s • Complex booking conversions% - Glide path targets for the first 06 months starting from 15% to 30%. • Up sales % - 30% of the bookings converted accept upgrades. • Customer Lifetime Value / Repeat customers - 30% increase in customer retention for agent handled customers. • Handoff efficiency – 90% of the AI transferred interactions to be resolved within first agent interaction. • Escalation resolution – 95% of the complaints to be resolved within 48-72 hours • AI training and feedback contribution – Achieving weekly and monthly contributions targets towards AI improvement and training. • C-SAT scores - Above 4.5 out of 5 Following an integrated approach, we were able to transition from a transactional to a consultative sales process. While AI handled the maximum volume and routine tasks, this allowed the human agents to focus on building relationships and solving complex problems. As a result, we were able to see faster response times, improved CX, and increased revenue per booking.

Signs of AI fragility Let’s look at some signs that can practically be monitored through performance metrics and user feedback. Same are listed below – - Decreased Performance – If the AI solution starts providing inaccurate or irrelevant results, esp. for complex problems, it may be struggling to adapt. - Increased errors – Frequent mistakes, including generating false information (hallucinations) or providing incorrect advice like fictitious information. - Vulnerability to manipulation – If the AI solution can be tricked easily to provide absurd responses, it shows lack of robustness. - Data quality issues – Poor or biased training data can produce unreliable results. - Integration challenges – Difficulty adapting to new systems or processes may indicate potential for obsolescence. Ensuring Long-term sustainability of AI solutions To keep AI solutions sustainable, we can follow the below strategies – - Continuous monitoring – Keep auditing solution’s performance and update and train the models with new data to adapt to changes. - Ethical practices – Ensuring the AI solution is transparent, fair and compliant with regulatory frameworks to build trust and avoid risks. - Stakeholder engagement – Involving users and developers to facilitate feedback allowing alignment with evolving needs. - Energy efficiency – As AI solutions can consume significant resources, hence its pivotal to address the energy usage impacting the environment. - Global Equity: Customize AI solutions to address the needs of diverse regions, ensuring benefits can be reaped by less developed areas as well. Strategic role of Business excellence professionals in preventing AI solution fragility To prevent AI fragility and ensuring long-term sustainability, the role of a business excellence professional encompasses the below roles and responsibilities – - Advocacy for continuous learning and adaption – Evolving with new AI technologies and fostering a culture of continuous learning within the organization. - Implementation of monitoring and evaluation frameworks – To continuously assess the performance of the AI solutions on account of accuracy and user satisfaction and to ensure they are ethically compliant. - Drive ethical and responsible AI practices – Lead the creation of ethical guidelines to ensure AI systems are transparent, fair and accountable, by conducting regular audits and compliance checks. - Ensuring data quality – Overseeing data management to ensure AI systems are trained on high quality, relevant and unbiased data through and with the use of data governance frameworks. - Promote sustainability – Advocating for green AI practices, including lower energy consumption models and compliance to environmental standards. - Align AI with Business objectives – Ensuring AI systems support broader business goals like improved customer satisfaction, revenue growth, process improvement etc. This allows the AI solution to contribute to organizational growth and hence supporting long term sustainability. - Stakeholder collaboration – By gathering feedback in the form of VOC, and VOB to ensure AI solutions meet the evolving needs and hence preventing drift from business goals.

