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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.

Below listed are the traits of a good AI Governance Model – 1.) Clear and Fair – Identify and reduce any biases in the model, provisioning options for human interventions for big decisions and ensuring POCs are defined and known to all stakeholders in case something goes wrong. 2.) Strategic Fit – Ensure every AI deployment directly supports the organization's strategic goals and delivers clear value 3.) Managed Lifecycles: AI systems should have a defined route from initial conception to continuing upkeep. This calls for extensive testing prior to deployment, ongoing performance monitoring, and a defined procedure for modifications or even retirement. We require accurate documentation of everything. 4.) Training – Ensuring that relevant teams are well trained to work effectively with the AI. Also, teams need to be aware of what the AI can and cannot do. Stakeholders of an ideal AI Governance Model – 1.) Leadership 2.) AI oversight group 3.) Ethics and compliance team 4.) Internal Auditors Mechanism to ensure both agility and control 1.) Smart Risk Assessment – Design and approval framework aligned with the risk quotient of the deployment. 2.) Use of standardized tools and reusable modules – Provision pre-approved tools and use of reusable building blocks to cut down redundant work. 3.) Build in governance from day 1. 4.) Centralized guidance from the AI oversight team. 5.) Clear and defined RACI. 6.) Provision Human-AI collaboration for high risk decisions.

In an AI based ecosystem, following are key elements in the governance framework that I would be interested in - Laying out Clear Governance and proper accountability structures - Defining adequate policies and procedures for data governance and security - Effective oversight mechanisms to ensure proper safety, and also, legal compliance - Adherence to security standards ensuring data protection and trust maintenance I feel that the stakeholders who should be involved in this, should be Business Owner, AI Solution Architect, Legal Team, Governance team/leads Mechanism/Approach for maintaining agility and control - Establish the objectives as what you want to do, by setting up a SMART goal - Define the KPIs/metrics - Data Availability and Privacy - Ensure right technology infrastructure is available - Ensure right skilled people are available - Ensure stakeholder management is properly done - Ensure Continuous Security Testing is done which can help in identifying and fixing vulnerabilities - Ensure right data strategy is available Example: In an IT project for an Insurance customer, creating and managing Property insurance policies were a challenge. The Project team wanted to re-write from a legacy application to a modern rich and niche-skilled application. As the team started its journey, it found out many bottlenecks, such as the non-availability of few experienced underwriters (who moved to different teams), frequent change in regulatory laws and compliance [in some of the countries in which the customer (insurance organization) is placed at] delaying the policy creation/policy management processes. These things resulted in delay in Policy management, especially delay in creation of policy. From few hours (existing), it took a day to complete a policy creation, on an average There was a discussion that happened between the Product Manager (who takes care of the Property Insurance product suite – that contains - Policy Management, Claims Management, Administration…), the Product Owner (who takes care of the Policy Management) and Business stakeholders. The discussion centred around as how to improve the overall cycle time of getting a policy created and how to provide a better experience for the end user. For them, the purpose of re-writing an application rather than migrating from a legacy to a modern application was supposed to be superior. But the results were not showing as expected. One of the stakeholders suggested about the usage of AI and proposed if that can be leveraged. Then the Product Owner suggested to include an AI Solution Architect to get his opinion. The AI solutions Architect was called upon and was briefed on the current situation. He suggested that with the help of AI, those challenges can be addressed. How were the challenges Addressed: With the stakeholders' consent, the AI Solution Architect (along with a few AI minded volunteers), arrived at the following things 1. Defined clear-cut goal for reducing cycle time – from a day to a range of 10- 15 minutes 2. Defined KPIs/metrics · >= 98% Accuracy Risk Profiling of Policyholder data · 100% documentation/repository of updated/compliant regulatory laws/rules for various states/provinces in different countries (where the insurer/customer organization has a presence) 3. Established the AI specific roles and responsibilities · ML Engineer who was responsible for guiding/helping the team in its this AI journey and acted as a technical SME for the team · Domain Expert with minimal AI knowledge, to offset the lack of enough underwriters · Data Scientist/Engineer (who was used across teams) to ensure the data governance was smooth · Prompt Engineer was responsible for helping the team to rephrase or refine their queries/ideas in a clear and focussed manner. [She was later leveraged for multiple teams as the team got matured in prompting] · AI Quality Engineers for testing the AI system · Provided extensive training to the people as per the AI roles that they played · AI Facilitator helped the team to collaborate with all the stakeholders (such as Business stakeholders, AI Solution architect, key end-users..) 4. Established proper data governance and accountability structure · Product Owner in conjunction with the AI Solution Architect had put the backlog items for consumption · The Order of escalation was : the first point of escalation was the Product Owner, followed by Product Manager 5. Established Safety and compliance mechanisms · Safety Procedures and Policies were fed into the AI KB · All Safety compliance things were well- documented 6. Right technology infrastructure was established · Microservices based architecture was created – so that loosely coupled systems can work easily – for instance Policy Issues not impacting Claims Mgmt · Niche Front end technology skills such as React.js, Android were being used to make the system highly interactive 7. Established a data protection and privacy policy With all those necessary changes, the existing challenges faced by the team was addressed. The Risk Profiling profiling was predominantly automated by an AI agent, with a minimal oversight by an underwriter (This drastically reduced the need for an underwriter and addressed #1 challenge). With respect to the regulatory rules and compliance, the team leveraged an AI agent which constant polled (pulling info from a server) on a centralized repository (for every country in which the customer organisation was present) that had the latest info on the regulatory laws and ensured that the latest update was available. This made it a 100% POKA YOKE system and resolved #2 challenge. Thus, the team was able to achieve its goal of reducing the cycle time and in the process made the stakeholders and end-users delighted. The team also started to make everything through a Kanban board (as a Visual radiator of information) of its progress, providing dashboards on how many policies were created in a month and what type of policy coverages are getting generated more etc.. It also started highlighting any outstanding issues/blockers and leveraged AI KB agents to provide timely resolution. Conclusion: AI has reshaped the way how the world is responding to challenges, ideas. It is beyond imagination to see how AI can influence any industry. The more we embrace AI, the better it could be for our business. For that to happen, our understanding of that powerful technology has to be in-depth. As we keep exploring on that, we may tap its potential to the fullest. As we try to co-exist with it, we need to understand as a framework, how to govern it. What are the elements that are needed to govern the AI framework, therefore becomes critical. IMHO, I find the aforementioned ones as some of the key elements that are required. Source Reference: Benchmark Six sigma academy CAISA program material and based on personal learning

