Introduction
Insurance providers operate in highly customer-centric environments where service speed, accessibility, and reliability directly impact customer trust. High volumes of support tickets, SLA violations, and manual intervention often lead to delays and poor customer experience. This case study highlights how an insurance provider transformed its support operations through self-service enablement, automation, and workload optimization. By restructuring IT support processes and introducing intelligent automation, the organization improved service efficiency, reduced operational effort, and enhanced customer trust.
Customer
An insurance provider managing high-volume application support operations across multiple channels, including web, voice, email, and automated alerts.
Business Objective
- Improve customer trust through faster and seamless support
- Reduce SLA violations in response and resolution
- Optimize support workload across L1, L2, and L3 teams
- Enable self-service and automation-led support
- Reduce dependency on manual intervention
Scope of Services
- Ticket data analysis across time, volume, and channels
- Incident vs service request classification and optimization
- SLA compliance analysis (response and resolution)
- Skill-based workload and demand analysis
- Identification of automation and self-service opportunities
- Implementation of BOT, RPA, and auto-healing use cases
- Enablement of self-help and self-service platforms
Key Insights from Analysis
- 3,100 total tickets analyzed
- ~96% tickets converted to incidents (2,988) → poor classification
- SLA violations:
- 527 response breaches
- 589 resolution breaches
- Majority tickets originated from web (2,289)
- High dependency on manual support across channels
Workload & Skill Observations
- Operations contributed 45% of total ticket volume
- Finance & Supply Chain accounted for 44%
- Top skills in demand:
- Oracle EBS (44.9%)
- .Net/C# (20.7%)
- Oracle 4GL (19.7%)
- Strong opportunity for L3 → L2 → L1 shift-left model
Detailed Findings
- Poor ticket classification between incidents and service requests
- High volume of P3 tickets (78%) indicating inefficiency in prioritization
- SLA response violations higher than resolution → process gaps
- Lack of structured service catalogue and self-service adoption
- Repetitive issues (data updates, training, access issues) suitable for automation
Benefits
- Reduced manual ticket handling through self-service
- Improved SLA compliance and response efficiency
- Better workload distribution across support levels
- Enhanced visibility into support operations and performance
- Improved customer experience and trust
Impact
- 48.11% of tickets identified for automation/self-service impact
- 37% overall effort optimization achieved
- Significant reduction in repetitive support workload
- Improved SLA adherence and faster response times
- Enhanced customer satisfaction through seamless support experience
Introduction
Telecom operations transformation is essential for operators dealing with rising costs, fragmented processes, and increasing customer expectations. A leading Asia-Pacific telecom operator transformed its operations to improve efficiency, reduce costs, and enable scalable service delivery.
Customer
A leading Asia-Pacific telecom operator offering mobile, broadband, and digital media services, operating across multiple markets with complex service delivery models and high operational costs.
Business Objective
- Reduce operational costs
- Improve workforce productivity
- Standardize processes across operations
- Enhance service delivery efficiency
Scope of Services
- End-to-end operations assessment
- Workforce and process optimization
- Implementation of standardized operating model
- Performance tracking and KPI alignment
- Automation-led process improvements
Key Challenges Addressed
- High operational costs
- Inefficient workforce utilization
- Fragmented processes across business units
- Lack of standardized KPIs
Benefits of Telecom Operations Transformation
- Streamlined operations across departments
- Improved workforce efficiency
- Better cost control and visibility
- Enhanced service delivery performance
Impact
- 70% productivity improvement
- 15% cost reduction achieved
- Faster decision-making and execution
- Scalable operating model for growth
Introduction
Product portfolio transformation is critical for global software companies managing multiple acquired products and fragmented offerings. Disconnected product lines often lead to inconsistent customer experiences, slower innovation cycles, and missed revenue opportunities. This case study highlights how a global software product company restructured its portfolio, modernized products with cloud-native and AI capabilities, and established a scalable innovation model. By aligning product strategy with customer needs and market opportunities, the organization improved monetization, accelerated releases, and strengthened its competitive positioning.
