Pavitra Jain

Our AI-powered B2B commerce platform offers solution for complex buying experiences with custom catalogs, quotes, recurring orders and order templates. AI is already used for advanced capabilities like personalized pricing and smart product discovery through product search and recommendations. Even then, the B2B buyers have trouble finding the right products quickly as they have to navigate extensive catalogs, compare technical specifications and reorder supplies under tight deadlines. The buyer can get frustrated if there are delays due to inefficient search or lack of clarity in the product specification which may lead them to competitors. Our customers on the other hand receive support requests which can overwhelm the sales and service team with routine inquiries which makes it difficult to provide personalized attention to high-value customers. Copilot for Buyers Imagine an enhanced customer experience with the AI assistant which will streamline the buying journey by serving as a digital sales and service assistant. It facilitates the buyers to find the right products, order tracking or provide product specification documents all in real time. The Copilot will be embedded in the existing selling stores like the customer portal, mobile app, or website where it can be invoked, like the chatbots, to provide expert guidance to ensure a smooth and efficient buying experience. Copilot for Buyers integrates seamlessly into the existing commerce platform to instantly connect product data, manuals, and support documentation. It will provides real-time, context-aware assistance to help customers find products and make purchase decisions through natural language queries, voice commands, or image recognition. We will build support in over 180 languages so that our customers can offer a consistent, personalized experience to a global audience. Key Features Problem-based discovery: Copilot understands customer challenges to zero in on the right product. Conversational & multimodal interaction: Buyers engage naturally through text, voice, or even images to find the right product Round-the-clock support: The Copilot can pull up product docs and can match with the specifications provided without human technical support. Efficient ordering & reordering: It offers step-by-step guidance for B2B purchases by understanding the customer's quotes, order templates and order history and customers can use AI workflows for quick purchases and one-click repeat orders. Seamless interaction & integration: Adapts to customer processes, brand, and UI and there are no forced one-template-fits-all approach. Deep integration into the commerce flow: It helps add products to cart, compares products and more, directly from the conversation Multi-agent orchestration: Orchestrates multiple AI agents, including internal and external systems, for seamless end-to-end automation. Multi-language support: Multi-language support (180+ languages) ensures consistent, localized experiences for global enterprises. By combining AI’s creative capacity with our strong B2B commerce foundation, the Copilot for Buyers can take our product beyond efficiency gains to reimagine the customer experience for making faster, smarter, and more intuitive purchasing.

At our e-commerce platform company (PAAS model), we support many customers who rely on our system as their primary revenue channel. This means we must ensure continuous uptime and meet strict SLAs. With hundreds of customers with deep B2B implementation the issues are many and varied and these customers spread across the world with their own business hours and support windows. So for a product company like ours, the ticket management system is a complex but finely balanced art of juggling customer needs with support, operations and engineering team. Uniquely to our product and company's need the solution has to address the following Technical Complexity: Tickets range from simple configuration queries to complex integration issues requiring deep platform knowledge. Customer Impact: B2B clients have varying SLA requirements based on contract tiers and business criticality. Knowledge Specialization: Agents have different expertise areas (catalog management, payment processing, API integrations, etc.) Escalation Timing: Critical issues require immediate escalation paths to senior specialists or development teams in case of product issues keeping customer's business hours. Of these the biggest challenge to consider is the agents domain expertise (e.g., database, networks, systems integrations - CRMs, ERPs, PIMS, payment gateways) and match it with preferred escalation types, knowledge and performance history. This is the most important aspect which was analyzed in depth and formed the anvil on which the solution rests. The Baseline - We need AI to rapidly generate optimized schedule plans by incorporating availability, preferences, skills, leave, seniority, and labor rules which can surpass the current manual spreadsheet-based scheduling. - Automatically reassign critical tickets in real time as these can be unattended The Input - Provide historical ticket data which includes issue type/time taken, agent response times and success rates of escalation with resolution. - Connect with Contract Management, ServiceNow, Personio, Product release calendar - Detailed Agents skill-matrix The Solution Primary Objectives Minimize Customer Impact: Give priority to high-value customers and business-critical issues Knowledge Utilization Efficiency: Match the right expert to the right problems Operational Cost Management: Reducing overtime while maintaining service level Defined Success Metrics Set clear and measurable KPIs to evaluate the solution on - Improved adherence to SLA - Reduction in resolution times, and - Increase in agent satisfaction. Key Design Areas AI Decision Logic Establish weights to help AI resolves conflicts for e.g. managing ticket/SLA urgency with agent preferences Edge Cases Handling Provide clear guidance designed to tackle scenarios such as the unavailability of specialists or urgent matters lacking a designated assignee. Data Privacy & Compliance Due to large amounts of personal data that is needed strict checks are instituted to preserve privacy and comply with local labor laws for Europe and other geographies Additional factors Skill Development Balance workload with growth opportunities for agents and ensuring all agents receive growth-promoting challenging tickets. Identify optimal times to pair specialists with generalists for knowledge transfer. Equitable Workload Measure workload by cognitive/difficulty effort and not just ticket count Stress Index Balancing: As our company has work council mandated one-on-one session with People manager every month with stress as an input Agent Preference Dynamic Preference Learning: AI learns individual agent preferences through behavior patterns Collaborative Filtering: Match agents with tickets similar to ones they've successfully resolved Work-Life Balance Optimization: Consider personal commitments, commute times, cultural sensitivity Transparency Build explainable AI: e.g., “This ticket went to you because it fits your Salesforce expertise and you handled last one 48 hours ago.” Decision Audit Trail: Every scheduling decision includes reasoning that agents can review Bias Detection Monitoring: Continuous analysis to prevent discrimination in shift assignments Feedback Loop: Agents can contest decisions with explanations required from the AI With this we are envisaging an AI solution that can transform workforce scheduling from reactive fire-fighting to proactive optimization, balancing technical expertise requirements, customer impact priorities, and agent wellbeing all balanced out in real-time.

