Topics in Focus

Fundamental changes in how technology is used.

Background: Traditional credit models exclude 45% of addressable customers due to thin-file limitations, while manual underwriting creates 5-7 day approval cycles and 20-30% operational costs, limiting competitiveness against digital-first lenders. Objective: Implement AI-powered credit decisioning that expands credit access through alternative data while reducing decision time to <60 seconds and maintaining credit loss rates within risk appetite. Scope: • Alternative data integration (cashflow, telco, utilities, rental history) • Explainable AI models for credit scoring with fairness constraints • Real-time income and employment verification APIs • Dynamic pricing and credit limit optimization • Model monitoring for performance drift and bias detection • Regulatory compliance for AI explainability and fair lending

Background: Generic marketing yields 1-2% conversion while personalized experiences drive 10-15% uplift, yet 65% of retailers lack unified customer profiles, resulting in $300B in lost personalization value and inability to compete with digital-native brands achieving 40%+ repeat purchase rates. Objective: Build customer data platform with AI-powered personalization that increases conversion rates by 25-30%, drives repeat purchase frequency by 40%, and achieves 60%+ email/push notification engagement through hyper-relevant content. Scope: • Customer data platform with identity resolution across touchpoints • Real-time personalization engine for web, app, and in-store • AI-powered product recommendations and next-best-offer • Dynamic segmentation and lifecycle marketing automation • Predictive analytics for churn, lifetime value, and propensity • Privacy-compliant first-party data strategy and consent management

Background: Hotels leave 15-20% revenue on the table through suboptimal pricing strategies, while legacy revenue management systems cannot process real-time market signals, competitor data, or micro-segmentation opportunities that drive 25-30% RevPAR premiums. Objective: Deploy AI-powered revenue management that achieves 12-18% RevPAR lift through real-time dynamic pricing, demand forecasting accuracy >95%, and personalized rate optimization across all distribution channels. Scope: • Real-time pricing engines with competitor rate intelligence • Demand forecasting with external signals (events, weather, flight data) • Channel-specific pricing and inventory allocation optimization • Personalized rate recommendations based on guest behavior • Group and corporate rate optimization with displacement analysis • Ancillary revenue optimization (F&B, spa, upgrades, packages)

Background: Diagnostic errors affect 12M Americans annually costing $750B, while clinician burnout reaches 60% due to administrative burden and cognitive overload, yet AI demonstrates 85-95% accuracy in radiology, pathology, and clinical risk prediction when properly implemented. Objective: Deploy ambient AI and clinical decision support that reduces diagnostic errors by 30-40%, decreases clinician documentation time by 60-70%, and improves early detection of sepsis, deterioration, and high-risk conditions with 90%+ sensitivity. Scope: • Ambient clinical documentation with real-time visit transcription • AI-assisted radiology and pathology image interpretation • Sepsis and patient deterioration early warning systems • Clinical risk prediction for readmissions and mortality • Treatment pathway recommendations with evidence-based protocols • Integration with EHR workflow and alert fatigue mitigation

Background: Traditional drug discovery takes 10-15 years and costs $2.6B per approved drug with 90% clinical failure rates, while AI/ML technologies demonstrate potential to reduce discovery timelines by 30-50% and improve lead compound identification accuracy to 80%+. Objective: Deploy AI-driven drug discovery platform that reduces preclinical development time by 24-36 months, increases hit-to-lead conversion by 40%, and enables in-silico prediction of ADMET properties with 85%+ accuracy. Scope: • Generative AI for novel molecule design and optimization • Target identification and validation using multi-omics data • In-silico ADMET prediction and toxicity modeling • AI-assisted clinical trial design and patient stratification • Literature mining and biomarker discovery automation • Integration with laboratory automation and high-throughput screening

Background: Manual quality inspections miss 15-20% of defects while consuming 8-12% of production costs, and foodborne illness outbreaks damage brand value by $50-100M, yet quality systems remain reactive rather than predictive. Objective: Deploy AI-driven quality systems that achieve 99%+ defect detection, reduce quality-related waste by 30-40%, and enable real-time contamination risk prediction across production environments. Scope: • Computer vision for automated visual quality inspection • Pathogen prediction models using environmental sensors • Allergen cross-contamination monitoring and prevention • Predictive equipment maintenance to prevent food safety failures • Real-time environmental monitoring (ATP, air quality, sanitation) • Digital HACCP with automated critical control point verification

Background: 70% of banks operate on core systems over 20 years old, creating 60-80% of IT budgets consumed by maintenance, while open banking mandates and embedded finance opportunities require API-first architectures that legacy cores cannot support. Objective: Modernize core banking infrastructure to cloud-native, API-enabled platforms that support real-time processing, open banking ecosystems, and reduce total cost of ownership by 40-50%. Scope: • Incremental migration strategy with parallel run capabilities • Open banking API gateway and developer portal • Event-driven architecture for real-time account updates • Product catalog modernization and componentized services • Embedded finance and Banking-as-a-Service (BaaS) capabilities • Cloud infrastructure with multi-region resilience

Background: Traditional site-based trials face 80% patient recruitment failures, 30% dropout rates, and $10-15M per study in site management costs, while decentralized trial technologies enable 50% faster enrollment and 25-40% cost reduction with improved diversity. Objective: Implement hybrid and fully decentralized trial capabilities that reduce enrollment timelines by 40-50%, improve retention to 85%+, and expand patient diversity to meet FDA requirements while maintaining data quality and regulatory compliance. Scope: • Remote patient monitoring with wearables and connected devices • Telemedicine infrastructure for virtual visits and assessments • Direct-to-patient drug distribution and home health services • Electronic consent (eConsent) and patient engagement platforms • Real-time data capture and adaptive trial design capabilities • Diverse patient recruitment through digital outreach and community partnerships