Customer
A global software product company with multiple acquired products, operating across diverse geographies and customer segments.
Business Objective
- Consolidate and rationalize fragmented product portfolio
- Monetize existing IP and software assets
- Accelerate product innovation and time-to-market
- Improve customer retention and engagement
- Enable scalable product engineering and delivery
Scope of Services
- IP acquisition and portfolio restructuring
- Product modernization using cloud-native architectures
- Infusion of AI/ML capabilities into product offerings
- Product roadmap definition and execution
- Customer success and lifecycle enablement
- Channel and partner ecosystem enablement
- API and integration framework standardization
Benefits
- Increased monetization of existing product assets
- Expanded solution portfolio and revenue streams
- Improved customer retention and renewal rates
- Faster innovation cycles and release velocity
- Scalable product engineering and delivery model
Impact
- Clear and scalable product roadmap established
- Improved customer engagement and retention
- Faster time-to-market for new product releases
- Stronger cross-sell and up-sell capabilities
- Sustainable innovation and engineering foundation
Introduction
Digital experience transformation is redefining how public transportation authorities engage with citizens and travelers. Modern mobility ecosystems require seamless, intuitive, and accessible experiences across airports, transit hubs, and digital platforms. Traditional infrastructure-focused approaches often fail to meet evolving expectations of convenience, personalization, and inclusivity. This case study highlights how a regional transportation authority transformed its ecosystem by integrating human-centered design, immersive technologies, and digital innovation. By leveraging AR/VR, mobile platforms, and multi-cloud infrastructure, the organization created future-ready transportation experiences while optimizing costs and accelerating innovation.
Customer
A leading Australian regional transportation authority responsible for managing metropolitan transport infrastructure, including airports, train stations, and multimodal transit systems.
Business Objective
- Design future-ready transportation assets aligned with long-term mobility vision
- Deliver accessible and world-class traveler experiences
- Position transport infrastructure as experience-driven destinations
- Reduce total cost of ownership through outsourcing and cloud adoption
- Accelerate innovation through incubation and digital experimentation
Scope of Services
- Human-centered design for airport and transit experiences
- Development of citizen-facing mobile applications
- AR/VR-based experience prototyping and visualization
- Strategic outsourcing of applications and infrastructure
- Multi-cloud enablement and migration
- Innovation incubation through rapid prototyping and validation
Benefits
- Improved citizen and traveler engagement
- Enhanced accessibility and inclusivity across services
- Reduced long-term IT and operational costs
- Faster innovation cycles through incubation approach
- Scalable and flexible digital infrastructure
Impact
- Delivery of immersive AR/VR-based experience designs
- Enablement of next-generation traveler experiences
- Foundation for scalable and future-ready transport systems
- Strengthened positioning of transport infrastructure as destinations
Introduction
Digital logistics platform transformation enables enterprises to modernize legacy systems, reduce operational costs, and improve visibility across complex supply chain operations. Large logistics organizations often struggle with fragmented application landscapes, high run-the-business (RTB) costs, and limited end-to-end shipment visibility. This case study highlights how a global logistics company transformed its operations by building a next-generation digital logistics platform. By rationalizing legacy systems, standardizing processes, and integrating application and infrastructure support, the organization achieved significant cost savings, improved efficiency, and enhanced revenue realization.
Customer
A global supply chain services and logistics company headquartered in the United States, managing enterprise-scale freight forwarding operations and a large application ecosystem.