The Swiss Cheese model initially developed for risk management and safety, is a powerful tool for understanding how multiple layers of defense symbolized by “slices of cheese” can prevent errors or points of failure represented by “holes” in complex systems or processes. When holes across multiple layers align failures can occur. Having been a part of contact centers, inside sales and customer support teams and applying the SCM has allowed stakeholders such as myself to identify vulnerabilities and guide improvement efforts using Business excellence principles. Below, I shall outline the most relevant use cases of the SCM in the sales process, providing examples of the defense layers (“slices of cheese”) and potential weaknesses (“holes”) and also how this understanding has allowed to drive process improvements. Swiss Cheese Model in Sales Processes In a sales process, the SCM helps visualize multiple layers i.e. the members / teams / systems that work together to ensure successful customer acquisition. Each layer aims to prevent errors, however weaknesses can still exist. Let’s break down the Sales process to identify the "Slices of Cheese" - Defense Layers and the "Holes" - Potential Weaknesses to better understand the SCM. Step 1 - Lead Generation and Qualification - Layer’s Purpose - Reaching out to and qualifying potential customers. - Holes - Poor lead quality, inadequate qualification criteria, ineffective lead scoring. - Scenario - Vaguely defined target audience might result in unqualified leads being passed to sales agents, wasting time and resources. Step 2 - Sales Training and Scripts - Layer’s Purpose – Equip the sales teams with the necessary tools and knowledge. - Holes - Outdated scripts, insufficient training, lack of product knowledge. - Scenario – Agents using outdated scripts or non-updated KB’s may end up providing incorrect information, leading to customer mistrust and lost sales. Step 3 - Customer Interaction and Engagement - Layer’s Purpose - Building customer rapport and effectively communicate value proposition. - Holes - Poor communication skills, unable to handle customer objections, failure to build rapport. - Scenario - An agent unable address customer concerns might fail to convert a qualified lead into a sale. Step 4 - Follow-up and Closure - Layer’s Purpose – Maintain follow ups to secure the sale and ensure customer engagement. - Holes - Ineffective closure techniques, lack of persistence, poor follow-up tracking processes. - Scenario – Non-compliance to follow up on a prospective / potential lead may result in the customer choosing a competitor. SCM guiding improvement efforts using Business Excellence principles The efforts to minimize and eliminate errors and potential failures would be directed towards - Identifying Weaknesses – By using process mapping and RCA (e.g., Fishbone Diagram) to pinpoint holes in each layer. Prioritizing Improvements - Applying tools like FMEA to prioritize high-impact weaknesses, such as inadequate lead qualification. Implementing Controls - Using LSS DMAIC methodology to address holes, e.g. refine the lead qualification criteria basis data analysis and monitor its impact. Monitor and Measure - Tracking KPIs such as Conversion% and leads quality to ensure improvements are effective. Improvement continuity - Regularly updating sales scripts and training programs based on VOC and market trends.

As a BB and having been a part of the DMAIC projects I agree that the success of the project hinges significantly on effective Change management. Change management is a structured approach to transition individuals, teams and organizations from the current state to a desired future state and is not merely an addon but a critical driver of adoption and sustainability. Let’s look at how change management is appropriate across the five phases of a DMAIC project and the pivotal role an MBB plays in ensuring success. I would like to draw relevant examples from a project that I was a part of for an Online Travel Company working to improve its revenue for its Holiday Packages inside sales teams. Change Management across the five phases of DMAIC A. Define Phase In the define phase, we identify the problem statement, define project goals, project scope, stakeholders and customer (CTQ) requirements. Relevance of Change management in Define Phase: · Stakeholder alignment – Stakeholders need to understand why the project matters and how it benefits them, hence an early buy-in is crucial. As change management ensures transparency about the “why” the project matters, hence it reduces resistance by addressing concerns upfront. · Communication Plan – Having a structured communication strategy is equally important to conveying the project intent, business impact and benefits to those involved. It involved including the sales leads and agents to explain how improving conversion rates will boost the Holidays Package revenue and, in turn, their incentives. · Sponsor engagement – Having the project sponsor’s commitment to champion the change ensures the project isn’t deprioritized during operational challenges. Define Phase actionables: Post kick-off of the project to improve Holiday packages sales conversions, workshops were conducted not just with the higher management, but also with team members and team leads. By incorporating the VOC (in this case the sales team members), we co-created a project charter. The agents felt heard and became partners in the project rather than showing resistance and becoming subjects to the project. They helped define the problem statement “Our current process doesn’t equip us to handle complex queries effectively, leading to lost opportunities”, which is far more empowering than “Agent performance is poor”. B. Measure Phase We capture the baseline performance, validate data sources and measure current state performance (e.g. Current Conversion% of the process). Relevance of Change management in Measure Phase: Change management ensures that those involved accept and support the data collection process by invoking- · Trust in data – Sales teams often distrust data especially if performance is linked to incentives. Change management enables address concerns regarding data misuse or “hidden agendas”. · Clarity for Metrics – Communicating what is being measured, and why it’s being measured and most importantly clarifying the goal is to find flaws and not to blame members. Involving team members in metric development also leads to increased ownership. · Acceptance for baseline findings – Presenting data findings openly helps to avoid surprises later. This also builds psychological safety and readiness for the upcoming project and improvements. Measure Phase actionables: The sales team were explained that the tracking