Pavitra Jain

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.

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.