Monica Salunkhe

Let’s understand how one would design an AI agent to stay aligned with regulatory or business requirement changes, be adaptable — without needing to be rebuilt from scratch each time. Example - Frequent changes in GST call for changes in overall direct and indirect tax structure. The GST slabs and calculations need to be reconfigured and aligned to match to stay complaint. Many times, in built GST calculators say in Tally or ERP are static and would need to be scrapped or would require change in configuration. This would call for manual efforts, off the system calculation, adding to delays and errors, till the time the tool is rebuilt. This may have an impact on cost and possible tax liabilities. AI agent design would need adaptive compliance engine to address the listed core challenges. Instead of hard coding rules directly into the system, AI agent with “Dynamic Rules Layer” that learns, validates, and applies updates automatically. Key Design Principles Regulation aware Natural Language Processing (NLP) engine - Use NLP to continuously scan official government notifications, circulars and tax websites. Automatically extract changes in tax slabs, exemptions, and compliance deadlines. Example – AI identifies “18% slab changed to 12% for foreign exchange services effective from April1, 2026”. Modular Rule Database - Rules to be stored in a configurable knowledge graph or rule library, instead of hardcoding. Each GST rule is represented as a modular entity (rate, category, effective date, applicability). The AI agent will update the database rather than rewriting the software. Automated Impact Simulation - AI runs “what-if” scenarios on existing transactions to simulate the effect of new rules. Example – AI identifies and aligns to “If GST rate changes to 12% for IT services, projected tax liability reduces by 8%. Human in the Loop Validation - Compliance Manager (Tax team) get AI generated draft updates and approval requests before deployment. Thus, ensuring trust, accountability, and avoids blind automation risks. Plug and Play API layer - The AI agent exposes updated rules via APIs. ERP/Tally systems pull updated rules dynamically instead of requiring rebuilds. API integration will give the required flexibility for scalability. Illustrative prompt for AI agent can look like – “Act as a compliance update assistant. Your role is to 1) monitor government tax portals for GST notifications, 2) extract and summarize changes in slabs, exemptions, or filing rules, 3) update the modular tax rule database with effective dates, 4)simulate impact on past and future transactions 5) provide clear recommendations in a supportive, non-technical manner to tax teams, 6) expose updated rules via APIs for ERP/Tally integration. Always flag ambiguities to Compliance Manager for approval.” The approach would thus position AI gent as the living “Tax Brain” and benefit business by optimizing costs, stay compliant and adaptable to meet any changes in regulation irrespective of the tax category.

To support growth and sustainability, one of the key strategic initiatives of any organization is Learning & Development (L&D) of its employees. The key challenges faced in this initiative are knowledge retention, employee engagement and overall effectiveness of the L&D program. AI could best address key challenges and behave like a supportive coach rather than a spy or an audit inspector through below recommended a high-level approach. AI LMS coach (agent) to be built as part of the Learning Management System (LMS) platform. 1. Personalized Micro-learning – LMS coach to break down complex concepts into bite-sized, role specific modules delivered at the learner’s preferred pace. Learning modules to be delivered as micro-interactions tailored to role/tasks and governed by human oversight and privacy rules. Knowledge retention – Reinforce for long term memory retention. Use quizzes, role-based challenges, spaced over time (spaced repetition). Prefer retrieval practice (questions, short problems) over passive content. Use interleaving (mix related skills) to improve transfer. Provide immediate corrective feedback with brief explanations and worked examples. Employee engagement Short micro-sessions (2–8 minutes) embedded into flow of work. Role-relevant scenarios and simulations (real tickets, meetings, code snippets). Social learning nudges and optional small cohorts for peer practice. 2. Continuous Real -Time Feedback Instead of waiting for assessments, LMS coah can provide instant corrective feedback in a supportive tone, highlighting progress as well as areas of improvement - Progress signaling - lightweight badges, streaks, and tangible next-steps. The interaction should be dialog based, making learning feel like a conversation with a mentor. 3. “Context -Aware Guidance” Program effectiveness Use adaptive curricula: move learners through diagnostics → practice → transfer tasks. Using NLP, the LMS coach can analyze the employee’s daily tasks and provide on the spot coaching (e.g.” Here’s how you could improve the presentation” or “Try this approach for your upcoming client presentation” Ensure learning is not abstract but directly tied to instrument outcomes - task performance, manager ratings, on-the-job measures, and learning metrics. Design prompts and policies so the agent recommends and nudges, not reports. When manager visibility is needed, present summarized, anonymized progress with learner consent. 