In my opinion the question is not Can AI Turn Knowledge Into a Competitive Edge? I want to argue that NOT having AI knowledge will give you Competitive Disadvantage. NOT having AI knowledge is no longer a neutral stance it is a strategic liability in today’s rapidly evolving AI landscape. NOT understanding or engaging with AI isn’t just a missed opportunity, it poses an active risk of being outpaced by those already learning, adapting, and building with it. We’ve seen this story before, when electricity was first harnessed, it was a scientific curiosity exciting but abstract. But soon, its practical applications began to reshape the world heralding Industrial revolution. Those who adopted had automated production lines, powered transport, lit cities. The speed, efficiency, and scale became their advantage. AI is following the same trajectory. It’s more than tool for automation or a back-office efficiency play. It’s a foundational capability—one that turns data into decisions, complexity into clarity, and speed into a strategy. Organizations that understand this are pulling ahead, not because they have AI, but because they know how to work with it. The edge lies in application. Companies are using AI to streamline their operations, personalize the customer experiences, and make better decisions faster. For instance, in our product development company AI helps teams to rapidly sift through user questions, support tickets, and qualitative feedback to identify what really matters. That insight translates to faster responsiveness, quick iteration and tighter product-market fit. Meanwhile, those who are still sitting on the sidelines—waiting for “the right moment” or “clearer ROI”—are already at a disadvantage. Delay doesn’t just mean slower progress it means falling further behind as others scale their AI capabilities and create better offerings in shorter timeframes. We no longer ask whether electricity gives a business an edge, it is just assumed and soon the same will be true of AI. It will be invisible but indispensable woven into decisions, processes, and products in ways we no longer even think about. This is the start of AI Revolution. So yes, AI can absolutely turn knowledge into a competitive edge. But more importantly, lacking that knowledge is already putting you at a disadvantage and without positive action that gap is only going to grow.

