Bijesh

Domain: Customer Support Operations – Handling escalations and service-level agreements (SLAs) A Fortune 500 enterprise software company deployed an AI model to predict escalations and improve SLA compliance. The CS team assumed that escalations were primarily caused by missed deadlines or slow responses. Processes are therefore designed to have strict SLA timers, auto-escalation rules, and agent coaching on speed. What was fed to the AI: 3 years of ticket data (≈ 1.8 million cases) Escalation logs and SLA breach reports Customer sentiment scores from surveys Activity timelines Knowledge Based usage patterns What was the AI’s job? The AI was designed to find patterns that predict escalations and suggest interventions. It analyzed: Ticket text and agent responses Customer sentiment trends Escalation logs Agent adherence to scripts The results: 90% of the time a predictable remixing with superficial tweaks was observed. The AI confirms what the team already “knew”. For example, tickets with long first-response times indicated higher escalation risk. Negative sentiment in initial email showed higher escalation risk It recommended better auto-routing and faster first replies. And even more predictable was the team’s response, “Nice job.” But Nothing surprising. The Revelation: Then comes the 10% case where the actual blind spot remained which nobody saw. Citing a real example that occurred with that premium enterprise customers of APAC region in February 2025. The AI flagged something bizarre: Escalations were NOT driven by SLA breaches or impatience. They spiked when customers followed the script too perfectly. What did the AI do? The AI model was designed to predict escalation risk and recommend interventions. When it correlated escalation patterns, it found something counterintuitive. Tickets that adhered to script had a 2.4 x higher escalation rate for APAC premium customers compared to partially customized responses. Why this Happened? During further analysis some cultural and behavioral dependencies were revealed: APAC premium customers preferred relationship-building and informal tone of communication. They expected proactive suggestions, not rigid compliance. Scripted responses indicated robotic and too formal tone, suggesting a perception of lack of warmth in the conversation or lack of ownership. Customers interpreted this as “They’re hiding something or not taking my issue seriously.” This distrust triggered escalations even when SLAs were met. Hidden Assumption Exposed The team believed: Consistency = professionalism In reality, for this domain : Personalization = trust This assumption had silently shaped training, QA, and performance metrics for years making team believe that scripted language was safe and accurate however it became a liability. AI’s behavioral tweak suggestion. AI proposed a dynamic tone-adjustment model. Understand the customer's persona and sentiment in real time; accordingly suggest response tone (formal vs conversational). Notify when an agent is using too much from the standard script. Notable Results: Escalations dropped by 37% in APAC premium accounts within 3 months with no SLA changes and no extra staffing. All that required was just a tone adaptation. Lessons learnt That insight was genuinely creative. No human manager had ever questioned the “formal = good” assumption. The training data contained zero explicit examples of tone causing escalations. The AI generated patterns for sentiment analysis, escalation triggers, and linguistic style existed but how to tweak and tune these based on domain applicability is the biggest takeaway. The AI didn’t just optimize speed; it challenged a cultural assumption baked into our playbook. Conclusion In most cases, AI behaves as a powerful pattern-matching engine that expeditiously applies proven best practices quicker than any human analyst. However occasionally, around 10% of the time, it uncovers hidden blind spots that force us to rethink entire processes. This rare but critical insight is where true operational creativity thrives. It’s not because AI “thinks,” but because it can explore possibilities no human team would ever consider. It is when those assumptions are revealed that the impacts are far-fetched and it may reshape workflows, agent training, and even our definition of what great service looks like.