Business Objective
- Reduce RTB costs across IT and operations
- Improve end-to-end shipment visibility
- Standardize and re-engineer business processes
- Reduce incident volumes and support dependency
- Establish integrated SLAs and KPIs across operations
Scope of Services
- Transformation of core freight forwarding systems
- Design and development of a next-generation digital platform
- Rationalization of 170+ legacy applications
- Creation of a unified enterprise data layer (single source of truth)
- Application support services across 115 applications and 25 technologies
- Infrastructure support and enterprise help desk operations
- Stabilization and automation of support processes
- SLA and KPI definition and implementation
Benefits
- Significant reduction in RTB costs
- Faster customer onboarding through standardized workflows
- Improved shipment visibility across logistics operations
- Reduced complexity through platform consolidation
- Enhanced IT service reliability and predictability
Impact
- $100M reduction in RTB costs
- 60% reduction in customer onboarding time
- 11% increase in revenue realization
- 20%+ reduction in ticket volumes
- Improved operational efficiency across applications and infrastructure
Introduction
AI-driven customs clearance optimization enables logistics companies to reduce delays, improve compliance accuracy, and enhance international shipment efficiency. Customs processing is often a major bottleneck due to manual documentation, regulatory complexity, and risk assessment challenges. This case study highlights how a global courier and express logistics company transformed customs clearance into a competitive advantage by leveraging AI, image recognition, and analytics. By automating classification, predicting risks, and enabling real-time workflows, the organization significantly improved clearance speed and accuracy.
Customer
A US-based global courier and express logistics company handling large volumes of international shipments and customs operations.
Business Objective
- Transform customs clearance into a competitive differentiator
- Predict shipment caging risks proactively
- Reduce manual documentation errors
- Improve accuracy and speed of clearance processes
- Enhance international shipment efficiency
Scope of Services
- HS code prediction using AI/ML models
- Risk scoring models for shipment evaluation
- Document and image processing automation
- Real-time workflow enablement for customs operations
- Integration with logistics and compliance systems
Benefits
- Reduced customs delays and processing time
- Improved accuracy in classification and documentation
- Lower risk of compliance errors and penalties
- Faster clearance workflows
- Enhanced operational efficiency in international logistics
Impact
- 94–97% success rate in caging identification
- Reduced revenue leakage from classification errors
- Faster international shipment processing
- Improved compliance and clearance accuracy
Introduction
Digital commerce platforms enable logistics providers to unify customer interactions, improve shipment visibility, and deliver seamless end-to-end experiences. Fragmented systems across channels often lead to inconsistent customer journeys, limited visibility, and increased dependency on support teams. This case study highlights how an APAC-based logistics provider transformed its operations by building a unified digital commerce platform. By integrating cloud infrastructure, DevOps pipelines, and real-time tracking capabilities, the organization enhanced customer experience, improved operational visibility, and reduced service overhead.
Customer
An APAC-based integrated logistics service provider operating across multiple business units and customer channels.
Business Objective
- Eliminate fragmented customer experience across channels
- Create a unified digital commerce platform
- Improve end-to-end shipment visibility
- Reduce dependency on support channels
- Enable scalable digital operations
Scope of Services
- Development of a unified digital business platform
- Cloud infrastructure setup and automation on AWS
- Implementation of track-and-trace MVP
- Integration across business units and systems
- Deployment of recovery and resilience mechanisms
Benefits
- Unified and consistent customer experience
- Improved visibility across shipment lifecycle
- Reduced dependency on call-center support
- Enhanced operational efficiency
- Scalable digital platform for growth
Impact
- 20% reduction in customer churn
- 28% reduction in call-center volumes
- Single unified view of customer transactions
- Improved customer engagement and retention
Introduction
Connected fleet platforms enable logistics providers to leverage real-time telematics data for operational efficiency, predictive maintenance, and new revenue streams. Traditional fleet operations often lack integrated visibility across vehicles, leading to reactive maintenance, inaccurate ETAs, and limited ability to monetize data. This case study highlights how a leading trucking and logistics provider transformed its fleet operations by implementing a connected telematics platform. By integrating IoT data, predictive analytics, and route optimization, the organization improved operational predictability, enhanced customer experience, and unlocked new monetization opportunities.
Customer
A leading US-based trucking and logistics provider operating a fleet of approximately 0.5 million vehicles across large-scale transportation networks.
