Arunungshu

Business excellence focuses on improving what already exists, but true breakthroughs often come from new ideas. AI can scan trends, connect distant concepts, and even generate novel solutions that humans might overlook. In my product (electronic device) development domain, one of the prominent challenges is to keep the profit margin consistent towards the higher side, especially after imposing the new tariffs in one region. To address those strategic challenges, AI can drive the solution. Approach— Since hardware import is part of the tariff imposed, we can shift our focus from hardware to AI-enabled software solutions. For example, we can create an AI solution that will predict the life of a product and diagnose real-time performance. In case of any drop in performance, the software has the ability to repair the same online. We can sell this type of bundled AI solution with the new product to not only increase our revenue and margin but also decrease the demand for spare parts, which in turn will influence fewer export requirements from outside regions. 1> AI-driven subscription solution Instead of relying only on hardware sales, companies can use AI to build high-value service subscriptions that keep devices running at their best. Imagine an “AI Health Check” plan: smart algorithms track how customers use their products, fine-tune performance on the fly, flag upcoming maintenance needs, and even recommend greener upgrades. By turning intelligence inside the device into an ongoing service, businesses can reduce the impact of tariffs on imported components—revenue comes from software rather than heavily taxed hardware. Linking insights from IoT data with models of tariff scenarios, this approach helps clients keep their equipment “tariff-safe,” for example, through remote firmware improvements that avoid costly replacements. Financially, it opens up recurring income with margins of roughly 20–30% (compared with 5–10% for devices alone), cushions tariff pressure by packaging services alongside equipment, and strengthens loyalty by cutting churn. 2> Tariff-Resilient Product Ecosystems One promising direction is to build products that can withstand tariff pressures while staying attractive to customers. Think of modular, upgradeable devices where only the most essential hardware is imported, ideally from countries with lower duties such as Mexico or Vietnam. The real intelligence would sit in the cloud: software powered by AI that keeps improving the device long after it’s sold. For example, “smart adaptive electronics”—like smartphones that adjust their features automatically based on how people use them—could stay useful for years, cutting down on replacement needs. This approach connects tariff analysis with sustainability goals such as the circular economy, and it may even help products qualify for green-tech exemptions. The business upside is significant: less reliance on high-tariff imports (by roughly 15–20%), the ability to charge a 10–15% premium for truly future-ready devices, and lower warranty spending thanks to AI that predicts failures before they happen.

In Account Receivable domain teams focuses towards the open payments from customers and map the payment to corresponding Invoice and update GL Why AI in AR scheduling helps It can match work to people in real time instead of relying on weekly rules. It balances service goals, cost, and employee experience simultaneously. It surfaces tradeoffs and lets managers make informed decisions faster. What an AI scheduler should look at besides shifts and volumes Skills and case fit - Domain expertise (commercial vs. retail receivables) - AI scheduler will create the volume and queue based on domain expertise so that we can allocate adequate resources and balance them as per volume of work - Specific competencies (disputes, reconciliation, litigation support). - AI should create a profile for skill set based on past records of individual employee. Then AI will help team members to propose the allocation based on their skill set and competencies. - Language, region, and currency familiarity for international accounts. - In case of multilingual support AI will try to solve by own AI chat bot system, in case of extended conversation AI will assign the queue based on region and time zone. - System/tool permissions (who can post payments, issue credits, escalate) - Based on historical data AI will allocate the issue to specific person who has the authority to issue credit and handle escalation Case complexity and value - Simple automated reminders vs. high-value or dispute-prone accounts that need senior attention. - RPA deal with simple automated reminders however based on the historical transaction details AI decide where Human in loop is required for high-value or dispute prone critical accounts. - Dollar value and impact on DSO (prioritize high-impact accounts)- AI algorithm filter the high dollar value and DSO impacted invoice items for first follow up schedule - Ageing bucket and SLA sensitivity - Based on aging and SLA sensitivity AI perform the prioritization - Performance signals and learning curves Historical KPIs (AHT, collection rate, dispute resolution rates). - Based on individual performance and criticalness of the customer AI allocate the Invoice accordingly - Time-of-day performance differences (some agents perform better mornings vs. evenings). - Ramp-up time for new tasks — avoid assigning high-risk accounts to recently reassigned agents. Real-time events - Billing runs, system outages, client campaigns, sudden churn spikes are the factors which AI consider during effective and efficient schedule management How to make AI recommendations efficient and fair for employees Build a multi-objective recommendation engine - Treat scheduling as a problem with multiple goal like meeting SLAs, Magnify recovery, Optimize cost and reduce unfairness. - Leverage hard constraints for laws and union rules; use soft constraints or penalties for preferences and fairness tradeoffs. Define measurable fairness metrics - Examples: variance in overtime hours, Use more weight to weekend assignments, average “undesirability” score per agent. - Track these on rolling windows (4, 12, 24 weeks) so small short-term imbalances don’t compound unnoticed. Make tradeoffs explicit and tunable to take appropriate decision - Show managers the cost of prioritizing fairness (e.g., temporarily slower SLA) and let them adjust weightings. - Maintain a dashboard that shows how changing a parameter shifts both service and fairness metrics. The dashboard must give the transparent visibility to leadership while taking the decision. - Human-in-the-loop and transparency - Present recommended schedules with clear reasons: “Assigned to Nancy because she has dispute certification and is 30% more likely to close this account.” - Allow managers and agents to propose swaps or vetoes with a clear audit trail. - Offer simple swapping comment “If you swap this shift to Alex, SLA risk increases by X% but overtime cost drops by Y.” It will help manager to take the decision in a comprehensive way