Domain: Enterprise L&D & Process Optimization in a Global Electronic manufacturing MNC- Fox Fonn (Name masked) (Context: Fox Fonn has multi-country plants, thousands of employees in production, quality, and supply chain; continuous onboarding, compliance training, and process improvement) The creative task- Design KPI-linked training journeys that adapt to live operational signals and suggest micro-improvement ideas or Process fixes (checklists, prompts, playbooks or Kaizen ideas). What is currently happening in the company. AI-driven curriculum creation & simulations At Fox Fonn, AI systems now create training modules for Surface Mount Technology (SMT) operators. When the goal is specified -to reduce soldering defects by 20 %, the AI creates competency maps, assembles micro-learning modules, and builds realistic troubleshooting simulations for common failure modes (e.g., defective soldering, misaligned parts, and profile errors) Operation linked adaptive learning Training connects to MES (Manufacturing Execution Systems). If defect rates increase on Line 4, the AI triggers a quick simulation on root causes (e.g., misalignment). The data gets updated weekly based on live production feedback. Continuous idea harvesting AI extracts operator feedback and machine logs to suggest improvements (e.g., “Add a pre-check for misalignment,”. These creative Ideas are clustered and sent to process engineers with impact scores. Results thus far Onboarding time is reduced for new line operators where training is completed in 7 days against previous 12 days with defect rate down 12% post-onboarding. Process stability: MTBF (Mean Time Between Failures) for machine improved 18–22% after AI-suggested checks were undertaken and with updated SOPs. Idea generation: From earlier approx. 25 ideas to presently 150 ideas per quarter, while 30% have been implemented (e.g., minor fixture tweaks, checklist changes). Now is this AI being creative or remixing? What is Creative? AI generates new interventions like alignment calibration prompt or simulation for soldering errors—based on real-time failure patterns. It hypothesizes, tests, and iterates. Where is it still remixed? Dialogue tone and scenario framing reuse familiar patterns and templates. The construct or framing: The tone, eg: “line one requires feed defective”, “material inputs quality check “often borrow established patterns. The AI’s reporting framing can feel familiar or even repetitive because it pulls from common phrases and reusable conventions or themes. Risk of cosmetic change: Without effective constraints, AI generates simulations that seem new but are superficial variants (different names for same logic). Unless checked, this leads to decorative remix rather than substantive creativity. What helped Fox Fonn to safeguard "real creativity"? Diverse and high-quality data training using varied sources such as internal training materials, industry best practices, academic research, multimedia content. Including global perspectives for electronic MNCs to avoid cultural bias in learning content Fresh data binding by connecting learning content or scenarios to the latest, real-world data so that training stays relevant. Automatically remove or update outdated training scenarios that no longer reflect current processes, technologies, or compliance requirements Human review for sensitive content. Instructional designers review AI-generated learning modules before deployment A/B test interventions where a comparison of two versions (A - Current training approach and B- New intervention (e.g., micro-learning, gamification, AI-driven personalization) is done to see which performs better and randomly assign learners to either A or B and measure outcomes. Bottom line In electronics manufacturing domain, AI is not just remixing but it’s creating new, useful interventions that improve yield and speed. The winners are those who link training to live data and let AI iterate fast. whereas the Static training models that can’t keep up will end up as losers.

Domain: Global Employee Expense Reimbursement in a Multi-Organization Ecosystem (Background: An MNC with 60,000 employees having a global employee expense management program with a shared-services BPO partner handling approximately 1.5M expense claims annually) In a global Employee expense reimbursement, there are several originations partnering together to process a claim from submission to Payout. A typical expense reimbursement process flow is as follows: - An employee submits an expense on a system -> the BPO partner picks up for validation -> an AI system classifies the expense -> the compliance vendor checks for tax and other statutory rules -> an auditor vendor then picks up samples and validates transactions for fraud -> and finally, the report for clearance reaches back to the parent organization’s Corporate Finance Team who approves and pays out. Five organizations located in three continents, enabled by multiple AI systems, are involved all interacting seamlessly for the same expense reimbursement. The challenge: The multiple AI system owned and operated by these five partners; all optimize the actions for its own objective. One looks exclusive into fraud detection, the other into policy compliance, tax accuracy, or processing speed. When systems don’t have the same rationale, collaboration stalls and efficiency gives way to friction. Where Transparency Matters Most Six months ago, we deployed an AI-driven anomaly detection engine to flag suspicious or incorrect claims (duplicate meals, erroneous taxi bills, miscategorized “customer entertainment”). While our BPO partner having a homegrown ML model that kept a tab on processing efficiency of Expense reimbursements, the audit vendor relied on a third party enabled fraud-risk algorithm. All three models treated the same expense claim differently and the ecosystem stalled. A single hotel bill was High-risk for the corporate AI (out-of-policy location) and a Low-effort for the BPO AI (clean receipt) while it was tagged Medium-risk for the audit vendor’s model (price anomaly vs city index) In this case, as a fallout, the employee faced delays. All business partners considered their own system as correct and did not trust others AI since nobody understood the reasoning behind these decisions. This is where transparency at the right level determines success or failure. So, let’s answer the question- How much transparency should an AI System Share? 