Preethi Bijesh

Chosen BPO Process--> Accounts Payable (Invoice processing & vendor payment validation) In finance process,Accounts Payable (AP) is one of the most widely outsourced and AI-automated process. It is high-volume, rule-driven, compliance-sensitive, and directly impacts cash flow and vendor relationships making it an ideal example of AI-embedded workflow transformation. When AI enters AP operations, employees move from processing invoices to supervising financial decision flows. 1. (Pre-AI) Workflow and performance expectations Workflow before AI Invoice was received via email/portal. Agent manually enters invoice data (vendor name, PO number, amount, tax details). Three-way match (Invoice vs PO vs Goods Receipt). Identify discrepancies and email stakeholders. Route for approval. Post for payment. Performance expectations before AI Speed--> Invoice turnaround time, volume per day. Accuracy--> Data entry precision, minimal posting errors. Compliance--> Adherence to approval matrix and audit controls. Cost Control--> Avoid duplicate or incorrect payments. High performance means processing large volumes quickly with minimal errors. The work was repetitive but required attention to detail. 2. ( Post- AI) AI now functions as an automation layer plus decision assistant. Typical AI capabilities include OCR-based invoice data extraction. Automatic PO matching. Duplicate invoice detection. Anomaly detection (unusual amounts, vendor changes). Payment prioritization recommendations. Risk scoring for fraud or compliance flags. The AP analyst is no longer primarily entering data. Instead, they validate exceptions, investigate anomalies, and approve or override AI recommendations. Their role shifts from processor to financial control reviewer. 3. Where AI Improves results( High volume Month-end closures) During month-end or quarter-end, invoice volumes surge. Manual processing leads to: Backlogs Late payments Duplicate entries Approval bottlenecks AI improves outcomes by Instantly matching large volumes of invoices. Identifying duplicates before posting. Highlighting high-risk transactions. Prioritizing urgent vendor payments. This reduces payment delays, strengthens vendor trust, and improves working capital management. 4.Where AI introduces risk(Over-Reliance on Anomaly Detection) AI models flag “unusual” invoices based on historical patterns. But, A legitimate new vendor may be flagged as suspicious. Seasonal price increases may trigger false alerts. Incorrect OCR extraction may distort invoice values. Risks include Payment Delays--> Excessive false positives slow approvals. Vendor Friction--> Legitimate invoices held unnecessarily. Blind Trust--> Analysts approve auto-matched invoices without review. If AI suggestions go unchallenged, financial control risk increases rather than decreases. New skills become essential Analysts must assess whether anomalies are genuinely risky or contextually justified. Exception Analysis-->Instead of processing everything, employees focus on edge cases and discrepancies. Override Accountability-->Knowing when to trust AI auto-match versus when to escalate becomes critical. Audit-Ready Documentation-->Clear reasoning for overrides or approvals must be recorded for compliance audits. Skills that become less critical Fast manual data entry. Memorizing approval matrices. Pure volume-based performance. Speed without validation can lead to financial leakage. How performance metrics should change Replace Invoices per Day → Exception Resolution Quality Processing Speed → Downstream Error Rate Add First-time match accuracy. Override correctness rate. Duplicate payment prevention rate. Vendor satisfaction indicators. Metrics must reward sound financial judgment, not blind automation. Summary In AI-embedded Accounts Payable operations, responsibility does not decrease it intensifies. The AP professional evolves from invoice processor to financial risk controller. High performance now means-->Accurate judgment, controlled trust in AI, and clear accountability not just speed. In an AI-embedded world, careers advance based on how well professionals supervise intelligent systems and not how fast they execute repetitive tasks.