would involve reviewing important business metrics including AHT, number of follow-ups for converted and non-converted queries and system-generated quotations shared per query etc. We positioned it as a “Gemba” in order to shadow the agents and understand their daily challenges. We also had the team leads share a transparent funnel view of the query lifecycle, this breakdown from queries :: quotes :: bookings, led the team to understand the bottlenecks better, thereby reducing the blame game. And we also had the team members design query trackers to ensure they were practical and captured the right information. This approach led to minimizing fear and more accurate baseline data. C. Analyze Phase Identify, validate, and prioritize root causes of the problem from the collected data using tools like Fishbone diagrams, pareto charts and regression analysis. Relevance of Change management in Analyze Phase: This phase challenges long-held assumptions and beliefs. Change management fosters collaboration and consensus amongst stakeholders, by – · Engaging diverse perspectives – Like the frontline agents know the customer objections firsthand, similarly the conducting cross-functional workshops with sales, marketing and customer service teams may reveal as to why holiday package queries convert low (e.g. pricing issue, poor follow-up, script inefficiencies, unclear promotions) ensuring the identified issues resonate with those who will implement the solutions, building a collective problem-solving mindset. · Managing fear of blame – Teams may fear punitive action and hence can create defensiveness in root cause identification. Change management emphasizes process over people. · Consensus creation – By having identified root causes validated by members ensures they feel solution are built with them and not imposed on them. Analyze Phase actionables: The data analysis revealed that the conversions were highest when the agents instead of following a rigid script, acted upon and used the product repository to quickly create customized itineraries. This contradicted the established belief that script adherence was the key to improved conversions. Using visuals and clear language including showing the agents Pareto charts and scatter plots, they were able to see the undeniable correlation in the data and realized the need for a new approach with focus on dynamic bundling options and not static scripts. D. Improve Phase Develop, test and implement solutions to address the root causes, pilot changes and validate improvements. This involves brainstorming, prioritizing solutions, piloting changes and creating an implementation plan. Relevance of Change management in Improve Phase: Change management is important to ensure these changes are embraced through training, communication and support. · Co-creation of solutions – Involving teams in designing the new process, will ensure they identify pitfalls and will make them more invested in solutions. · Piloting and iteration – Involving frontline leaders and “change champions” in pilots to create success stories and generating pull from others. · Training and Support – Providing trainings to address both skills and mindset is a must, while making support readily available during transition. Also, acknowledging that there is a learning curve and performance may dip temporarily. · Celebrating Early wins – Publicly acknowledge the success from pilot teams to build momentum. Improve Phase actionables: The pilot for a new holiday bundling tool along with a CRM Dashboard that populated Key knowledge base articles was initiated. The pilot team provided feedback, the tool and dashboard were refined. After 4 weeks their conversions improved by 30% for complex queries. These agents shared their experience in team forums leading to peer-driver adoption. E. Control Phase Sustaining the improvements and the gains, by standardizing the new process, creating monitoring plans (using control charts), updating SOP’s and have a response plan incase the process goes out of control. Relevance of Change management in Control Phase: Change management focuses on embedding the changes into the organizations culture and systems, or else the team’s may revert to old habits. · Systematic integration – Ensuring new scripts and tools become part of standard practice and not temporary fixes by using regular coaching, feedback loops and regular huddles. Additionally, aligning the job descriptions, KPI’s and incentive structures with the new way of working. · Performance monitoring – Making use of dashboards and control charts instead of micromanaging teams. · R&R’s – Re-enforcing value of the change, and sustaining motivation by linking improvements to ongoing rewards or career development. · Knowledge transfer – Creating SOP’s and training modules to prevent knowledge loss. · Handoff to Process owner – Clearly transition the ownership of the new process from the project team to the process owner who is responsible for the ongoing process performance. Control Phase actionables: The holiday bundling tool and CRM dashboard were integrated into the formal process SOP’s. The team’s performance scorecard, which was reviewed weekly, now included metrics directly related to the new process. The incentive structure was modified to reward both high conversions and consistent usage of new tools. The changes stuck as they had now become the new standard of work. The Role of an MBB in Driving Adoption and Sustaining Results A project’s success is measured by the tangible and sustained improvement it brings forth in business performance and not just by the statistical analysis. A MBBs role extends beyond the technical expertise to leadership, engagement and sustainment. Below is how they can drive adoption and ensure lasting results – · Communicating the vision – Translate complex data and project goals into a compelling narrative that answers “what’s in it for me?” for every impacted group. · Stakeholder engagement – Identifying early, and proactively and continuously engaging with key stakeholders from project sponsor to front-line agent, ensuring alignment, trust and fostering a shared vision. · Managing resistance – Anticipating, understanding and addressing resistance not as a problem to be crushed, but as a valuable feedback about the real-world impact of the change. · Training and Development – Overseeing tailored trainings to ensure everyone is confident in the new processes. Coaching project leads, process owners and team members, empowering them to become change champions to carry the improvements forward. · Sustaining the gains – Institutionalizing the project changes in the organization’s systems, ensuring the project’s impact endures and guarantees a lasting return on investment.