A robust control framework/guardrail is required in any process driven organization to sustain the process, create accountability for process owners, transparency, fairness and compliance with regulation. It integrates lawful and ethical considerations, ensure compliance, and operational guidelines to ensure that AI systems are developed and utilized with responsibility. Elements of AI governance framework · Align initiatives with organizational goals · Stakeholder engagement from the beginning and two way communication · There should be fairness, accountability and transparency establish · There should be Risk management strategies. that is, assess and mitigate risks for the process data including all scenarios and impact assessment before deployment · Embed AI taking overall process into consideration, ie core business workflows · SOP to be updated for the new process and procedure and all scenarios · Train team about the process changes and provide clear communication · Performance monitoring and evaluation Who should be included Role Responsibility Executive Sponsors/senior leadership They are there to set the vision and provide direction for AI initiatives. Culture embedment and collaboration are promoted by them Legal and compliance teams They establish the policy and ensure legal and compliance guidelines followed. They conduct the implemented AI monitoring. Business Process Owners Define SLA, test and validate the AI developed solution. Data Scientists / AI Engineers Build and maintain AI solution and ensure the best practices followed IT / Infrastructure Teams Ensure scalability and secure deployment of solution. MBBs / Process Excellence Leaders Ensure alignment with Lean/Six Sigma principles and eliminate the process waste In order to maintain both Agility and Control, we can integrate prioritization framework and for agility there can be cross functional squads working in sprints. we should define SLA for performance review and monitoring and light weight governance for minimum viable product. there should be performance dashboard as control and for agility there should be feedback loops from users