Hiring of medical professional AI used as a decision support tool AI systems like IBM Watson Health are increasingly used in modern hospital system to assist doctors in diagnosing patients. Within seconds, these systems can analyse the X rays, MRI’s CT Scan etc ..It can analyse any abnormalities, suggest ,diagnoses and some time highlight risk based on the data available in historical records. Here AI is not replacing the doctor. Though AI is providing support, the final medical decision is taken by the Doctor as the ethical and legal responsibility still with human professionals. AI can contribute to the analytical process but human is in charge of accountability. This has significantly changed for an organization what is the expectation from a medical professional. Hiring criteria before AI integration Earlier the hiring decision was taken based on the deep medical knowledge, memory recall and the skill to do independent diagnostic ability. Doctors had to check and interpret the scans manually. Connect the symptoms shown in the report to the disease and treatment completely based on the experience gained. A strong candidate was someone who could(Before AI) Detect any abnormalities in reports accurately. Quick recall of the medical condition. Confident decision making even under tough times. Performance of the professional was based on the accuracy ,speed and technical competence. Ie, the reward based on individuals’ expertise and independent execution. Performance was measured by diagnostic accuracy, speed, and technical competence. In simple terms, the system rewarded individual expertise and independent execution. Doctor’s role after AI After involving AI into daily work, the doctors role changes. Instead doing diagnosis from a blank slate, doctors review the AI generated suggestion.AI already show warnings in reports and scan and propose possible conditions and treatments. Here the role of doctor shift from a sole analyser to a reviewer and a decision maker. Due to this, hiring criteria also changes. Instead of checking if the candidate can manually identify and interpret everything, the checking should be on whether the candidate can critically assess AI recommendation and take ownership of the outcome. Importance of critical judgment and analysis Eg:-Lets consider a case where a lung scan is flagged as potential cancer by AI due to a detected shadow. If the doctor reviewing this report is a skilled professional, they won’t believe it immediately. They do further check the medical history and identify the shadow matches scar tissue from a past infection. Here the doctors value lies in the reasoning, experience and sound judgement. This example shows an important hiring shift. Organizations must assess if the candidate can detect AI errors, understand the limitation of system, confidence in override the AI recommendation if needed. The ability to think and take decision even when there is technology support becomes the main competency. Ethical and practical decision making in an AI environment AI give recommendation based on the statistics and clinical outcome. But the medical decisions are not purely mathematical eg: - An AI tool might recommend an expensive treatment because of the data showing high chance of survival. However, doctor should consider the patience financial capacity, physical strength to do the treatment, mental health and the personal value. Since the doctors should balance data insight with empathy and ethical responsibility, hiring must be evaluated on emotional intelligence, communication skills etc. The human professional must ensure that decisions are not only medically good but practical as well. Summary – A shift in hiring process When AI is part of the thinking process, the hiring should not only be based on technical expertise to take independent decision, but someone who can collaborate with intelligent system, question and take a decision with full accountability. Thus, the hiring process shift from criteria of testing memory, speed ,technical execution, judgment ,accountability ,reasoning and ethical maturing to thinking better, supervising technology wisely, owning the consequence of decisions. Traditional written tests are no longer enough. They should be given scenarios and request to detect and decide during practical test. Basic understanding of AI limitations and comfort in working with intelligent systems becomes essential.

Use case:- Motor accident claims (Insurance operations) Importance of the process In an insurance operations,Motor accident claim assessment is very important.Whether to approve,reject or investigate a claim has direct financial impact . To guide the officers,AI system checks the accident photos,history of the driver ,police report etc.Even though AI can detect any fraud and predict the cost of repair,human takes the final decision.A wrong rejection can result in the customer complaints and regulatory issues where as a wrong approval can cause financial loss. Scenario1:AI was right but human action was wrong For a vehicle damage,a customer submitted claim. AI reviewed the images and reports. AI found inconsistency and suggested for further investigation indicating fraud.But the officer focused on the traditional evaluation on the speed and focused on the daily case closure count .To meet the low turn around time and further delays,the claim is approved ignoring the alert from AI. When there was internal audit,it was pointed that the claim was fraud and company had a big loss.On the performance score point,the office still received good score because as per the old matrices they reward for speed and not on the decision quality.This shows that,if the metrics is based on the low turnaround time, the agent is encouraged to ignore AI even though it is correct. Her the performance metrics of the Officer should be changed to quality than the speed. Scenario2:Human followed AI but the officer got negative score Here AI reviewed an accident claim and recommended to APPROVE because the estimate on repair and the damage in photos were matching with the incident description.The officer trusted AI here and approved immediately.After a week,there is an alert on this claim because the AI failed to consider the vehicle model which had a known recall that could have changed the liability rules. Here there is a system gap and the officer is not at fault. The officer here received a negative score for approving incorrectly. This discourages the adoption of AI and is not fair. When AI is part of the workflow performance metrics must separate human error from AI error. Humans should not be punished for following system guidance unless missed mandatory verification steps are missed by them How performance metrics must change based on these scenarios The above scenarios clearly shows that the performance metrics should be evaluated based on the quality and noton speed when AI is involved.Officers performance should be measured based on whether they used AI appropriate , followed all guidelines, put more attention on the high risk cases and the out come is reflecting on the decision making.Instead of focusing on the low turn around time and closure or multiple claims,focus should be on the quality of the claim closure.If the officer is overriding the AI recommendation, check if it is been justified and whether it had any financial loss. At the same time,the performance of AI should also be tracked. AI should be marked with negative score if the AI recommendation is at wrong and AI should be evaluated on the rule awareness accuracy and reliability.Officer must check if all the required elements like Vehicle model rules,Repair eligibility ,Policy exclusions are considered instead of blindly following AI. Conclusion In our case,performance metrics changes when AI is part of the work flow.Human metrics should be more on the decision accuracy ,proper use of AI recommendations ,Justification on overriding AI recommendation, Financial impact and Compliance checks. AI should also be measured on its rules coverage and reliability. This creates a fair and balanced evaluation where the responsibility is shared between human and AI and can improve the process together.

