Lakshmi_Iyer

In the aerospace domain, particularly within airport services and operations powered by SAP technologies, a frequent conflict arises between operational efficiency and passenger experience. Taking this as a scenario, trying to elaborate my take on how both these good goals create a conflict and how AI could be leveraged here. For example, SAP-based AI systems managing gate assignments or baggage handling may face a trade-off between minimizing turnaround time (efficiency) and ensuring personalized service (experience). An AI might recommend rerouting ground staff or reallocating gates to optimize aircraft flow, but this could result in longer walking distances for passengers or reduced accessibility services. To navigate this, AI should be designed to: Analyse real-time operational metrics (e.g., aircraft delays, gate availability, staff capacity) Incorporate passenger-centric data (e.g., special assistance requests, connecting flight urgency) Simulate outcomes to forecast the impact of prioritizing one goal over another with the help of statistics and already existing datasets The decision logic should be dynamic — for instance, during peak hours, efficiency may take precedence, while during off-peak times, passenger comfort could be prioritized. Human oversight plays a pivotal role in setting these contextual thresholds. Airport operations managers, supported by SAP dashboards & monitoring mechanism, must define business rules and override AI decisions when exceptions arise for e.g. VIP movements, emergency landings, or regulatory constraints Metrics AI Should Monitor To make informed decisions, AI systems integrated with SAP (e.g., SAP S/4HANA, SAP EWM, SAP Fiori) should continuously monitor: Operational Efficiency Metrics Aircraft turnaround time Gate utilization rates Staff availability and workload Baggage transfer time Real-time flight schedules and delays Passenger Experience Metrics Walking distance to gates Wait times at check-in, security, and boarding Special assistance requests (e.g., elderly, disabled passengers) Connection times for transfer passengers Passenger satisfaction scores (from surveys, feedback apps, random on the go feedback on airport waiting areas to get the Gemba validation) Contextual and External Factors Weather conditions Regulatory compliance requirements Emergency Landings or VIP movements Real-time disruptions (e.g., system outages, strikes) Important Factor: How Should AI Decide Which Goal to Prioritize? AI should not rely on static rules but instead use context-aware decision logic. Dynamic Thresholds: AI can adjust priorities based on time of day, passenger volume, or disruption severity. For example, during peak hours, prioritize efficiency; during off-peak, enhance comfort Weighted Scoring Models: Assign weights to each metric based on business rules. For instance, if a flight is delayed but carries many connecting passengers, AI may prioritize passenger experience over gate optimization Predictive Analytics: Use historical data to forecast the impact of decisions. If rerouting baggage saves ~5 - 10 minutes but increases mishandling risk by ~20 – 30 %, AI can flag it for human review Scenario Simulation: AI can simulate multiple outcomes and recommend the one with the least operational risk and highest passenger satisfaction Important Parameter: Role of Human Oversight Human oversight is essential to ensure AI decisions align with strategic, ethical, and regulatory considerations: Define Guardrails: Operations managers must set business rules and thresholds that AI cannot override such as accessibility standards or regulatory mandates Override Mechanism: SAP dashboards should allow supervisors to override AI decisions in real time, especially during emergencies or exceptions Continuous Feedback Loop: Human feedback should be fed back into the AI model to improve future decision-making. For example, if AI’s gate assignment led to passenger complaints, that data should refine future logic Ethical Judgment: AI may not fully grasp nuances like cultural sensitivities or reputational risks. Human judgment ensures decisions are empathetic and brand aligned Efficiency vs Experience - AI should act as a decision support system, not a decision maker. By combining real-time SAP data, predictive intelligence, and human oversight, Airport Ops can strike the right balance between efficiency and experience — building trust with passengers while maintaining operational excellence

I am trying to give a shot to this question with the help of a scenario where we support Energy & Utilities Industry client. The Client operates on SAP technology as its core system and trying to visualize how humans and AI can collaborate. Scenario – Predictive Maintenance of Power Grid Equipment Systems monitor equipment (like transformers, circuit breakers, etc.) using sensor data, historical failure patterns & environmental conditions to predict potential failures. Field Engineers then validate, check, or override these predictions based on field experience and subject knowledge AI learning from Humans – Engineers might ignore a failure prediction because they know the equipment was recently serviced or the sensor is faulty. AI should learn to incorporate such overrides and flag similar patterns in future predictions. Humans may delay maintenance due to local festivals, weather conditions, or workforce availability. AI can learn to factor in these constraints when scheduling or prioritizing tasks. When humans diagnose root causes post-failure, AI should include this feedback to refine its models (e.g., distinguishing between thermal overload vs. insulation breakdown). These show scenarios from Contextual overrides, environmental, social factors and Failure attribution. Predictive Analytics via SAP Predictive Maintenance and Service IoT Integration using SAP Asset Intelligence Network Machine Learning models embedded in SAP BTP (Business Technology Platform) Humans learning from AI - AI can detect subtle correlations (e.g., humidity spikes + voltage fluctuations = higher failure risk) that humans may not identify easily. Engineers can use these insights to revise inspection protocols or preventive measures. AI can suggest which assets need urgent attention based on risk scores, helping humans prioritize better. This improves operational efficiency and reduces downtime. AI provides 24*7 monitoring, alerting humans to anomalies they wouldn’t catch during periodic inspections. These show areas for implied patterns in data, optimal HC allocation, triaging mechanisms. Field engineers and planners interact through SAP PM (Plant Maintenance), feeding in real-world insights, overrides, and contextual decisions. For effective deployment of AI with Human framework, we should work on an effective governance model – Feedback Capture: Every human override or comment is logged in SAP Model Retraining: AI models in SAP BTP are updated using this feedback Insight Delivery: AI-generated insights are pushed to SAP Fiori apps or mobile dashboards Human Validation: Engineers validate or reject insights, creating a continual improvement cycle This model will help reap benefits in long term through reduced equipment failure rates, optimized maintenance costs, improved grid reliability, enhanced and trusted workforce productivity.