According to what VK noted under his forum questions, “Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone..” AI is created by humans who are prone to error. We must always remember this and be diligent to make sure AI will make the best decisions. “Making sure” will ALWAYS to be and, I believe, will forever be, a human responsibility. I can’t ever imagine anyone shirking their responsibility and pointing at the AI solution and saying “It’s the AI’s fault that we lost revenue”. Yes. It might have been that we trusted the AI agent to make the decision but ONLY after we allowed it to make that decision. So, the real accountability still falls back to a human. Knowing that AI is prone to make errors, just as humans have done to mitigate making our own errors, we created guardrails to increase proper decision making and better outcomes – ergo, Business Excellence. Think of AI as another person. But now you are responsible for the decisions and actions of that person. It will need oversight, accountability, and transparency to make sure AI is making the right decisions on our behalf. Here are some of the elements that I think could be included in a governance framework to ensure responsible, high-impact use of AI in a process-driven organization. Creating a governance team or committee to oversee all AI solutions. This team would comprise people from IT, the business, legal, risk management and defining each role and responsibility throughout the AI development, deployment and maintenance. For transparency and accountability, conducting regular impact assessments to identify potential risks, biases and consequences of AI-driven decision. Also, implementing techniques that can provide insights into the how AI is making its decisions, such as feature attribution or model interpretability methods. Lastly, performing audit trails that let us see the data inputs, processing and outputs the AI used to make its decision. For agility and control, using agile development methodologies to allow for rapid iterations and deployment. Using change management to capture the all the changes made throughout the development which can easily be reviewed, tested, and validated. Lastly, establish access controls to prevent unauthorized changes to the AI system or data.

Since each one of us works with data and metrics, plus given that AI is increasingly getting integrated in our processes and work, it will be a worth while investment to go through all the answers. You will get ideas on what you need to focus on and what you can let go. Best answer has been provided by Sargun Diwan. Well Done!!

I consider three key questions while designing a governance framework for AI. 1. What are we doing and why? Team Charter: A public, plain-language document outlining the organization's commitment to ethical AI. This includes principles like Fairness, Accountability, and Transparency, as well as Security and Human Oversight. Use Case Registry: A central, living inventory of all AI models in development or production. For each model, it should clearly state its purpose (the business process it automates and the specific decision it influences), data (what data it was trained on and what data it uses to make decisions) and the owners (business leaders accountable for its performance and impact) 2. Who is responsible? Roles that play both technical as well as business accountability. Model Owner (Business): A leader from the business unit who is ultimately accountable for the AI's business outcomes and risks. Model Owner (Technical): The data scientist or MLOps engineer responsible for the technical health, monitoring, and maintenance of the model. 3. How do we get better? Performance Dashboards: There should be a continuous mechanism to monitor the performance of AI's real-world business impact and operational health. Feedback Loops: A clear, simple process for end-users to flag unexpected or seemingly incorrect AI behavior. Audit Trail : For every decision made by AI, the system must be able to log why it made that decision in a way that a human auditor can understand. This isn't just for compliance; it's essential for troubleshooting and building trust. 4. Who Should Be Involved? A multi-disciplinary approach ensures all angles are covered. Steering Committee (Strategic Level): A cross-functional group of senior leaders that sets the overall AI strategy and principles. (COO, CTO, CDO, Head of Legal/Compliance, and key business unit leaders) AI Review Board (Operational Level): A hands-on group responsible for reviewing Impact Assessments and risk audits Business & Technical Teams (Execution Level): People on the ground. (Model Owners & Stewards responsible for the day-to-day success and health of a specific AI model).

