Introduction
Banking institutions operate in high-availability environments where system downtime and delayed incident resolution directly impact customer experience and business continuity. High incident volumes during peak business hours, duplicate tickets, and manual intervention reduce operational efficiency and increase risk. This case study highlights how a banking institution improved resilience through automated healing, intelligent ticket analysis, and service recovery mechanisms. By enabling event correlation, automation, and proactive monitoring, the organization significantly enhanced system stability and operational efficiency.
Customer
A large-scale banking institution managing high-volume IT incidents across application and infrastructure environments with 24×7 support requirements.
Business Objective
- Improve IT resilience through automated healing and recovery
- Reduce high incident volumes during peak business hours
- Minimize SLA violations and improve response times
- Eliminate duplicate and redundant tickets
- Shift from reactive to proactive IT operations
Scope of Services
- Heat map–based incident analysis across time and business hours
- Identification of peak-hour incident patterns and workload spikes
- Ticket classification and automation probability analysis
- Detection of duplicate and parent-child ticket patterns
- Design and implementation of automated healing workflows
- Enablement of event correlation and alert suppression
- Establishment of 24×7 Integrated Command Centre
Key Insights from Analysis
- 17,600+ incidents analyzed
- 75% incidents occur during business hours (9 AM–6 PM)
- High-volume incident drivers:
- Password issues (22%)
- Account issues (19%)
- Connectivity issues (17%)
- Configuration issues (16%)
- Significant duplication and parent-child ticket patterns observed
Detailed Findings
- High dependency on manual ticket logging and resolution
- Lack of event correlation leading to duplicate tickets (~400–500 cases)
- Inefficient prioritization affecting response times
- Repetitive issues (password, access, configuration) ideal for automation
- High operational load during peak hours impacting service quality
Benefits
- Reduced duplicate and redundant ticket volumes
- Faster incident detection and response
- Improved SLA adherence and service reliability
- Better prioritization of critical incidents (P1/P2)
- Enhanced operational efficiency and workload management
Impact
- 30.7% automated resolution achieved
- Up to 75% automation potential for password-related issues
- Significant reduction in manual intervention
- Improved service recovery and incident handling speed
- Strong foundation for resilient, scalable IT operations
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
Automotive enterprises operate complex IT environments across datacenters, SAP, MES, and enterprise applications. High volumes of manually logged incidents, false alerts, and delayed resolution impact operational efficiency and system reliability. This case study highlights how an automotive leader transformed its IT operations using predictive self-healing and automation. By enabling event correlation, alert suppression, and automated resolution, the organization significantly reduced manual intervention, improved system stability, and enhanced operational efficiency.
Customer
A leading automotive enterprise managing large-scale datacenter operations, enterprise applications, and manufacturing systems across global operations.
Business Objective
- Reduce manual ticket logging and operational overhead
- Minimize false alerts and improve monitoring accuracy
- Reduce P1/P2 incidents impacting critical systems
- Enable predictive and automated incident resolution
- Improve IT operations efficiency and reliability
Scope of Services
- Datacenter and IT incident pattern analysis
- Event correlation and alert suppression design
- Automation of service requests and incident resolution
- Predictive monitoring across SAP, MES, and infrastructure
- Self-healing workflow enablement across IT environments
Key Insights from Analysis
- 51%+ issues logged manually → major inefficiency
- False positives increased up to 19%
- P1/P2 incidents driven by:
- SAP security issues
- MES engine failures
- SAP HCM downtime
- Backup failures
- Automation potential identified across service requests and incidents
Detailed Findings
- High dependency on manual incident logging and handling
- Lack of effective alert correlation leading to noise
- Inefficient prioritization causing delays in critical incidents
- High recurrence of issues across SAP, MES, and infrastructure
- Significant automation gaps across EUC, DC, and network
Benefits
- Reduced manual intervention through automation
- Improved monitoring accuracy with alert suppression
- Faster incident detection and resolution
- Improved system stability and uptime
- Enhanced efficiency across IT operations
Impact
- 44% of processes identified as automatable
- 40–50% automation potential across service requests and incidents
- Significant reduction in false alerts and operational noise
- Reduced P1/P2 incidents across critical systems
- Improved operational efficiency and service reliability
Introduction
Manufacturing operations rely heavily on efficient IT support across infrastructure, applications, and core services. Rising ticket volumes, poor classification, and lack of structured service management create inefficiencies, slow resolution, and increased operational costs. This case study highlights how a cement producer transformed its IT operations by combining self-service enablement with automation and process standardization. By improving service catalogue design, governance, and automation readiness, the organization enhanced efficiency, reduced operational load, and improved service delivery.
