Arun Gokul_112916

While Bex supported View A, I support View B: Wait for understanding. In high-precision industries, acting on a black box is not a solution; it is a technical debt that eventually breaks the system. For example - consider biologics manufacturing in the pharma industry. These processes involve living cells that are notoriously temperamental. If an AI predicts a sudden drop in batch yield but cannot explain why, a manager might be tempted to intervene by adjusting the temperature or nutrient feeds based on the black box's hunch. if you don't know the root cause, your intervention might actually be what kills the batch. Without the why, you cannot distinguish between a sensor malfunction and a genuine biological shift. If you act and the batch fails anyway, you have learned nothing. You haven't improved the process; you've just added more noise to the data. Besides, acting on unexplained predictions creates a dangerous dependency trap. The moment the team stops asking why and starts just doing what the machine says, it loses its internal engineering capability. Over time, the team loses the ability to troubleshoot when the AI eventually - and inevitably - encounters a scenario it wasn't trained for. The final say should stay with the human expert, and the decision to act must be based on a verified causal link. It is better to lose one batch and gain a permanent understanding of a new failure mode than to save ten batches and remain permanently ignorant of how your own system works.

In the contact center industry, the most common hidden inefficiency is the Escalation for Visibility trap. This happens when mid-level managers maintain high ticket volumes in specialized back-office queues to justify their function's budget and HC. Anyways, it creates a situation where agents are not allowed from solving simple issue - like processing a basic refund - even though they have the necessary context. A recent analysis by an AI Model on ticket metadata might show that 45% of escalations to Senior Billing Specialists are for tasks that take <3 minutes and require no actual expert judgment. The AI pattern shows that these tickets are being moved primarily to keep the senior team's utilization rates looking high. The problem is that a senior leader might have authorized the creation of this specialist team two years ago to solve a specific crisis. Acknowledging that the team is now a bottleneck would mean admitting the original structural decision is obsolete. To avoid a defensive reaction, the project should move the conversation away from manager performance and toward agent autonomy. The focus should be on the technical permission gaps the AI found. The transition needs to be framed as a system update. Basically, the data isn't used to cut staff, but to identify which system permissions need to be pushed to the front line to lower the average handle time for customers. This avoids the "puffery" often found in AI-generated corporate responses. The organization should use a "Pilot and Pivot" model. They can test the AI’s suggested routing on a small team for two weeks. If the customer satisfaction scores go up and the backlog drops, the evidence becomes too practical to ignore. It bypasses the hierarchy by making the results the primary authority. The final say should belong to the Product Owner of the CRM or the systems architect, not the function head. The basis for the decision is the Exception Rate. If the manager claims the work is too complex for entry-level staff, they have to prove it by showing a high error rate in the pilot phase. If the error rate stays low, the system architect overrides the legacy policy.

In a typical contact center environment, Ops leads prioritize based on what is currently alarming on the dashboard. If the live chat queue is blowing up, they pull people off back-office email processing to handle the immediate noise. This is a reactive instinct. An AI model, however, looks at historical demand patterns and downstream consequences. It might recommend staying on the email queue because those emails contain high-value cancellation requests that, if not processed within two hours, result in permanent churn. The AI forecasts that the chat spike is a temporary 15-minute anomaly, whereas the email delay is a long-term revenue risk. How the Conflict is Resolved When the AI and the humans disagree, the resolution shouldn't be a simple override. It requires a Logic Audit based on two specific criteria: a) The Unknown Variable Check: The human must identify a real-world factor the AI cannot see. For example, is there a massive regional internet outage or a viral social media post causing the spike? b) The Risk Horizon Comparison: If there is no outside anomaly, the AI's recommendation wins. Humans are biologically wired to prioritize the immediate (the ringing phone) over the abstract (the future churn). The framework forces the team to prioritize the forecast impact over the current noise. Who Has the Final Say? The human lead holds the veto power, but only on the basis of Contextual Anomalies. If a Ops Lead chooses to override the AI, they have to document the specific external reason. This creates a feedback loop. If they override because they feel it's right and the result is a massive backlog elsewhere, the data will show that. The final say rests with the human, but the basis of the decision must move from seniority and instinct to external context.

In the BPO / Contact Center industry, a boundary exists between Front-Office Support (agents handling live customer interactions) and Back-Office Processing (teams managing data entry, claims, or complex ticket resolutions). Today, an agent identifies a complex issue - like a billing dispute or a specialized insurance claim and escalates it to the back office. This results in a delay during which the customer may wait several days for a solution. As Agentic AI integrates into the CRM and telephony stacks, this handoff becomes a relic of a pre-automated era. Real-Time Execution: AI co-pilots can now perform back-office tasks - like database lookups or policy validation -while the agent is still on the phone. The agent no longer asks a teammate for help The Death of Tier 1: Bots handle 90% of basic inquiries. This pushes all human agents into Tier 2 or Expert territory, where they must understand both the emotional context and the technical backend. Back-office roles shift from performing the data entry to auditing the AI’s high-volume processing. Their role is no longer to carry out the tasks themselves, but to make sure the AI’s reasoning aligns with evolving regulatory requirements. The traditional "Front vs. Back" split is replaced by a Unified Model: The "Agent" and "Processor" merge into a Resolution Expert. This individual is a versatile professional who leverages AI to maneuver through complex internal systems in real time. They take full responsibility for the CX, from the initial greeting to the final system update. A subset of tenured agents moves into Prompt and Policy Engineering. They don't take calls; they refine the KB that the AI uses to answer customers and execute tasks. New Coordination Model (HITL): A Performance Architect looks at the Human + AI output. Coordination is no longer focused on tracking talk time, but rather on monitoring the exception rate - the 5% of situations where the AI falls short and human intervention becomes necessary.

A/B testing refers to a method of comparing two versions of a marketing campaign (website/app) against each other to determine which one performs better. For example..In an experiment, 2 or more variants of a website/app are shown to users at random, and statistical analysis is used to determine which variation performs better. InA/B Testing, A refers to 'control' or the original variable whereas B refers to 'variation' or a new version of the original variable. The idea behind A/B testing is that you show the existing version of the product to the control group and the variated version of the product to the experimental group. The difference in performance in experimental vs control group is tracked, to identify the effect of this new version of the product on the performance. The version that moves business metric in the positive direction is known as the 'winner. This essentially leads to testing new product variants that will improve the performance of the existing product. It also helps in getting direct feedback from the actual users by presenting them the existing vs variated product options and in this way they can quickly test new ideas.

Hi All, I am working in a premier ITES company based in India. I have completed my Six Sigma Green Belt Certification from BSS in 2014. I am currently working in Medical Devices Domain of the company. Our primary work would be report preparation, protocol creation, CAD activities, Design Verification Plan, Design Verification Protocol, in plain words it would include more of documentation work and some sample statistical analysis for supporting the results. I want to implement Process Improvement/ Six Sigma project in my current work. Please guide me how to identify one business case/pain area? Kindly revert if you need more information. Thanks in advance, Arun