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I support View B — Do not rely on non-explainable AI, especially in high-stakes processes like insurance claims. Efficiency without accountability is a fragile advantage — it works until the first serious challenge. Here’s the core issue: a decision that cannot be explained cannot be defended, trusted, or improved. In regulated and customer-facing domains, that’s not a minor gap — it’s a structural risk. Why View B is strongerAI in this example is not just optimizing a backend process — it is making decisions that directly impact customers’ financial outcomes. That changes the standard completely. Regulatory risk: Insurance is heavily regulated. If a rejected customer challenges a claim legally, “the AI decided so” is not a valid defense. Customer trust erosion: A fast rejection with no explanation feels arbitrary and unfair — even if statistically correct. No continuous improvement: If you don’t understand why decisions are made, you can’t identify bias, correct errors, or refine logic. Efficiency gains are real — but they are not durable without transparency. Real operational example (Finance & Accounting context)Consider an AI-driven invoice approval system in Accounts Payable: It auto-approves or blocks invoices based on patterns (vendor behavior, pricing anomalies, contract matching) Processing time drops by 50–70% Duplicate and fraudulent invoices reduce significantly Now imagine: A vendor’s invoice gets rejected The procurement or AP team cannot explain why Vendor disputes escalate Payments get delayed → supplier relationships deteriorate Impact: Working capital disruptions Vendor distrust Audit complications (especially during statutory audits) In finance, every decision must be auditable and traceable. A black-box AI breaks that fundamental requirement. The deeper problem: Hidden risk accumulationNon-explainable AI creates a dangerous illusion: Everything is working — until suddenly it isn’t Without explainability Bias can silently creep in Incorrect patterns can get reinforced Edge cases remain invisible By the time issues surface, damage is already done at scale

I support View B — Do not implement the change. Speed means nothing if what arrives is wrong. A 20% gain in processing time sounds like progress, but a 10% increase in incorrect shipments isn't a side effect you manage later — it's a failure you've engineered into your core operation from the start. The moment you accept that trade-off without fixing the error side first, you've optimised for the metric that's easiest to measure and ignored the one that actually determines whether your customer comes back. Here's the thing about incorrect shipments that often gets underestimated: the cost doesn't stay in the warehouse. It travels. A wrong item triggers a return. That return requires reverse logistics, inspection, restocking, and a customer service interaction. In e-commerce, the fully loaded cost of processing a return typically runs between 50 and 65 percent of the item's original value. So the order you fulfilled 20% faster just cost you more than you made on it. And customers don't absorb errors quietly. They leave reviews, they dispute charges, and — most importantly — they simply don't reorder. Speed impressed them once. The wrong item is what they remember. A Shared Services Centre example makes this even clearer. Consider an SSC handling Accounts Payable for a large manufacturing group — processing around 40,000 supplier invoices per month. The finance team deploys an AI tool to automate three-way matching: aligning the purchase order, goods receipt note, and supplier invoice. Processing time drops from four days to same-day. The SSC head celebrates the turnaround improvement in the next leadership review. But the AI model wasn't adequately trained on vendor-specific exceptions — blanket POs, partial delivery tolerances, early payment discount windows that vary by contract. A 10% error rate sets in quietly. At 40,000 invoices a month, that's 4,000 mismatches going through unchecked every single month. What happens next is entirely predictable: A critical packaging supplier flags unresolved invoice disputes and puts the account on payment hold. Procurement can't raise new POs. Production planning gets disrupted because raw material orders are blocked. What began as an AP processing speed win has now cascaded into a supply chain stoppage — and the SSC is answering to operations, finance, and the CFO simultaneously. Meanwhile, the team that automation was supposed to free up is now spending every working day on exception clearing and vendor reconciliation calls. The cost per invoice — when you factor in that rework — ends up higher than the original manual process. The speed gain didn't disappear. It turned negative. That is precisely the risk hiding inside View A's logic. "Errors can be addressed separately" assumes errors are patient. They aren't. In the warehouse scenario, incorrect shipments create return inventory, return inventory distorts stock counts, distorted stock counts cause more picking errors, and more picking errors push the error rate higher than the original 10%. You haven't contained the problem — you've given it a mechanism to grow. The right sequence is not implement first, fix later. It is fix first, then scale. Run the AI change in a controlled pilot on a defined subset of orders. Measure the error rate in that environment. Bring it down to at or below the baseline before full deployment. Then the 20% speed gain is real, sustainable, and doesn't carry a liability attached to every shipment. A change that makes you faster at delivering the wrong outcome isn't an improvement. It's an accelerated path to losing the customer.