4. Learning Journey Mapping Coach vs Monitor (behavioral stance) LMS coach to track each employee’s goal, career progression, skill gaps, and progress to suggest growth plans as per career aspirations. Rather than pushing generic training, it will help align development with both personal aspirations and organization objectives. A/B test coaching interventions and measure retention at 1 week / 1 month / 3 months. Default persona: supportive, confidential, growth-oriented coach. Only collect minimal telemetry required to improve learning, surface aggregated metrics to managers. Explicitly state what is private, what is shared, and give opt-out controls. Schedule workflows for Pre-session diagnostic → Focused practice → Retrieval checks Diagnostic (2 min) identifies 2–3 weak subskills. Practice loop: 3 micro-exercises (2–4 min each) with immediate feedback. Retrieval checks after 2 days and 10 days (auto scheduled). On-the-job coaching (just-in-time) Trigger: employee opens a PR / joins a meeting / receives a support ticket. Coach offers a 1–2-minute scaffolded checklist, a quick example, and an optional practice prompt. Project wrap / transfer task After completing a real task, coach asks 3 reflective prompts (what worked, what to try next, 1 skill to practice). Plan a focused practice item tied to that skill. For the LMS coach to come across as an encouraging, adaptive, and empathetic coach, rather than a strict auditor – The LMS coach prompt to be instructed as “Your role is to 1) reinforce past learning using spaced repetition and contextual examples, 2)provide constructive feedback in a positive motivating tone, 3)suggest personalized exercises that build both confidence and competence, 4) encourage self -reflections by asking open ended questions, and 5) adapt your guidance based on the employee’s role, pace, and learning style. Avoid sounding like a monitor and focus on nurturing growth, celebrating progress and fostering curiosity. Taking earlier example of improving presentation. User can interact with LMS coach saying, “Hey, your presentation micro module helped, and I was able to get through confidently, but I faced challenges while moving across the slides. LMS coach reply – “Glad, you impressed the audience through your presentation…here are few tips to empower you fully as a presenter. Click on the link to watch the tips”. User post watching the tip, “Thankyou buddy! It helped. I’m now better prepared and feel more empowered for the next presentation.” Thus, by positioning LMS as a personal growth companion, employees will experience L&D as an exciting journey, and an enriching and fulfilling learning experience. This will result in higher knowledge retention, enhanced engagement and more meaningful skill development, transferring L&D mindset from compliance driven to growth driven.

Tradionally, the Quality function has been seen as a guardian of compliance, process optimization, and defect reduction. Let’s consider how AI can help Quality to plays a bigger role in a service organization. While Quality professionals can be the evangelist AI can be the Catalyst for Innovation in Quality function. Quality’s legacy role of process optimization, defect reduction remains critical. However, AI can offer an opportunity to elevate Quality into a proactive driver of innovation and customer experience. Instead of simply fixing what is broken, AI can help reimagine what customers truly value. A breakthrough can be achieved when ADMIRE (Approach, Deploy, Measure, Impact and Result) logic is adopted. Approach – Design a creative and feasible AI powered “Quality Innovation Hub”. Start with VOC – Customer Voice Mining, using Natural Language Processing (NLP) to continuously scan diverse inputs such as customer feedback, service reviews, call transcriptions, social media discussions, and also include company website interfaces.AI can uncover latent needs, emotional drivers and emerging trends that may not be articulated by customers. Chain Of Thoughts (CoT) reasoning can be used for breaking down complex problems into a sequence of logical, interpretable steps. In the context of quality and AI, this means using AI systems that can “think aloud,” mapping their reasoning as they analyze customer data, identify pain points, and suggest improvements. Deploy - Cross Domain Inspiration, AI powered Customer Journey Mapping · Data Collection: Use AI to gather and integrate data from multiple touchpoints—customer feedback, service logs, social media, and IoT devices. · CoT Reasoning: Implement AI models that apply CoT reasoning to trace the customer journey step-by-step, identifying moments of delight and frustration. · Root Cause Analysis: The AI “thinks aloud” to connect customer emotions and behaviours to specific service processes, uncovering hidden causes of dissatisfaction or missed opportunities for delight. The system can connect insights from unrelated industries and be used for benchmarking. For example, a complaint about delays in financial services may be matches with AI identified solutions from logistic or healthcare, sparking cross pollinated innovation ideas. Idea Generator Mode – Using multipurpose prompts, Quality teams can query the system not only to optimize existing workflows but to brainstorm fresh concepts. This needs to be embedded in the Quality Innovation Hub. The Idea generator mode module is also made available to key business stakeholders and intelligent decision making can be fostered. Example – A business user is looking for ways of increasing revenue and offer new services to recent onboarded customer. The business user now will go to Quality Innovation Hub and write in chat box: ‘Based on organization service offering, customer retention, operational data, profitability, customer reviews, and industry best practices, suggest three innovative service enhancements that can reduce friction, delight customers and differentiate us from the competitors.’ Measuring New Product/ Service Developments using Predictive Quality Design – AI can simulate how potential innovations would impact service performance, cost, and customer satisfaction before implementation, reducing trial and error risk. AI driven co- creation with customers - · Virtual Quality Assistants: Deploy AI-powered assistants that interact with customers in real time, gathering feedback, and co-creating solutions. The assistant uses CoT to transparently explain how customer input shapes service improvements. · Rapid Prototyping: AI simulates new service concepts (e.g., digital self-service features or loyalty programs) and predicts their impact on customer satisfaction, using digital twins and scenario analysis. · Personalized Service – NLP based insights can guide the design of hyper- personalized offerings, increasing emotional connection with customers. Continuous learning and Innovation Loop – By embedding AI prompts into Quality reviews, every audit or improvement cycle also becomes an innovation session, fuelling a culture of creativity. · Real-Time Monitoring: AI continuously monitors service delivery and customer sentiment, using CoT to flag anomalies and suggest iterative improvements. · Knowledge Management: AI organizes and updates a dynamic knowledge base of best practices, lessons learned, and innovative ideas, making them accessible for ongoing quality and innovation initiatives. Impact and Result on Customer Experience – · Proactive Delight – With intelligent NLP driven prompts, Quality function can not only safeguard standards but also ignite ideas that will redefine the customer experience. Instead of reacting to complaints, Quality can anticipate needs and introduce enhancements before customers ask for them. · Transparency: CoT reasoning makes AI’s decision-making process clear and trustworthy. · Personalization: AI tailors’ innovations to specific customer segments and needs. · Agility: Rapid identification and implementation of new ideas keep the organization ahead of customer expectations. · Collaboration: Customers become partners in innovation, not just recipients of service. In essence AI will spark organization transformation journey where Quality function moves beyond process optimization to become a true engine of innovation—proactively shaping service experiences that delight customers and set new standards in the industry thus becoming an innovation trusted partner. The AI powered Quality Innovation Hub thus will benefit service organization in achieving a breakthrough in moving Quality from a mere cost centre to a profit adding valued powerhouse.

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Yes, AI can transform risk management from mere finding issue only during audits or post occurrence of an incident during RCA. From a reactive approach AI can transform entire risk management into a proactive one and help business in spotting anomalies and flag off early warnings before they become crises. Let us explore the case using an example – Duplicate or fraudulent invoices in Finance. Duplicate or fraudulent invoices are common risk in vendor payments. Traditional system often relies on rule-based checks (e.g. Invoice number+ date+ address + unique id+ account number) and leave it further at the mercy of risk compliance team to detect exceptions that may slip through (e.g. INV 333 vs INV – 333, or vendor name anomalies ART - Adi Ram Tech vs Adi Ram Tool, timing mismatches – same invoice submitted again after 3 months, amount manipulation – same service billed twice under different cost center). By this time (when audit is being done) business has already lost money, plus additional cost of poor quality gets added in recovering the amount. Traditional system needs to evolve and get integrated with AI. AI can be put to use for Pattern and anomaly detection - Fuzzy matching models to detect near duplicates in invoice numbers, vendor names, and address or payment timeline references. Early warning or outlier detection to flag off unusual amounts, frequencies or timings compared to vendor/ payment history. Highlight patterns related to vendor raising invoices - be it the volume, account, amount or time period. Natural language processing to be used for reading invoice description to catch semantic similarity (Adi Ram Tech vs Adi Ram Tool). OCR along with API using NLP. Leverage on existing system and integrate with AI workflow – Process FMEA involving key stakeholders to be done to assess existing controls. Controls that can be fixed to be addressed through process automation. Controls beyond immediate fix to be further brainstormed for potential risk. Identified risks to be scored basis severity and occurrence. Risk mapping to be done and assign a score to each invoice based on probability of fraud or duplication. Build the risk matrix and mapping to be integrated in Risk model either using existing Powerapps + AI model. Set a workflow to trigger early detection. Notifications/alert to be triggered to the mapped action owner as per the risk matrix. System will now be able to flag off high risk invoices before payment and not post payments during audits. Run the model and perform UAT. Loop in the feedback and adjust variables and rerun the model and publish for use. Preventing “Alarm Fatigue” – Risk AI model to avoid overwhelming employees