1. High Transparency for Reason Codes but not sharing the raw model logic The AI system must explain its rationale in a standard and simple way. Eg: Travel exceeds policy limit for the day Duplicate receipt detected within last 30 days Price variance from approved rates Such information is safe, actionable, and develops trust across partners by sharing the reasoning without exposing the hard codes behind. 2. Medium Transparency for Risk Scoring Frameworks Each AI model should share its risk scale, not its proprietary scoring method. For example: 0–0.4 = Low 0.5–0.7 = Medium 0.8–1.0 = High By aligning processes, organizations can implement tiering, prioritize effectively, and maintain consistent workflows—all while safeguarding critical IP. 3. Protected Transparency for Shared Data (but not private data) Here the data sharing is on essentially needed basis or only what’s necessary for the other party: Receipt images → essentially required Corporate credit card statements → contractual and essentially required Employee performance ratings → absolutely not Internal travel-approval memos → not required The rule is simple: If a vendor doesn’t need it for decision making, they shouldn’t access it. 4. Nil Transparency for Proprietary Algorithms or Competitive Intelligence Millions have been invested in fraud-detection models where Audit vendors and BPO partners wants accuracy for the data to be reliable for Corporate Finance. Such algorithms will be patented or may be trade secrets. Similarly, the Internal budget structures, Approval hierarchies and Sensitive vendor negotiations are sensitive information and need data protection. Transparency here would destroy competitive advantage or create legal risk. A Fine and reasonable Balance Here’s what we learned: High transparency causes the vendors to feel exposed, corporates leak sensitive workflows, employees may reverse-engineer the system. Very low Transparency results in delays, mistrust and rework with collaboration failures between partnering organizations. The Balance is a mutually agreed and established AI transparency protocol across all partners: What to share: Include elements such as reason codes, the risk scale (but not the actual score), relevant claim data, and the final decision along with its rationale. What not to share: Sensitive components like algorithms, feature weights, proprietary training datasets, or details related to financial exposure and internal negotiations. This balance made the ecosystem predictable, fair, and auditable — without compromising competitive, contractual, or compliance boundaries.

AI in Finance Excellence: Balancing Efficiency with Empathy in Customer Relationships AI is turning into the voice and face of organizations. From shaping customer experiences, it is going beyond automation and analytics. While AI can personalize responses and anticipate the needs of the customers, strong relationships with them are built on empathy, trust, and genuineness that cannot be compromised for efficiency alone. Customer-Facing Process: Complaint Handling for Warranty Claims In the domain of finance excellence of an electronics manufacturer, complaint handling for warranty claims and refunds is one of the critical customer-facing process. This process plays a vital role in building as it involves sensitive financial transactions and customer’s perception of fairness. AI Can Strengthen Relationships in the following ways: - Personalized Communication: AI can analyze purchase history, warranty details, and prior interactions to craft empathetic responses. Eg: the Company received input of a customer’s laptop that had keyboard sensitivity issues. The built in AI sensed the issue with the change in key stroke behavioral patterns and prompted a conversation with the User acknowledging the inconvenience and offer expedited resolution options based on the purchase records. Here Tone alignment is of vital importance. If AI uses overly cheerful language when a customer is frustrated about an ongoing glitch, it can appear insensitive Result: Customers felt understood, not treated as ticket numbers queued up for resolution. Predictive Assistance: AI can predict potential complaints by monitoring product performance data and proactively reach out before issues escalate. The AI also gives an expected time to resolution based on past trends Eg: A particular batch of laptops had battery draining issues. The AI prompted an alert to the affected customers and initiated a replacement process while suggesting nearest service centers or alternatively customer site service with an expected turnaround time based on location of customer. Result: Proactive care builds trust and reduces frustration. Financial Transparency: AI used by Customer service department has dashboards that provides real-time updates on refund status, warranty coverage, and expected timelines, reducing anxiety and uncertainty for customers. Risk of Over-Automation: Denying claims based solely on algorithmic rules without human review can erode trust, therefore HITL (Human In The Loop) is a control placed to have a check on such rejections. Result : Transparency fosters confidence in the brand. Key Takeaways: In finance excellence, AI can transform complaint handling from a routine transactional process into a trust-building opportunity however the success essentially pivots on respecting customer emotions and sentiments, fairness and transparency while maintaining a human touch where it matters most.