Since the launch of AI solution into business excellence world, the decisions are completely shifted manual to automate. 1) Followings are the elements should be included in a governance framework: Ø Strategic approach: · Organizations objectives and goal should be clearly defined at the time of AI integration and identify the critical area’s where AI can add values. · Build a separate team which includes but not limited to data analyst, AI operation experts and machine learning engineers. Conduct training sessions for existing employees in AI can be a cost effective approach. Ø Governance and Quality of data: Ensure that quality, integrity and security of the data can’t compromised. Ø Data privacy policy: Build a policy to evaluate, storage and collection of data inline to the regulatory guidelines so that compliance can be maintained. Ø Continuous improvement approach: Updation of the AI model inline to the regulatory guidance and based on continuous learning, so that model can adopt changing data and development requirement. 2) In order to mainlined both agility and control, it is necessary to build a team contains different stakeholders and also establish a system that can harmonized the practices. Ø Executive leadership: Management board should be involved for strategic directions and long term business goals. Ø Management review board (MRB): MRB should include different CFTs or representatives of different department for effective implementation and up gradation of AI model. MRB should include but not limited to: Operations, IT/AI/machine learning engineers, Quality assurance, Legal, compliance and business excellence. 3) In order to implement practically in the real world, Ø 1st start with the pilot program which resulting low risk and high value application Ø 2nd based on the existing learning, risk and compliance AI governance checkpoint should be placed. 4) In order to align AI with business excellence, some crucial steps needs to be taken: Ø Customer Service and Engagement: AI programs like chatbots and virtual assistants needs to be utilized, for 24 hours customer support. Ø Integration of methodology: Integration of six sigma and operation excellence methodology with AI in order to bridging the common language/ terminologies, mind-set and skill gap.

Governance, Business Excellence values, transparency, accountability, and continuous improvement shall be granted during applying AI Solution otherwise company will face a lots of problems and concerns and not comply with international standard. Currently, one of the most national standards/requirements is Governance We need to ensure well governance during applying AI Solution Therefore, we need to ensure the following during design AI Solution: 1)Transparency AI decision should be transparent, explainable, and auditable. 2) Accountability Define clear accountability for AI system performance. 3) Continuous Improvement we should always improve the design and enhance AI Solution through reviewing the process and ticket that raised by end user also, validate the suitable solution 4) Compliance Ensure that AI systems comply with government laws, regulations, and industry standards like MODON. 5) Monitoring Implement continuous monitoring and feedback mechanisms to detect issues, identify areas for improvement, and optimize AI system performance. To ensure the Governance we need to involve the following: 1) Board member 2) IT 3) Risk Management Team 4) AI Solution owners It's better to be representative from each operation department It's very important to include all operation departments Then everyone will be involved and feel responsibilities The mechanisms that need to put in place to maintain both agility and control are as follows: 1)Well-designed governance framework. 2)Risk Management Can use tool like FMEA 3)Regular Reviews 4) Enhance VSM 5) Data Analytics 6) Use Quality control tool like pareto, SPC, Fishbone,… We should always review and analysis the data Develop a new procedure for Conduct regular reviews of AI systems and governance frameworks to ensure they remain relevant and effective. 4)Training Provide advanced training and awareness programs to ensure that staff understand AI governance policies and procedures.

If an organisation is business excellence driven, it is expected that it would have structured processes and most of it output dependent on process and lesser on people related activities. Now if you add an AI governance element to it there are several factors that the organisation needs to consider for it to be called as an effective AI driven governance model. Factor 1 Risks are assesed : , when you add AI driven solutions are embedded with your regular processes. the exposure of confidential data, access levels to various designations in org chart are to be assessed and considered before implementing solutions. there should be a periodical review Ethical usage : Org needs to ensure ethical guidelines are clearly defined in conflict scenarios while implementing any AI solution. scenarios and situations needs to be constantly added to a library for ensuring correct decision making by an AI enabled solution in case of dilema. there should be high level of transparency to with stakeholders so ensure trust is build and remains intact with periodic checks. Scope, Reach and accountability : The scope for all processes and solutions developed should be clearly defined along with the goals. the. reach has to be to the lowest level of organisation so that everyone is involved and invested in any solution implemented. All department interests must be considered by running a program like QFD roof diagram. and ensure that none if the department goals are compromised when AI solution is run to optimise a process. for e.g. if a TAT reduction or quality improvement of product solution is run. there should be accountability and consideration of all said and unsaid impacts of all possible metrics. defined method of collecting data. inbuilt guage RnR to be considered. Data governance : Parties involved, level of access, what would be input methods and what data is shared, what frequency. How long is it to be stored. whether it will retain or erase historic data trends. All has to be assessed and defined. Last but not the least would be Continuous improvement and innovation : there should be room for new ideas to be implemented for process to become more lean if possible with lesser dependencies. Constantly asses moving and impacting internal and external factors. Thus making it an effective AI governance and lead the business excellence philosophy.