with false positives be configuring workflow , model for · Workflow to trigger notifications to concerned stakeholder to review the risk on detection. - Set rules to block fraudulent invoices considering the risk matrix. Payments to be blocked and exceptions to be routed for manual approval. - Set threshold for escalation as per business risk appetite, that need immediate action - Medium and low risk alert to be reported only for information - Each notification/alert to come with the reason in subject line to keep the user informed and action required. With clear explanations and controlled alert volume, finance teams will take alerts seriously This will enable trust, reduce alarm fatigue and maintain smooth operations, Risk AI model to be further connected to PowerBI for better monitoring, visibility, autonomous and intelligent decision making for a faster action. · Integrate Risk AI model with existing finance controls (Billing system, vendor onboarding – master creation, approval workflows, business rules).Ensure biases are addressed and compliance to security, ethics and privacy protocols. · Approval workflow - Set notification workflow along with risk score and explanation. High risk invoices routed for secondary approval. · Continuous monitoring – E.g. Post payment monitoring for duplicate disbursements across months. Close loop with the feedback – Once published, risk model will continuously learn from historical cases and auditor feedback. Model to be calibrated and republished. Keep the stakeholders informed on the feedback closure. This will build trust and adoption, reduce human fatigue and stress of risk crisis. Performance monitoring - Publish dashboards on set periodicity as part of proactive risk mitigation and monitoring. Continuous monitoring and the feedback loop will make the system robust over period of time. Adopting AI in risk management will benefit any business in an earlier detection, proactive risk management, overall improved efficiency, smarter controls and a happier workforce.

AI definitely has an emerging role to play in workforce optimization beyond the obvious (shift hours, volume forecasts). AI has the capability to move from structured static rule-based estimation flows to adjust to dynamic everchanging demand and what if scenario based predictive and business user specific customized modelling. Influencing factors that need consideration in changing the legacy - Availability of employees - as an impact of climate and environment (monsoon, traffic congestion). Employee wellbeing (health, work fatigue factors – mura, muda, muri) Lack of Support system to take care of family needs (childcare senior citizen care, etc.) Seasonality trends and patterns (weekly, monthly, quarterly, annual, festive) Task nature – Complexity, Competency and skill requirement vs availability, Effort required for tasks as per complexity and competency. Training hours to get proficiency in the job. Time – Experience of the employee- how long in the role and how many years of experience. Time zone in which the maximum volume is received. Employee Preference - Aspirations, expertise, time zone/ shift, location, role Tenure Takt time , SLA penalty risk Employee engagement index Data availability and accuracy of base measures and derived measures Shrinkage (attrition, absenteeism – planned/ unplanned) Employment type (Permanent, Part Time, Contract) Efficiency – First time right, productivity rates Location, Infrastructure cost – Seat availability, cost per seat To ensure AI recommendations are both efficient and fair to employees, Work Force Optimization needs to be a real time data flow management with compliance and ethics addressed at design. 1.Ensure fair and accurate database – Gather influencing factors historical data for optimization and setting the rules. Skill, gender, age and role bias to be debated and addressed involving key stakeholders. Perform outlier analysis, deal exceptions, build what-if scenarios, calibrate the system with biases removed. Model the problem as a constraint using multiple objective optimization and arrive at target variables and set baseline. Example of Work Force Optimization Model = Minimize { ^SLA penalty+^ cost+ ^attrition+^ seat cost} +Maximize {^Employee engagement+^ skill+^ productivity}+ Normalize and Balance out {^tenure+^ age+^time+^ Employee preference} 2.Create Pull – Role based skill competency and career progression matrix to be made. Skill graphs and aspirations to be mapped. Maintain transparency and communicate skills needed to perform current role and what it would need to move to the next level. Cross train resources to address spikes and availability issues. Have scheduled job rotation to address monotonous work. Set prompts to address the condition and reasoning logic. 3. AI modelling – Montecarlo simulations to be performed using baseline. Test and then publish the model. 4. Interactive human workflows – As per the outputs received from Monte Carlo Simulations, set decision instructions and exit conditions. 5. Real time planning – Build APIs to integrate AI model with workforce planning rosters and share it as per access rights for usage. Seek feedback from employees and make changes in variables/ decisions only on approval from key stakeholders. 6. Governance – Audit if ethics - fairness and equality principles, compliance and protocols were adhered. Monitor the performance for enhancement and retrain the model as required. Adopting the approach shall help AI redesign Work Force Optimization while promoting fairness, equality and compliance ethics.