Rule Based chat bots are getting obsolete with newer and modern AI solutions beckoning a change. Every technology has a lifecycle. This statement is true and not limited to physical products, but we have seen that it extends to software solutions too. In my domain, a Chat Bot was introduced to assist Customer Support in 2022. The Rule-Based Bot was developed using early AI systems that had predefined decision structures and responded to matched key words. In today’s world of NLP models these technologies stand obsolete in the world of AI. Key signals to Bot's obsolescence 1. Limited Understanding: The Bot failed to handle unstructured language or queries that were complex. 2. Unsatisfactory User Experience: The users gave feedback that the Bot response is not as expected, and chat was closed abruptly due to limited options ending up speaking with Human agent. 3. Scalability constraints: Upgrading the system to accommodate requirements as per Business Growth was limited. This forced continuation of legacy system left little scope for system improvement leading to progressive performance issues. 4. No Learning Capability: The critical factor noted was that the system had no scope of learning or training itself towards improvement from the interactions. The Customer Care design team gathered to analyze certain key metrics to convince the Leadership into the need for replacement of this Chat Bot. Some important metrics that were leveraged to indicate Obsolescence. Additionally, Industry standards were compared to benchmark against them. They were as follows: Customer Satisfaction Score (CSAT): Legacy bot: 60–70% Modern AI (LLMs): ~85–90% First Contact Resolution (FCR): Legacy bot: ~40–50% Modern AI: ~75–80% Average Handling Time (AHT): Legacy bot: Higher due to comprehending capabilities, further escalations and Human touchpoints. Modern AI: Significantly lower due to higher FCR Maintenance Cost: Legacy bot: High due to manual rule updates. Cost-benefit imbalance Lifecycle: Legacy bot: 2-4 years before major redesign Modern AI: 6–12 months as the model versions evolve rapidly Learning Ability: Legacy bot: System remains Static over time Modern AI: Keeps improving based on interactions Other factors that trigger Sunset or Replacement 1. Performance Degradation-Drop in accuracy and increase in false positives 2. Regulatory Changes: New compliance requirements (e.g., updation of KYC norms) that the current system cannot accommodate without major rework. 3. Technology limitations to integrate to new APIs for real-time data access. How to manage this transition? Post Leadership alignment on the need to replace the ChatBot, the next step was to have a Management of Change (MOC) framework in place. Few steps to manage this transition were - Creating the MOC board with representation from IT, Compliance, Business Process Owners, and User Experience teams Define scope, objectives, and success metrics for the new system with expected accuracy, resolution rate, tone, response speed (where possible with metrics) Parallel deployment of both systems: Deploy the new system alongside the old one for a defined period while analyzing and validating accuracy and reliability of the new system in comparison to the old. Knowledge transfer: Extract FAQ databases, conversation flows, intents, and historical user queries from the old system. Documenting the historical failure patterns. Assess impact and risks: Maintain rollback capability to the old system during initial phases. User engagement and communication: Early announcement of new system rollout explaining the necessity of upgrade and benefits of the new system. Effective lifecycle management of AI systems includes recognizing early indicators of obsolescence. A proactive mindset, monitoring of performance of existing systems, staying ahead of the curve through timely identification, quick solutioning, timely execution and responsible transition keeps the business at a competitive advantage ensuring business continuity and institutional learning.