ENT-02 | AI for enterprise technology

ERP Transformation

Evaluating and deploying a next-generation ERP platform — potentially SAP S/4HANA — tailored to Delta's unique airline-specific requirements, leveraging TCS Center of Innovation expertise to modernize finance, procurement, and supply chain operations.

SAP evaluationAirline-specific ERPTCS Center of InnovationProcess modernization

-35%

Process cycle time

-20%

ERP operating cost

+25%

Data visibility

The stakes

Business scale and impact that makes this transformation critical.

$63.4B

Revenue through ERP

All financial transactions flow through ERP

Countries of operation

Multi-currency, multi-regulatory complexity

15+

Legacy ERP modules

Fragmented system landscape

$200M+

Estimated transformation cost

Current-state friction

Fragmented

Fragmented ERP Landscape

Delta operates 15+ legacy ERP modules across finance, procurement, HR, and supply chain — many customized heavily for airline-specific processes like fuel hedging, aircraft depreciation, and route-based cost allocation. This fragmentation creates data silos, reconciliation overhead, and slow decision cycles.

15+ legacy ERP modules

Airline-Specific

Airline-Specific Requirements Gap

Standard ERP platforms don't natively support airline accounting standards (ASC 606 for ticket revenue), complex fuel hedging through Monroe Energy, or maintenance reserve accounting. Any ERP transformation must bridge the gap between industry-standard platforms and Delta's unique operational model.

30+ airline-specific processes

Change Risk

Transformation Execution Risk

ERP transformations at airline scale are notoriously complex and high-risk. A failed or disrupted cutover could impact financial reporting, supplier payments, and regulatory compliance across 52 countries. Delta needs a partner with deep airline ERP expertise to de-risk the transition.

$200M+ at risk

Intelligent choices architecture

Four-step agentic decision loop powering autonomous operations.

STEP 01

Sense

What the agents observe

↳ Current ERP transaction patterns, volumes, and performance bottlenecks
↳ Process mining data revealing actual vs designed workflows across modules
↳ Data quality metrics across legacy systems identifying reconciliation gaps
↳ User experience friction points from support tickets and process exceptions

Process mining platform · Data quality scanner · ERP performance monitor · User experience analytics

STEP 02

Decide

How the agents reason

↳ Fit-gap analysis comparing airline requirements against SAP S/4HANA capabilities
↳ Migration sequencing optimization balancing risk, value, and business continuity
↳ Custom development vs configuration decisions for airline-specific processes
↳ Testing strategy generation covering regression, integration, and performance scenarios

TCS COI fit-gap framework · Migration planner · SAP configuration analyzer · Test strategy generator

STEP 03

Act

What the agents do

↳ Automated data migration with validation and reconciliation checks
↳ AI-assisted configuration of SAP modules for airline-specific processes
↳ Automated test execution and defect classification during migration waves
↳ Change management communications and training content generation

Data migration toolkit · SAP configuration assistant · Automated testing platform · Change management portal

STEP 04

Learn

How the agents improve

↳ Migration wave analysis identifying patterns in data quality issues
↳ User adoption tracking and training effectiveness optimization
↳ Post-go-live process performance comparison against legacy baselines
↳ Continuous optimization of airline-specific configurations based on user feedback

Migration analytics · Adoption dashboard · Process performance tracker · Configuration optimizer

During the finance module migration wave, the ERP agent discovers that 2,340 fuel hedging transactions in the legacy system use a custom accounting treatment that doesn't map to standard SAP. It auto-generates a custom ABAP extension, validates it against Monroe Energy reconciliation data, runs 150 regression tests, and presents the solution to the finance team — compressing a 3-week manual analysis into 4 hours.

Human + AI autonomy levels

L1 — Tool

CURRENT

L2 — Assistant

TARGET

L3 — Supervised agent

L4 — Autonomous agent

L5 — Agentic workforce

Human role

Human as analyst

Human as decision-maker

Human as supervisor

Human as exception handler

Human as strategist

AI role

AI as process mining dashboard

AI recommends migration approach

AI manages routine migration tasks

AI manages transformation pipeline

Continuous ERP optimization

Description

Process mining and ERP analytics dashboards revealing inefficiencies and migration readiness across modules.

AI generates fit-gap analysis, migration sequencing, and configuration recommendations; project team reviews and approves each decision.

Agent autonomously handles data migration, test execution, and routine configuration; escalates complex airline-specific customizations and financial impacts.

Full migration pipeline management including automated testing, defect resolution, and performance optimization with human focus on strategic decisions and risk management.

Post-migration continuous optimization across all ERP modules, coordinating with finance, operations, and HR agents for enterprise-wide process excellence.

Team type

Traditional squads

Human-led with AI copilot

AI-led with human oversight

Autonomous with guardrails

Agent ecosystem

Guardrails

Read-only analytics; all migration decisions made by project team

All migration decisions require team approval; financial systems changes require CFO sign-off

Financial module changes require approval; data migration validated before commit; rollback capability required

Production cutover always human-approved; financial reporting integrity checks immutable; regulatory compliance validation

Cross-module change coordination; financial audit compliance; regulatory reporting accuracy; strategic direction by CIO

TCS agentic AI agents

Click an agent to see detailed capabilities, autonomy levels, and TCS proof points.

KPI architecture

Level	KPI	Baseline	Target	Business link
L0 Board	ERP total cost of ownership	$180M/yr	$145M/yr	Enterprise technology cost efficiency
L1 Exec	Financial close cycle time	12 days	5 days	Decision speed and reporting accuracy
L2 Ops	Process exception rate	8.5%	3.2%	Operational efficiency and user satisfaction
L3 AI Ops	Automated test coverage	35%	85%	Migration quality and speed
L4 AI Decision	Migration defect rate	N/A	<2%	Transformation risk mitigation

TCS proof points

TCS IP

TCS Center of Innovation (COI) for Airlines

TCS's dedicated airline industry practice with deep SAP expertise, having delivered ERP transformations for 6 of the top 10 global airlines with airline-specific accelerators and proven migration methodologies.

Top-10 airline ERP transformations

30%

Average migration time reduction

$2.8B

ERP program value delivered

Quick-win opportunity

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.

Expansion path

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.

Enterprise Control Plane

How this connects

→ Model orchestration for process mining and migration optimization AI
→ Governance controls for financial system compliance and audit trail integrity
→ Observability tracking migration progress, defect rates, and system performance

← Back to home ← AI for enterprise technology

Related use cases

→ GBS-01: Finance Automation → GBS-02: HR & Workforce → ENT-03: Mainframe & AIOps