The roots of an organization lie in its culture. To stay competitive, be the innovator and the go-to market brand, organization transformation is needed at a cultural level. For an organization to have this transformation journey, AI can be one of the trusted partner only if used judiciously. Let us debate this with an example. To promote organization wide learning and development, upskilling and efficiency improvement, from a strategy point of view an organization invested on an AI solution. Every month AI report was shared which gave visibility on number of training hours spent at Department level. Say we have Department A with 40 hrs and Department B with 240 hrs. It was but obvious that Department B got recognized. This was misleading as Department A had 5 team members with an average of 8 hrs per spent on training per member whereas Department B had 120 team members with an average training hrs of 2 per member. The feedback was given to the involved stakeholders and right behaviors were promoted. AI workflows were rebuilt to trigger training modules as per Department needs and dashboard was re-aligned to report correct metrics. This was possible because leadership had the visibility to the dashboards and timely actions were taken to make the AI platform more interactive and enabled the organization to promote a learning culture. An average 2hrs of training spent per member moved to an average 8 hrs of training per member within 6months.This also contributed to employee engagement and continual improvement initiatives. For AI to be the trusted advisor for leaders, recommending following steps to be taken by leadership - Creation of Core team - Before onboarding the AI journey, it is crucial to have the buy in and alignment of all key stakeholders. Create core cross functional team having leaders representing domain, business and technical expertise. Strategic Initiative - Leaders are the influencers for setting the strategy. Leaders need to communicate the need and the objective for adoption and adaptation. Corporate to sponsor the strategic initiative. Go big bang with communication. Set the tone at the top. AI goals, KPIs , resources, etc. AI tools to be part of the short-term Annual Business Plan and long term 5-year strategy roadmap. AI awareness – Educate leaders on what AI is and make them aware of its limitation. Leaders to be mindful and cognitive to overcome biases related to cost, technology and speed. Message from leaders to team to maintain ethics and transparency. Building capability – One of the key reasons why AI can be the trusted advisor is for the speed of data availability, accuracy, reliability and faster decision making. Core team to explore and build AI solutions that would meet the business objectives. Core team to involve domain and technical expertise for bias in and bias out checks. Few key criteria’s for evaluation could be Cost, Quality, Risk of failures, Flexibility – Scalability, Processing Time, Implementation speed, data collection, processing, storage capability and ROI. Proof Of Concept - Create prototypes, use cases, assess opportunity, prioritize and validate, design, build, test and deliver. Course correct if needed. Calibrate, train and update knowledge repository. Roll out the solution. Communicate – Address myth that AI is people reduction method and promote it as value creation to customers and business. Send organization wide communication when milestones) are achieved. Share failure and success stories. Capture lesson learnt and submit solution in Knowledge repository. Governance and oversight – Management to have periodic governance. Reinforce ethical adoption of AI and compliance to data security and privacy and role based access to daily dashboards for better monitoring and oversight. Reward & Recognize – To promote and encourage continual improvement celebrate achievements and reward all those involved in making and leading the transformation. AI thus can be the trusted advisor for leadership if adopted in spirit and maintaining integrity and business ethics at the root level.

Q. Bais in, Bais Out: How to break the cycle? Answer - In a service delivery context given example of prioritizing cases, recommending actions and responding to customers, following steps can be take at various stages of solution development – Design stage – · Assess project objective, scope, metrics and success measures with timelines · Reach out to stakeholders incase of difference of opinion. · Interview and empathize the issues faced · Brainstorm and validate assessment criteria.Design FMEA · Course correct metrics and success measures if required · Involve Developers, testers in the kick off call Testing phase – · Develop use test cases. · Build Agentic AI workflow with what-if scenarios, And OR logic · Link knowledge base repository with correct calibrated clean database Monitoring phase – · Intelligent dashboards with powerapps workflow when any shift in data is observed · Calibrate and retraining AI for precision and accuracy. · Periodic governance This is how one can let the Bias IN and then Bias it Out through careful design, testing and monitoring to break the cycle.