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.

AI Governance Framework for Business Excellence AI integration is transforming how decision-making, and operations are performed in organizations. As AI automates more business functions, strong governance becomes essential for responsible deployment. An effective and well-structured governance framework builds trust, reduces risks, and aligns AI advancements with organizational goals, regulations, and stakeholder expectations while maintaining competitive agility. A. Proposed AI Governance Framework Elements - Ethical Guidelines o These are a set of clear, non-negotiable principles that guide all AI initiatives, translating company values into technical requirements. o Defining acceptable use cases and explicitly prohibiting any unethical or biased applications. o Using reference frameworks including – EU AI Act / NIST AI RMF etc to translate these principles into policies and decision logs to ensure how each AI solution meets the guidelines. - AI Governance Structure and Oversight committee o A council of senior executives with cross-functional representation responsible for strategic AI direction and policy approvals o The panel reviews AI projects not only for business objectives but also for ethical standards and societal impact o Conducts periodic audits and model validations including ad-hoc sessions for urgent issues - Data Management Guardrails o Its imperative to maintain an AI repository with the details of the AI models, training data sources and intended usage o Monitoring data quality, lineage and privacy controls to ensure compliance with the adopted guidelines frameworks and the existing data-governance policies - Risk assessment and mitigation o It covers categorizing potential risks into – Operational, reputational, legal and ethical headers with their respective mitigation strategies o A Tiered framework for risk assessment (Low, medium, high) allows for agility by matching the level of oversight to the potential impact of the AI projects, thereby, ensuring low-risk projects aren’t affected by unnecessary governance whereas the high-risk projects receive intense scrutiny o The protocol also covers the real-time tracking of AI performance metrics, bias emergence and unexpected outcomes with incident response procedures for addressing AI system issues - Stakeholder engagement and communication o It involves including the employees / end-users, customers and the external advisors in the loop during design and post deployment of AI projects, to ensure that development and deployment of AI are not done in silo o Comprehensive training for teams to understand AI capabilities, limitations and their role in governance o Publish the explanation of the AI models purpose, performance and disclosures to build trust with customers and partners - Performance and accountability mechanisms o Define AI performance metrics to measure accuracy, fairness, and business impact of AI systems o Recording of AI decision making processes, model changes and associated governance activities B. Stakeholders for AI Governance Stakeholder Role and Responsibilities Chief Ethics Officer / Governance Lead Manages the ethical application of AI and chairs the AI Governance Committee. IT / Data Science Teams Ensure models are technically robust, monitored, explainable, and secure. Business Process Owners To validate AI outputs against the business goals and customer outcomes. Legal & Compliance To ensure AI systems comply with regulations, data and privacy laws, and any ISO standards and AI frameworks, as applicable. HR & Change Management Conduct training, initiate communication, and change readiness for AI-impacted teams. Internal Audit Regularly review model performance, risk, and controls. C. Balancing Agility and Control - Real time monitoring and Alerts o Use of monitoring dashboards to track live model performance, flag issues and trigger alerts for intervention, thereby closing the gap between operations and governance. - Controlled Pilots and A/B testing o Iteratively test AI models in a secure environment before deployment to track issues during development itself. - Living document and Fact sheets o Document the assumptions, limitations, training and retraining cycles and model versions for transparency and control. - Continuous feedback loop o Use feedback from users and business scenarios into model retraining processes to support continuous improvement and ensure alignment with organizational objectives. Subsequently, we can conclude that an effective AI Governance framework anchors the principles of Transparency by laying down clear guidelines and documentation; Accountability by defining roles and responsibilities and putting in place the required controls and continuous improvement through real-time monitoring, feedback and evolution of the governance framework based on the best practices and stakeholder needs. By adopting globally established standards and frameworks in AI governance, organizations can harness the transformative power of AI without compromising ethical or operational integrity, while achieving its business excellence goals of quality, cost optimization and super customer satisfaction.

