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Position (View B) : Wait for Understanding Before Acting on AI Predictions I take a clear position against Bex’s argument. Organizations should not act solely on unexplained AI predictions. Instead, they should prioritize understanding the underlying mechanism before operationalizing the prediction, especially in complex service environments like BPO. Acting blindly on black-box predictions may provide short-term risk reduction, but it undermines long-term process improvement, operational transparency, and sustainable performance management. Bex’s aerospace example emphasizes catastrophic failure avoidance. However, BPO operations are fundamentally different. They are human-centric, process-driven systems where improvement depends on understanding why issues occur so that root causes can be eliminated. In this environment, acting on unexplained predictions converts operations into a reactive firefighting loop rather than a continuously improving system. Why Acting Without Understanding Is Dangerous in BPO Operations There are three major risks when organizations blindly follow black-box AI predictions: 1. It Creates Operational Dependency If the organization cannot explain why failures occur, it becomes dependent on the AI system for decision-making. This weakens internal capability and process ownership. 2. It Prevents Root Cause Elimination Prediction helps avoid failure once, but understanding eliminates failure permanently. BPO process excellence relies on structured frameworks such as Lean, Six Sigma, and root cause analysis, which require explainability. 3. It Can Drive Incorrect Operational Actions AI models may identify correlations rather than causation. Acting without explanation may lead teams to take corrective actions that do not address the real issue, or worse, introduce new inefficiencies. For knowledge-intensive operations like BPO, explainability is not a luxury—it is foundational to operational governance. BPO Industry Example: AI Prediction in Payment Processing Quality Failures Consider a finance and accounting (F&A) BPO process handling vendor invoice payments for a global enterprise client. Process Context The BPO team processes 50,000 invoices per month. Errors in invoice validation can lead to: Duplicate payments Compliance violations Vendor disputes Financial reconciliation delays To improve quality, the organization deploys an AI model that predicts invoices likely to fail downstream audit checks. The AI flags invoices with 92% prediction accuracy, but it cannot explain which factors drive the prediction. What Happens if the Organization Follows Bex's Approach If the team acts blindly on predictions, they would: Route flagged invoices to manual review Delay processing for those transactions Add extra verification steps This may temporarily reduce audit failures. However, after several months the organization notices: Manual workload increases by 35% Invoice cycle time increases Process cost per invoice rises Root causes of errors remain unknown The organization has effectively created a permanent inspection layer rather than improving the process itself. The AI becomes a dependency rather than a capability enhancer. The Better Approach: Wait for Understanding Instead of immediately operationalizing the model, the organization conducts model interpretability analysis using techniques such as: SHAP value analysis Feature importance mapping Process correlation studies This reveals something unexpected. The AI predictions are primarily triggered by: Invoices submitted in non-standard PDF formats Vendor names containing abbreviations Invoices from a specific regional procurement team Further investigation reveals the root cause: A regional procurement team recently implemented a new invoice submission template, which the OCR extraction system struggles to parse correctly. This creates incorrect field extraction, leading to downstream validation errors. Outcome of Understanding the Mechanism Instead of permanently reviewing flagged invoices, the organization implements three targeted fixes: Standardize invoice submission format across vendors Update OCR extraction rules for the new template Train vendors on proper invoice formatting Within two months: Invoice error rate drops by 60% Manual review volume reduces significantly Processing time improves AI prediction alerts decrease naturally The organization eliminated the root cause instead of reacting to symptoms. This outcome would not have been possible if the team simply followed unexplained predictions. Why BPO Requires Explainable Intelligence BPO organizations are measured not only by operational outcomes but also by: Process transparency Continuous improvement capability Client governance and auditability Knowledge transfer sustainability Clients expect service providers to explain why issues occur and how they are prevented, not simply say “the AI told us so.” Blind reliance on AI predictions creates opaque operations, which is unacceptable in regulated or client-audited environments such as: Finance and accounting outsourcing Insurance claims processing Healthcare revenue cycle management In these industries, explainability is operational credibility. Conclusion While Bex argues that outcomes matter more than explanations, this logic applies mainly to physical asset failure environments like aerospace maintenance. BPO operations are fundamentally different. In BPO processes, long-term performance comes from understanding and eliminating process variation, not merely predicting it. Therefore, organizations should wait for understanding before acting on AI predictions. Prediction without explanation may prevent immediate failure, but understanding transforms the system so failures stop occurring altogether.