What Should Teams Learn When AI Advice Is Ignored or Proven Wrong? The following case study in Credit Underwriting shall bring out the frontline Risk Operations. Importance of the process In order to approve, decline, or verify applications, the Credit underwriting process is vital where AI risk models help underwriters in decision making. Although these AI models can assess data like credit bureau reports, bank statements, income patterns, and device signals, however humans are needed to make the final decision. Approving wrongly can result in financial issues and regulatory issues. Underwrite Teams should have good knowledge on identifying when the AI is wrong and when humans are wrong . AI always gives option to Approve ,Decline or Verify .AI gives reasons for the same. In our Scenario 1, AI is wrong but human followed it.With high confidence AI approved the loan.However,it is later defaulted. The reason was AI model missed the indications like address change and income stability.The explanation given by model was not clear and the score of confidence was not reliable .Incase of high risk cases,the process should push to Verify and understand the reasons provided by the AI .In this case the responsibility is on both human and model owners. In the Scenario 2, The human has made mistake by declining the loan based on intuition thouah AI asked to Verify .Though the application was genuine,the loan is rejected. AI should have explained in better and clearer way .Also,human decision should have been more based on thinking and facts than bias or intuition . AI should have a track record on when the human decisions went wrong.There should be proper justification for each rejection by human. Below important approaches to improve the process. Team-->There should be a regular review on the AI/human mistakes, and the AI should be fed with the information regularly to improve further.Should keep in mind that AI/Human mistakes are common and act accordingly Human-->Judge and record decisions. Should consider AI as a support and not an authority. Decision should be based on the evidence and not intuition or bias. AI-->Should show the pros and cons ,evidence and the past cases. Model Owners-->Should manage the model quality. Tools should include tracking override patterns, calibration, and decision time. Process Owners--> Set rules and Reviews on regular basis. Records-->There should be record on all all decisions which should be logged with AI output, human action, and evidence. Policy-->Ther should be clear policy on who can override the data. Conclusion When we follow this approach,it reduces errors ,speed up decisions,improve fairness and build AI trust. It helps to have a clear documentation and steady improvements. When AI or humans fail, teams must understand why. Model gasps should be fixed , the explanation should be improved ,Process should be strengthen and guide human judgment. Responsibility is shared across the model,people and process.

Domain--> SAP BW Finance Reporting - Daily Revenue and Margin Decision Support. Overview--> Here we discuss when Finance Controllers and Sales Leaders can rely on AI recommendations in SAP BW reports and when and when AI should be overwritten by Human decisions. Business Context-->Business review daily Margin and Revenue using BW reports These reports are used by Finance controllers and Sales Leaders to check · The daily revenues · Any Abnormal discounts · Corrective Actions An AI layer is embedded into SAP BW dashboards to · Detect abnormalities in revenue or margin · Predict short term trends · Recommend actions The recommendation provided by AI is directly going to affect the business decisions. Hence it is important to know when to use AI and when not to use. When to Trust AI · You should trust AI suggestions when: · SAP BW data is clean and complete. · The pattern is consistent and based on solid historical data. · The AI's recommendation matches past business behaviour. · Previous AI alerts of the same type were accurate. When to Override AI · There are business events not yet captured in data like new contracts. · Recent SAP/BW configuration changes may affect numbers. · The AI explanation is unclear or incomplete. · The decision has a large financial impact. Override Rules are · Document the reason for overriding. · Overrides above a financial limit need manager approval. · Overrides are logged and reviewed weekly. How Overrides can improve AI · Feeds into model training. · Adjusts rules and thresholds. · Reduces false alerts. Governance Model · AI gives the first recommendation and humans validate exceptions. · Define clear rules when AI can act automatically. · Have a weekly review of the decisions and overrides. · AI should clearly explain what changed and why. Business Benefits · Quick daily decision making · More trust in SAP BW reports · Reduced manual analysis effort · Improve collaboration between Finance and Sales Summary Use AI for repetitive data driven patterns in SAP BW reporting. Override AI when high impact decision needs to be taken. The goal is not blind trusting of AI but a balance between AI and People