Proposed Business Excellence Metrics for the AI Era - The use of AI in the core business processes is reshaping how value is created and delivered by organizations. Subsequently, the traditional KPI metrics we have used to measure performance in areas like quality, cost, and efficiency are becoming insufficient and redundant. Using these old metrics in an AI-driven environment can be misleading, causing organizations to optimize for the wrong behaviors and not reap ROI on their technology investments. Let us begin by assessing the Traditional metrics and their shortcomings in an AI driven environment. 1. Assessment of Traditional Metrics Metric 1: First Call Resolution (FCR) It has long been a primary KPI to monitor contact center efficiency and customer satisfaction, indicating a low effort experience for the customer and low cost for the business. In an AI-Driven Environment: Using AI-powered chatbots, IVRs, and self-service portals to manage simple, high-volume, transactional queries is an attempt to give the “Easy” Calls today to machines instead of humans. These were precisely the calls that used to be FCR wins for human agents. By filtering simple issues, AI is ensuring that the only calls reaching human agents are the complex, emotionally charged, or multi-faceted problems that the AI could not solve. And it turns out that these problems are more difficult to solve in one phone conversation. Following these developments, a high FCR rate might actually be a concern! It could potentially indicate that the AI is not being effectively used to screen issues, or human agents bring complex problems to a premature close just to attain an outdated target. While a lower FCR could signify that agents are appropriately handling the highly complex issues that require follow-up, research, and collaboration. Metric 2: Average Handle Time (AHT) AHT measures the average duration of customer interaction. It has been a pivotal metric in gauging operational efficiency, used for staffing models and cost control. The goal has always been to reduce AHT. In an AI-Driven Environment: Since the calls that are able to reach human agents as mentioned above are likely to be important ones. We shouldn't be obsessed with how soon the agent can get the customer of the phone but rather with what quality and value is one giving. A complex issue, high-value customer retention or an upsell opportunity might require a longer AHT. Stressing agents to cut AHT on complex calls can have detrimental effect not only with regards to poor outcomes, customer churn, and repeat calls (which negatively impact other metrics). The AHT metric also disregards entirely the time customers may have already spent interacting with an AI chatbot, rendering the “AHT” only a partial — and potentially misleading — view of the overall customer journey effort. 2. Proposed New Metrics In order to track performance in an AI-driven setting, we need new metrics capturing proactive problem-solving, and the utility of human-AI interaction. Proposed New Metric 1: Proactive Resolution Rate (PRR) PRR is the ratio of potential customer issues that are identified and resolved proactively by the AI system before the customer initiates contact. PRR Logic o The AI tracks customer journey data, usage patterns, and system logs for anomalies that indicate there is a problem in the process (e.g., missed payment, delayed delivery, odd user behavior in a software application). o The AI then initiates an automated resolution using the SOP’s, FAQ’s and KB updates to assist the customer (e.g., retries the missed payment, informs the logistics partner, proactively sends a "how-to" guide, or sends a system alert to the user). o PRR Calculation: (AI-initiated Proactive Resolutions / Total potential issues detected) x 100 · This metric, most importantly, switches the mindset away from reactive service and illustrates the value of preventative excellence. It captures a measure of the organization's ability to avoid problems, which is a far stronger indicator of operational excellence and customer-centricity than how effectively it cleans up messes. Proposed New Metric 2: Human-Assisted Value-Add (HAVA) · HAVA Score is a metric for evaluating the efficacy and efficiency of human agents involved in complex situations escalated by AI. The HAVA Score replaces the use of simplified metrics like AHT and FCR for these high-value encounters. · HAVA Logic: The HAVA Score is calculated after the interaction and based on a weighted calculation of the following: Problem Resolution Success (40%): Was the customer's issue ultimately resolved? (Binary: Yes/No, or a scaled rating). Customer Sentiment Analysis (30%): AI parses the text or audio of the communication to measure customer sentiment levels (i.e., measuring if the customer's levels of frustration decreased, positive language increased, etc.) Customer Lifetime Value (CLV) Impact (20%): Did the interaction led to customer retention, a new purchase, or an upgrade, this can be done by mapping the service interaction to CRM data. Knowledge Base Contribution (10%): Did the agent record the solution for this unique problem, so it could be used for training the AI in the future? (thus helping avoid similar