AI governance in a organization should be linked to organizational strategy with all the pillars such as continuous improvement , Customer value, Performance measurement, Leadership Involvement. It should be a enabler for sustainability and operational Excellence. These are the following ways AI governance should be in organization: 1. Strategic Alignment - AI governance should support operational excellence by enabling AI initiatives. It should drive continuous improvements and process optimization to enhance efficiency and customer value through AI integrated solutions. 2. AI governance Pillars - AI driven organizations should have these pillars such as: 2.1 Responsible AI - AI ethics board setup is required to maintain the fairness, transparency and accountability. 2.2 Performance - AI KPI's to be integrated in Scorecards for monitoring 2.3 Risk Management - AI assessments should be conducted regularly for mitigating any risk towards AI enablement and to plan the measures for the risk. 3. Culture Building - AI awareness trainings to be mandate for all the employee's to maintain AI literacy level. These levels can be designed by AI governance Board. 4. Governance Board - A CCB or a AI governance board to set up for proper governing the overall AI development in the organization.

In medical coding, it is possible for AI to bring transformation in effectiveness and efficiency. But this should be governed with precision and accuracy. We should define the permissible usage, constraints, and ethical guidelines for AI applications in programming and ensure compliant with HIPAA standards whenever applicable. Medical Coding Specialists validate AI-generated codes and offer continuous training feedback mechanisms. The real-time dashboards like Kanban boards could be executed to visualize and monitor the coding accuracy With respect to Explainability & Traceability, every AI-generated code is auditable and linked to specific medical record documentation. The outputs are integrated explaining "why this code was selected" as part of the coding workflow and facilitating transparency. The corrections are made to retrain models and ensure continuous enhancement is proper aligned with Business Excellence. We can start with less critical coding areas like outpatient coding with pilot sandboxes To control mechanisms, role-based access is to be given to sensitive information like protected health information. There should be governance board reviews for significant model updates or any changes in rule logic. There should be transparency in terms of clarifying coding logic, sustaining audit trails, and involve key stakeholders in decision-making activities.

Traditional KPI metrics such as productivity, quality, cost, delivery, efficiency, and many more should not leave management lenses, rather, targets associated with them should be adjusted accordingly. Customer and employee satisfaction surveys however can be done through AI, leveraging on its capability to detect emotion, interpret facial expression, body language, and many more which is difficult for human eye to decipher and prone to certain biases. To track AI’s real performance and value, I recommend Input Data Integrity, and Bias Detection as two additional KPI metrics that management should add under their lenses. These are crucial for AI’s model creation, accurate training and analysis, impacting AI’s recommendation for business decision-making process.

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.

Yes, ready. There exists a relationship between Business Excellence (BE), AI transformation, and the measurement of organizational performance. Traditional metrics of Business Excellence were effective in systems driven by human input and repetitive transactions. AI is now responsible for decision-making, process optimization, and large-scale output personalization, some of these metrics may become misleading, incomplete, or even outdated. In medical coding field, the existing metric which is possible to be outdated/obsolete is the number of charts coded by coder in an hour. Earlier, the medical coder was able to code 25 charts per hour, but now they may only code 15 charts per hour because of complexity and AI codes 30 charts which are all easy and simple. Subsequently, the obsolete productivity metrics will show a decrease in trend. The new metric to introduce is Human-AI Collaboration Ratio (HACR) in Coding. It tracks how AI augments and not replaces workforce capacity The updated/revised business excellence scorecard for AI-enabled medical coding is for Productivity - The traditional metric is the number of charts coded per FTE. The updated AI-enabled metric is the total number of charts processed (AI + human) and HACR. How it is related to Business Excellence: These new metrics: • Connect AI performance with human empowerment • Assist leaders in proactively managing AI drift and coder disengagement How to Implement: • Test new metrics alongside existing ones to ensure continuity and facilitate comparison • Utilize heatmaps to illustrate performance by specialty or AI version, which is beneficial for CAC tuning • Ensure dashboards are aligned with compliance, revenue integrity, and training functions, not solely operations.