Mainframe Modernization & AIOps
Modernizing Delta's z/TPF mainframe estate through a hybrid cloud strategy — leveraging the Kyndryl 20-year partnership, AWS cloud infrastructure, and TCS Ignio AIOps to create a resilient, observable, and progressively autonomous technology foundation.
The stakes
Business scale and impact that makes this transformation critical.
Current-state friction
z/TPF Mainframe Dependency
Delta's core reservation, crew scheduling, and departure control systems still run on IBM z/TPF — a 1970s-era transaction processing platform. Institutional knowledge is concentrated in a shrinking pool of senior engineers, and the rigid architecture limits the pace of innovation and integration with modern cloud services.
Limited AIOps Observability
Current infrastructure monitoring is fragmented across mainframe, on-premises, and cloud environments. Without unified AIOps observability, incident correlation is manual, root cause analysis is slow, and predictive failure detection is virtually impossible across the hybrid estate.
Hybrid Cloud Complexity
With 40% of workloads on AWS and critical systems still on z/TPF, Delta operates a complex hybrid environment. Data movement, latency management, and consistent security policies across environments create operational overhead that will only grow as cloud migration accelerates.
Intelligent choices architecture
Four-step agentic decision loop powering autonomous operations.
- ↳ z/TPF transaction volumes, response times, and resource utilization metrics
- ↳ AWS CloudWatch and infrastructure metrics across all cloud workloads
- ↳ Application dependency maps spanning mainframe and cloud environments
- ↳ Change management feeds tracking deployments across all environments
- ↳ Anomaly detection correlating signals across mainframe, cloud, and network layers
- ↳ Predictive failure analysis using historical incident patterns and capacity trends
- ↳ Workload migration candidate identification based on coupling analysis and risk scoring
- ↳ Incident priority and routing decisions using business impact assessment
- ↳ Automated incident remediation for known patterns (restart services, clear queues, scale resources)
- ↳ Proactive capacity scaling in AWS based on predicted demand surges
- ↳ Automated runbook execution for standard operational procedures
- ↳ Incident communication and escalation to Kyndryl and internal teams
- ↳ Incident post-mortem analysis identifying systemic infrastructure weaknesses
- ↳ Mainframe workload profiling for progressive migration planning
- ↳ AIOps model retraining on new incident patterns and resolution outcomes
- ↳ Capacity planning optimization using trend analysis and seasonal modeling
Human + AI autonomy levels
TCS agentic AI agents
Click an agent to see detailed capabilities, autonomy levels, and TCS proof points.
KPI architecture
TCS proof points
Enterprise AIOps platform providing unified observability, automated remediation, and predictive analytics across hybrid mainframe-cloud environments for global enterprises.
TCS Incept.AI Innovation Camp: 4-6 week discovery workshop ($500K-$1M) to assess current state, identify automation opportunities, and deliver a prioritized transformation roadmap with measurable business outcomes.
From discovery to full-scale deployment: Spark.AI for prototyping (8-12 weeks), Realize.AI for production scaling (6-12 months), and ongoing managed services with SLA-based outcomes.
- → Model orchestration for AIOps anomaly detection and prediction models
- → Governance controls for infrastructure change management compliance
- → Observability tracking system availability, incident metrics, and migration progress