AI-Powered Incident Management System At our e-commerce platform company (PAAS model), we need to support environments of all customers who rely on our system for their online ecommerce sales and we need to have continuous uptime and meet strict SLAs. We use NewRelic as our observability platform which collects massive volumes of logs, traces and metrics. After filtering we are analyzing over 60TB of telemetry data per month. We’re now building a system to query this data for AI model integration, enabling anomaly detection, incident correlation, and predictive analytics. Decision Making Scenario- Analyzing and classifying incidents in real time When an Alert is generated the Operations Manager need to make quick decisions during disruptions like outages, performance degradation, or critical bugs. These incidents needs to be evaluated quickly due to SLA requirements since they directly impact buyer satisfaction, conversion rates, and revenue for our clients. The challenge is to prioritize correctly, weighing severity, scope, resource constraints when choosing action steps. AI Agent Support We have been working on a design of an Incident Response AI Assistant with the final goal that it will - Aggregates and analyzes real-time data, such as error logs, user requests, system metrics, and incident tickets. - Scores incidents by combining: - Business impact (example: conversion drop, cart abandonment) - Customers or geographies affected - Urgency indicators like large number of error log entries, memory or cpu spike - Generates and recommends priority and type based on pre-learned categorization - Suggests response paths to help mitigate the problem - Quick mitigation (rollback the latest patch) - Escalation (pass to development team) - Monitoring (observe performance metrics) - Offers confidence scores on its solution For this we are creating solution with two key components to support the decision-making and the communication 1) Incident Analyzer Bot which will automatically detect system issues using AI/ML Learn from historical incidents to categorize alerts by severity and type and reduce false positives Correlate related events — for example, grouping three different alerts under a single outage — to help managers see the full picture quickly Find and provide reference of related incidents from our historical data to help provide the manager with information about the previous RCA and solution 2) Ops Chatbot is focused on communication (not customer-facing) If a critical issue is detected, it can notify customers automatically and proactively before they notice it themselves. It supports manual overrides, customizable communication methods (like email, message, chat) with pre-defined message templates. Makes message suggestions to the manager, who can review and approve them directly in tools like Microsoft Teams. Manages follow-ups automatically if the issue remains unresolved — for example, sending timed updates like “we are still analyzing your system” every 1, 6, 12 hrs depending on the case. This AI assistant will help to surface and prioritize incidents quickly but the final decision remains with the manager who reviews the categorization, solution recommendation and approves the communication suggested by AI. The manager will be have full discretion to override, approve, or modify the AI’s actions. We have planned to create feedback loop to help the system learn and improve over time and implement accuracy monitoring by comparing AI predictions to actual outcomes, review the AI's confidence scoring especially when AI uncertainty is high. This regular validation against historical incidents should help us ensure that the human/AI-assistant together work towards meeting customer's SLAs.

At our e-commerce product company, we have an AI powered search and recommendation engine feature. It can be configured on each customer project to leverage multiple data sources (ERP, e-commerce, PIM, purchase history) to personalize search and product recommendations. Personalization features include adjusting results based on purchase history, brand preference, and customer profiles. Our learning has been The recommendation engine can personalize shop assortment for different customer segments. While designing customer flows for this feature, we must ensure that the engine does not unintentionally limit catalog visibility or surface exclusive categories disproportionately. If historical purchase data, browsing patterns, or segment profiles reflect societal biases (e.g., preferences along gender, age, ethnicity, or socioeconomic lines), the algorithms can and will replicate and propagate these biases—such as recommending certain products less to some demographic groups or showing limited assortments. Segment-based catalog restriction could reinforce silos and limit choices for certain customer groups, mirroring or reinforcing pre-existing marketplace or data biases. Customizing algorithmic weighting based on customer profiling without scrutiny could favor or disadvantage groups. We had a real example of a sports attire retailer using our product where we experienced that “Inclusive Sizing” (sizes beyond standard American XS–XL, such as plus sizes or petite/tall fit) appeared in only about 10% of products in a given search result. The dynamic facets logic tended to omit these size attribute from the filters entirely. As a result: Customers seeking inclusive sizes were unable to filter effectively. The represented bias favoured mainstream size ranges, thus marginalizing niche segments. The system then further skewed visibility toward products that align with majority sizing, and had potential to worsening representation over time. Some real world complains from users were - "I can never find anything smart with a good price in my size unless they are your top-of-the-line products" - "I see models wearing new designs in the ads but I can't find enough trendy but age-appropriate colours on the website" Additionally, one real risk that was evaluated was that our model/engine might consistently push popular products from high-traffic regions, while under-representing niche or emerging markets. This not only skews visibility but may also limit growth opportunities for less dominant segments. Some steps that we have attempted to apply Design Phase - Curate diverse and representative data inputs - Allow manual overrides for known critical attributes and for attributes deemed socially or commercially significant (e.g., inclusive sizing, accessibility features) were treated as “defined facets,” ensuring consistent visibility regardless of prevalence. - Ethical guardrails in personalization logic: Forbid certain features (like region or size) from driving recommendation weighting unless justified. Testing Phase - Synthetic Test Profiles across demographics - Manual Testing to find if the engine is developing such biases Monitor and Audit Facet Presentation - Track which facets are consistently hidden across queries and evaluate whether they represent systematically underrepresented groups or product lines - Before releasing compliance review is emphasized on Legal, Privacy(GDPR), Security & Accessibility These proactive steps are now taken on early and help ensure our AI serves all buyers fairly, avoiding the “bias in, bias out” trap in new implementation projects.