Use case in PTP Domain One of the prominent use case is Vendor query resolution system in my domain. In Procurement to Payment domain, vendor reaches to know the status of their Invoice which was shared by vendor during the product/goods delivery. There could be various status of Invoice processing cycle before any organization release payment. Vendor reaches frequently to any organization until the payment has been released. Vendor expect prompt response to know the invoice payment status so that they can plan their cash flow. In the other hand Organization face enormous challenges to solve the vendor query and vendor get offended because of late response. Let me summarize the precise challenges. 1> Often Vendor support team not aware about the processing status of a Invoice 2> They refer multiple tools, such as excel, ERP, Bank Portal etc. to get the processing status of any specific invoice 3> Since the knowledge or information of Invoice is scattered and fragmented to many tools hence the agent spent more time to consolidate and share with Vendor. 4> Vendor make escalation because of this kind of delay in response. 5> Supplier/Vendor will be more irate if they receive any generic response about their invoice processing or payment status. They want specific answer about the invoice status such as "Payment released", "Payment credited" , "Payment on hold due to goods are in transit" etc. Solution 1> AI agent powered by prompt and flow delivered personalization response to individual Vendor queries. 2> The AI agent enquired the Invoice number to Vendor. Once vendor provided the invoice number then the flow first attempted to search the internal KB 3> The KB was created by API integration of various fragmented tools such as SAP and many different Bank Portal 4> Now when vendor ask for any specific query about any specific Invoice then the agent could able to search the internal KB to provide the specific information about the invoice processing status 5> In case the agent type incorrect invoice number then AI agent refer the external model capability and ask to verify the Invoice number. After deploying this solution we observed significant reduction in Vendor escalation regarding late payment issue and moreover the vendor satisfaction also was significantly enhanced.

In the procurement-to-payment domain, poor knowledge management creates waste or risk of vendor dissatisfaction and delays in payment. PTP is a global team that deals with invoice processing from invoice receipt to payment release. The vendors are spread across 20+ countries globally and different time zones with diverse languages and tax codes. The PTP team observed enormous challenges while determining the tax code in vendor invoices during payment. Because, based on the tax code, the payment amount will be adjusted and released. Let me elaborate the issue below • Tax codes vary across the globe and are not integrated into the ERP. Different geographies require different tax codes (GST, VAT, withholding tax, exemptions). • Policies and rate updates are often scattered across emails, PDFs, or local folders which stores in isolated locations like individuals Sharepoint • Employees spend hours checking SAP notes, internal guidelines, and past cases. Associated waste & risk • Wrong postings will have ripple effect on rework and delay in vendor payment and in turn vendor dissatisfaction • In case of government tax payment, this will lead to compliance issue • In case of early payment vendors, risk of incurring penalties are high An AI Solution was created to address this KB issue. Below Prompt+ flow-based AI solution transform that knowledge into a real competitive advantage • Step 1 – Prompt: Once the invoice reaches the user for payment processing. Then user will write the below prompt“What is the correct GST code for a consulting invoice from Vendor X, billed in Kolkata, service date Jan 2025?” • Step 2 – Flow: o AI fetches policy documents, recent tax circulars, and historic postings which are in AI optimized format o Because the tax code and policy change over time, we need to change the KB accordingly. We preferred a solution that would have features such as content organization, version control, draft management, and search and tracability. We opted for Document 360. o Contextualizes the query (vendor type, geography, service category) using Content organization feature of Doc 360 o It recommends the correct tax code and confidence score. o Generate a short explanation (“This vendor is registered under Kolkata GST, services are consulting, applicable rate 18% as per 2025 notification”). o Links to source documents for the audit trail where proper tagging has already been done. • Step 3 – Action: AI pushes the selected tax code directly into SAP entry after validation with user with the help of API integration features of Doc360 In this way, the AI-infused solution helped consolidate fragmented tax codes and notes and employee assumptions, turning diverse knowledge into a real-time, contextual decision engine.

Bias is inevitable in AI enabled processes as the data or pattern of AI algorithms contains those Bias. Hence to optimize the Bias we need to measure Voice to Noise ration. The objective would be to optimize the noise to get the optimum voice from the data pattern, prompts & flows which are the source of Bias. Below are the overview of the process. Invoice Prioritization & Payment Scheduling AI agents will help to determine the priority of Invoice processing based on empirical data. In case the training was stressed only on past payments data then Focus only on those vendors who make high volume and value transaction while deferring small/local suppliers. Adequate focus will not be given to the newer vendors because there’s less historical data Urgency will be biassed on invoice literature, giving advantage to vendors with “Clean & Crisp” submissions. Steps to Prevent and Minimize Bias 1. Design Phase Modify Training Data: Ensure vendor data caters all types of Vendors (large, mid-size, small , new entrants) so AI doesn’t give additional weight to big players. Ensure Transparency: Define clear business rules such as all vendors, irrespective of size or demography, should be paid within agreed terms. 2. Testing Phase Bias Test: Test identical invoices across various strata of Vendor (local vs. global, large vs. small) to confirm AI recommendations are consistent. 3. Monitoring Phase Dashboards: Track payment schedule across vendor categories. Highlight if some specific groups consistently get delayed. HIL: Introduce Human In Loop model, let Account payables team override AI-driven prioritization where they observe AI bias. Feedback Incorporation: Store vendor complaints in case of not on time payment and use them as indication in model bias reduction. Ensure optimum residual value i.e Highest voice lowest noise.