Business Objective
- Monetize telematics and fleet data
- Enable predictive maintenance across vehicles
- Improve ETA accuracy and customer experience
- Enhance operational visibility and efficiency
- Prepare for electric and autonomous vehicle integration
Scope of Services
- Design of connected fleet platform architecture
- Real-time ingestion of telematics and vehicle data
- Predictive maintenance analytics implementation
- Route optimization and ETA prediction logic
- Development of data monetization frameworks
- Integration with OEMs and service providers
Benefits
- Reduced maintenance costs through predictive insights
- Improved compliance through digital inspection (eDVIR)
- Better operational predictability across fleet operations
- Enhanced visibility into vehicle performance
- Scalable platform for future mobility innovations
Impact
- Improved customer satisfaction through accurate ETAs
- Enabled value-added services through data monetization
- Proactive maintenance reducing downtime risks
- Enhanced efficiency in large-scale fleet operations
Introduction
AI-driven customer service optimization enables logistics organizations to reduce support costs, improve customer experience, and uncover hidden operational inefficiencies. Logistics providers handling large volumes of shipments often rely heavily on call-based customer support, leading to rising costs and inconsistent service quality. Limited visibility into the root causes of customer queries further restricts optimization efforts. This case study highlights how a logistics major leveraged analytics and AI to transform customer service operations, identify inefficiencies, and establish a scalable foundation for AI adoption across shipping workflows.
Customer
A logistics organization operating large-scale shipping and customer service operations with high dependency on call-based support and service desk interactions.
Business Objective
- Reduce customer service support costs
- Improve customer satisfaction and experience
- Identify hidden inefficiencies in operations
- Enable data-driven decision-making
- Scale AI adoption across logistics processes
Scope of Services
- Analysis of customer service call data and shipping operations
- Correlation of customer interactions with operational events
- Identification of inefficiencies and bottlenecks
- Root cause analysis of customer dissatisfaction drivers
- Identification and prioritization of AI use cases
- Continuous analytics and insight delivery
- Experimentation and validation of AI-driven solutions
Benefits
- Reduced dependency on live customer service agents
- Improved understanding of cost and inefficiency drivers
- Faster identification of operational bottlenecks
- Data-driven prioritization of automation initiatives
- Continuous improvement through analytics insights
Impact
- 13% reduction in customer calls through IVR and conversational AI
- 30+ analytical reports delivered to stakeholders
- 5+ AI use cases and POCs successfully implemented
- Improved visibility across customer service and shipping operations
- Established foundation for scalable AI adoption
Introduction
Scalable platform deployment enables capital-heavy manufacturing organizations to modernize operations without committing to large upfront investments. Traditional transformation programs often require significant capital expenditure, creating hesitation and slowing adoption. This case study highlights how a manufacturing enterprise adopted modular, service-based platforms to reduce financial risk and accelerate return on investment. By shifting from a CAPEX-heavy approach to a scalable OPEX-driven model, the organization enabled faster deployment, improved flexibility, and aligned technology investments with business growth.
Customer
A capital-heavy manufacturing organization cautious about large upfront investments and seeking flexible technology adoption models.
Business Objective
- Minimize upfront capital expenditure
- Achieve faster return on investment
- Reduce financial risk in transformation initiatives
- Enable scalable and phased technology adoption
- Improve confidence in technology investments
Scope of Services
- Deployment of modular, scalable platforms
- Implementation of service-based delivery models
- Phased rollout aligned with business priorities
- Enablement of flexible scaling across operations
- Optimization of cost and investment structures
Benefits
- Reduced financial risk through phased investments
- Flexible scaling aligned with business demand
- Lower barrier to technology adoption
- Improved alignment between cost and value realization
- Increased agility in decision-making
Impact
- Faster ROI cycles across initiatives
- Improved stakeholder confidence in technology investments
- More efficient allocation of capital resources