escalations). · HAVA provides a path away from basic efficiency and instead reflects the true value of the human agent in the world of AI. HAVA rewards agents to be thorough and empathetic problem-solvers. HAVA also promotes a learning cycle in which the agent is incentivized to make the AI smarter through KB updates, contributing to the improvement of the system over time. 3. Linkage to Business Excellence These proposed metrics are directly aligned with the core principles of Business Excellence. Business Excellence Principle How Proposed Metrics Align Customer Centricity PRR is a measure of an organization’s ability to solve problems before the customer even knows about them, it is the most efficient form of customer-centricity and true commitment to an effortless experience. The HAVA Score ensures that when customers do need to talk to a human, the focus is all about solving their complex needs and maintaining the relationship that impacts their perception of value and care. Operational Excellence & Quality Improvement PRR actively measures the quality of operational processes. A high PRR means that the underlying systems and processes that are driving the standard approach we work towards, are efficient, intelligent and self-healing, which is an essential component of modern operational excellence. The HAVA Score assists and develops an environment for continuous improvement. Agents are rewarded for contributing to a knowledge base, ensuring human knowledge is captured, and then used to build up the overall human-ai capability to get smarter and smarter, and to be able to do more at scale over time. Employee Engagement & Empowerment HAVA, also enhances the human agent's role from "call handling" to "resolution expert or relationship builder." It enables and rewards them for spending time in solving complex issues whilst creating value - leading to higher job satisfaction and lower turnover. It recognizes and rewards the value of empathy, creativity and complex problem solving that are inherent to being human. Value-Driven Leadership With these metrics available to leaders, they can get a clearer and more informative view of their business performance. Instead of managing counterproductive metrics, they can focus on the real priorities: stopping customer issues before they occur, getting the most value for each human engagement, and designing a learning system that continuously improves with every transaction.

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.

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

So by using structured root cause analysis and hypothesis testing techniques, we can validate the findings and propose targeted corrective strategies. In addition, we need to start by analyzing different sets of data to understand trends, timelines, forecast accuracy etc. Data sources may include: Sales Data (12 months): Seeking trends to indicate a mismatch between high-demand periods and stock availability leading to Lost Sales impact. Inventory Records: For stock-out frequency of high-volume SKUs across the above period. Forecast Accuracy Reports: To check for variance from actual demand. Delivery Timeline Logs: For Supplier delivery windows. On-time delivery performance >95%, suggesting minimal impact from logistics delays. Key Insights identified : Forecast Error Correlation: High forecast inaccuracies coincide with stockout events, especially for promotions and new product launches. Replenishment Lag: Time between forecast input and stock arrival often spans 10–14 days, reducing responsiveness. Demand Volatility Ignored: Forecasts do not factor in localized demand surges, social trends, or weather-related events. Inventory Turnover Analysis: Low turnover rates in some categories suggest misallocation of inventory resources. Analysis Techniques Used 1. Process Mapping (SIPOC Analysis) 2. Root Cause Analysis - 5 Whys Technique Problem: Products are frequently out of stock 3. Fishbone Diagram (Cause & Effect Analysis) 4. Hypothesis Testing Test 2 primary hypotheses using statistical analysis: Hypothesis 1: Delivery delays cause stockouts. We found out that on-time delivery has remained stable at > 95%, so we can confidently disprove this hypothesis. It's noise, not the cause. Hypothesis 2: Poor forecasting drives stockouts. By mapping “Forecast accuracy %” against “stockout incidents” we can find out the correlation between them. With a significant p-value we have statistical proof that this is the primary driver. How to Prevent Chasing the Wrong Cause - Define the Problem with Data - Begin with a problem statement from the Measure phase (e.g., "From Q2 to Q4, stockouts on A-list items increased by 40%, contributing to an estimated $1.2M in lost sales."). This is our ultimate benchmark. If an identified "cause" doesn't statistically impact this metric, it's not the right one! Gemba walk – Speaking with the store managers, the inventory planners, and the logistics coordinators is important as they have qualitative insights that can help with data analysis. Use a combination of Tools: Using a combination of tools like the Fishbone Diagram provides the structure, the Regression Analysis provides the statistical proof, and the 5 Whys provide the deep dive.