I strongly support View B — Keep humans in control of implementation. AI can optimize processes, but it cannot own consequences. The moment implementation becomes autonomous, the organization is not just accelerating improvement — it may be accelerate risk without accountability. Why human control must remainHigh confidence ≠ full context. AI models are trained on historical patterns, but process changes often have second-order impacts — compliance exposure, customer trust erosion, or downstream operational bottlenecks — that may not exist in the data. Human oversight acts as a risk filter, not a speed blocker. Real time exampleIn a Procure-to-Pay (P2P) process: AI detects that auto-approving invoices below ₹50,000 can reduce cycle time by 30%. Based on historical data, it reaches high confidence and implements the rule automatically. Possible risks we might face as belowVendors may start splitting invoices to bypass controls. Certain invoices may still require GST compliance checks depending on category. Fraud risk increases because the control threshold becomes predictable. Internal audit flags control weakness, impacting compliance ratings. These risks are not pattern-based anomalies but they are behavioral and regulatory consequences, where human judgment is critical.

I support View B — Wait until the organization is ready. AI-driven insights can be extremely valuable, but process change succeeds only when people adopt it. Implementing a technically superior solution without organizational readiness often leads to poor adoption, confusion, and ultimately failure of the initiative. In process excellence and transformation, sustainable improvement comes from aligning technology, process, and people simultaneously. When teams are not prepared, three risks emerge. First, operational disruption can occur because employees may not fully understand the new workflow or system changes. Second, loss of trust in AI recommendations can happen if the change initially reduces productivity or creates errors. Third, shadow processes may emerge where employees revert to old methods to protect performance metrics. These factors can erase the benefits that the AI initially identified. A better approach is to treat the AI recommendation as a strong hypothesis that requires structured change management. The organization should conduct pilot testing, involve key stakeholders, provide targeted training, and communicate the expected benefits clearly. This ensures that when the change is implemented, it is both technically effective and operationally sustainable. Example from my F&A clinet In Order-to-Cash process we faced similar challenge where AI analysis of system data identifies that automating order validation and credit checks could reduce processing delays by 25%. Technically, the change is beneficial. However, implementing it immediately without involving team members may lead to several problems in future such as Customer service teams may not understand new exception handling rules by AI tool Finance compliance teams may worry about incorrect credit approvals. Sales teams may fear that automation could delay urgent customer orders and can impact on key customers. Hence, If we rolled out change abruptly, teams may resist using the new workflow or escalate issues unnecessarily, temporarily increasing order errors and customer dissatisfaction. Instead, a structured readiness approach can be adopt where we can plan steps as below Running a pilot implementation with one region or product line. Conducting training sessions on the new automated validation rules. Establishing clear escalation paths for exceptions. Communicating performance improvements through dashboards and leadership updates. Once employees see that the AI-enabled process improves efficiency while maintaining control, adoption increases significantly.

I support View B — Keep the feature and fix selectively. When a feature benefits the vast majority of users, rolling it back immediately can unnecessarily slow down innovation and reduce the overall value delivered by the product/solution. In this case, more than 90% of users are experiencing improved results, which indicates that the feature is fundamentally effective. Removing it completely would penalize the majority because of issues affecting a smaller segment. A more balanced approach is to keep the feature active while prioritizing targeted fixes for the affected users. AI monitoring systems can identify patterns such as device type, configuration, or usage flow, enabling teams to deploy segmented patches, compatibility improvements, or temporary workarounds for the impacted group. This ensures that the majority continues to benefit while the minority receives focused remediation. A similar situation happened in on of my client account where we deployed AI-based invoice capture and validation tools that automatically read invoices and post them into ERP system (SAP) Where the AI automation successfully processed 90–92% of invoices accurately, but 8–10% of invoices from certain vendors with unusual formats failed or else created posting errors. In this cases, we did not roll back the entire automation. Instead, we keep the automation active for the majority of vendors while creating exception handling workflows for the problematic vendor formats and retraining the AI model to improve recognition accuracy. This approach protected our efficiency gain due to automation . Therefore, maintaining the feature while implementing targeted corrections for the affected segment is the more practical and strategically sound decision, as it preserves progress without ignoring the needs of the minority.