Customer
A cement manufacturing enterprise managing large-scale IT infrastructure, applications, and support services across plant operations.
Business Objective
- Reduce rising IT ticket volumes and operational load
- Improve service efficiency through self-service and automation
- Standardize ITSM processes and governance
- Enhance response and resolution times
- Enable scalable and cost-efficient IT operations
Scope of Services
- Ticket baseline and trend analysis across incidents and service requests
- ITSM process alignment (incident vs service request classification)
- Service catalogue design and digitization
- Business priority and IT severity standardization
- Automation opportunity identification across IT domains
- Integration of incident classification, governance, and workflows
Key Insights from Analysis
- 36,107 total tickets analyzed
- 31,255 incidents vs 4,852 service requests (heavy incident skew)
- 2025 ticket volume already reached 75% of 2024 within 5 months
- Incidents surged to 80% of previous year volume
- IT core support demand increased by 10% YoY
Detailed Findings
- Process Issues (47%) → Lack of structured classification and ITSM governance
- Security Issues (18%) → Need for compliance, SOX alignment, and governance
- Hardware Issues (10%) → Gaps in lifecycle and service catalogue alignment
- Software Issues (8%) → Need for digitalization and automation
- Network Issues (7%) → Performance and monitoring gaps
Benefits
- Improved ticket handling through structured ITSM processes
- Reduced manual intervention via self-service enablement
- Better SLA adherence through prioritization and governance
- Improved visibility into IT operations and performance
- Enhanced scalability of IT support operations
Impact
- 20%–24% automation potential identified
- 40% automation opportunity in security-related issues
- Clear segregation of incidents vs service requests
- Reduced dependency on manual support processes
- Improved efficiency across IT infrastructure and applications
Introduction
Manufacturing plants depend on stable IT systems across EUC, SAP, network, and application environments to ensure uninterrupted production. High ticket volumes, manual intervention, and delayed resolution directly impact plant uptime and operational efficiency. This case study highlights how a cement manufacturer transformed its IT operations using AI-driven self-healing and automation. By analyzing ticket patterns, standardizing processes, and enabling automation at scale, the organization significantly improved efficiency, reduced incidents, and enhanced plant uptime.
Customer
A cement manufacturing enterprise managing large-scale plant operations with high IT dependency across EUC, SAP, network, and application environments.
Business Objective
- Reduce IT ticket volumes and operational load
- Improve plant uptime and operational efficiency
- Minimize SLA breaches and turnaround time
- Enable automation-led IT operations
- Improve service quality across IT environments
Scope of Services
- Baseline ticket analysis across EUC, SAP, network, and applications
- Ticket classification and severity alignment
- Service catalogue rationalization and digitization
- Automation opportunity identification and implementation
- AI-driven event correlation and self-healing enablement
- ITSM process standardization and optimization
Key Insights from Analysis
- 11,586 total tickets analyzed (Jan–Aug 2025)
- Ticket volume increased by 33% in recent months
- Majority tickets categorized as Moderate severity (10,771)
- EUC accounted for 6,412 tickets (largest contributor)
- Significant inefficiencies in ticket classification and prioritization
Detailed Findings
- Process Issues (27%) → Misclassification and lack of structured ITSM taxonomy
- EUC Issues (51%) → High dependency on manual support and outdated service catalogue
- SAP Issues (47%) → Need for lifecycle alignment and better business integration
- Hardware Issues (17%) → Gaps in service catalogue and storage/EUC alignment
Benefits
- Improved ticket handling efficiency through automation
- Reduced manual intervention in recurring incidents
- Faster incident prioritization and resolution
- Better SLA adherence across IT services
- Improved visibility and control over IT operations
Impact
- 48.33% overall automation potential identified
- 47% efficiency potential in process-related issues
- 29% efficiency improvement opportunity in EUC
- 19% efficiency opportunity in SAP
- Reduction in manual ticket handling and operational load
- Improved plant uptime and IT service reliability
Introduction
AI-based clinical decision support enables healthcare organizations to improve diagnosis accuracy, identify risks early, and enhance patient outcomes. Healthcare providers managing patients with complex medical conditions often struggle with fragmented data, delayed insights, and challenges in early risk detection. These limitations can lead to missed diagnoses and inconsistent treatment outcomes. By leveraging AI-based clinical decision support powered by deep neural networks, healthcare organizations can augment clinical expertise, improve decision-making, and deliver more accurate and timely care.
Customer
Healthcare organizations managing patients with complex medical conditions.
Business Objective
- Improve prognosis and treatment planning
- Enable early identification of high-risk patients
- Enhance diagnostic accuracy
- Support clinicians with data-driven insights
- Reduce missed or delayed diagnoses
Scope of Services
- AI-assisted medical diagnosis enablement
- Risk identification and patient stratification
- Clinical recommendation support systems
- Classification and categorization of patient data
- Integration of AI models into clinical workflows
Benefits
- Improved accuracy in diagnosis and treatment decisions
- Augmented clinician expertise with AI-driven insights
- Early detection of high-risk patients
- Better clinical decision support
- Enhanced quality of patient care
Impact
- Improved patient outcomes
- Reduced missed or inaccurate diagnoses
- More proactive and preventive care delivery
- Increased confidence in clinical decision-making
Introduction
HR lifecycle automation platforms enable organizations to streamline employee onboarding, movement, and offboarding while improving efficiency and compliance. An organization in the Professional Services industry faced challenges with manual processes, access management errors, and delays during employee lifecycle transitions. These inefficiencies impacted employee experience and increased operational overhead. By implementing HR lifecycle automation, the organization created a seamless, audit-ready, and scalable system that improved process efficiency and reduced manual intervention.
Customer
An organization aiming to modernize and automate employee onboarding, movement, and offboarding processes to improve efficiency, compliance, and employee experience.
Business Objective
- Reduce time and effort in employee lifecycle processes
- Eliminate manual errors in access and asset management
- Improve employee experience during transitions
- Ensure auditability and compliance
- Lower operational costs through automation
Scope of Services
- Automation of onboarding, transfer, and offboarding workflows
- Integration between HRMS and ITSM platforms (e.g., ServiceNow)
- Access control and asset lifecycle management
- Approval workflow automation
- Audit-ready process execution and tracking
Benefits of HR Lifecycle Automation Platform
- Reduced manual effort across HR and IT processes
- Improved accuracy in access and asset management
- Faster employee onboarding and transition processes
- Enhanced compliance and audit readiness
- Consistent and standardized lifecycle workflows
Impact
- End-to-end self-service automation of HR and IT tasks
- Single-click approvals via email or SMS
- Improved cost efficiency through reduced manual effort
- Strong audit trail and compliance readiness
- Significantly improved employee experience
Introduction
AI performance evaluation automation helps organizations streamline employee review processes, improve feedback quality, and enhance manager productivity. An enterprise organization in the Professional Services industry faced challenges with manual and repetitive evaluation cycles, leading to inconsistent feedback and reduced engagement. As evaluation frequency increased, managers struggled with time constraints and declining quality of reviews. By implementing an AI performance evaluation automation solution, the organization simplified workflows, enabled conversational interactions, and improved the overall employee experience.
Customer
An organization seeking to modernize its employee performance evaluation process to improve manager productivity, feedback quality, and overall employee experience.
Business Objective
- Reduce manual effort in performance evaluations
- Improve quality and consistency of feedback
- Increase manager productivity during evaluation cycles
- Enhance employee experience and engagement
- Enable scalable and efficient evaluation workflows
Scope of Services
- Design and development of a conversational HR assistant
- NLP-based chatbot for performance evaluation workflows
- API integration with performance management systems
- Automation of feedback capture and evaluation processes
- Intelligent interaction storage for continuous improvement
Benefits of AI Performance Evaluation Automation
- Reduced manual effort and repetitive tasks for managers
- Improved consistency and quality of performance feedback
- Faster and more efficient evaluation cycles
- Enhanced employee engagement and satisfaction
- Scalable and standardized evaluation process
Impact
- Significant time savings for managers
- Improved efficiency in performance reviews
- Anytime, anywhere access to evaluation workflows
- Better employee experience through simplified interactions
- Higher quality feedback across teams
Introduction
An IoT production monitoring platform is critical for manufacturers to improve efficiency and reduce energy consumption in large-scale operations. A fertilizer manufacturer in the Manufacturing & Resources industry faced challenges in optimizing energy-intensive ammonia and urea production processes. Limited real-time visibility into plant operations led to inefficiencies and higher costs. By implementing an IoT production monitoring platform with real-time analytics, the organization improved operational control, reduced energy consumption, and strengthened its sustainability performance.
Customer
A fertilizer manufacturer in the Manufacturing & Resources industry operating large-scale ammonia and urea production plants.
Business Objective
- Reduce energy consumption across production processes
- Improve efficiency of energy-intensive operations
- Lower carbon footprint and support sustainability goals
- Enable real-time visibility into plant operations
- Stabilize operating costs at scale
Scope of Services
- Engineering optimization for energy-efficient manufacturing
- IoT-enabled monitoring across production, blending, and packaging
- Real-time data capture from plant equipment and sensors
- Operational analytics to identify inefficiencies and energy loss
- Continuous optimization of plant performance
Benefits of IoT Production Monitoring Platform
- Improved energy efficiency across production plants
- Reduced operational waste and inefficiencies
- Better control over blending and packaging processes
- Lower environmental impact and improved sustainability posture
- More predictable and optimized production performance
Impact
- Reduced operating costs through energy optimization
- Lower carbon footprint across manufacturing operations
- Improved production stability and efficiency
- Stronger alignment with ESG and sustainability goals
Introduction
A digital R&D platform is essential for agricultural manufacturers to accelerate innovation and develop sustainable products. An organization in the Manufacturing & Resources industry, specializing in agricultural inputs, faced challenges in reducing time-to-market and improving formulation accuracy. Traditional R&D processes limited scalability and slowed innovation cycles. By implementing a digital R&D platform with advanced analytics and digital twin capabilities, the organization enhanced product development efficiency and strengthened its position in climate-smart agriculture.
Customer
An agricultural inputs manufacturer in the Manufacturing & Resources industry focused on crop protection and nutritional products.
Business Objective
- Accelerate development of sustainable agricultural inputs
- Improve nutrient efficiency using simulation-based design
- Reduce time-to-innovation for new products
- Enable data-driven R&D decision-making
- Strengthen competitiveness in global markets
Scope of Services
- Enablement of digital twin simulations for product modeling
- Design of data-driven R&D platforms
- Integration of experimental and formulation data
- Advanced analytics for performance insights
- Acceleration of sustainable product design cycles
Benefits
- Faster innovation cycles for crop inputs
- Improved accuracy in formulation and nutrient efficiency
- Increased readiness for sustainable products
- Better collaboration across R&D teams
- Stronger competitive positioning
Impact
- Reduced product development and validation time
- Improved innovation throughput
- Better alignment with climate-smart initiatives
- Enhanced R&D competitiveness
