GBS-03 | AI for global business services

ESG & Sustainability Intelligence

Powering Delta's industry-leading sustainability commitments with AI — tracking the 53.6% GHG reduction achievement, scaling SAF from 13M to 400M gallons, and automating CSRD/CORSIA compliance across a global operation spanning 52 countries.

GHG reduction trackingSAF procurementCSRD/CORSIA complianceCarbon accounting

53.6%

GHG reduction achieved

400M gal

SAF target by 2035

Net zero

2050 commitment

The stakes

Business scale and impact that makes this transformation critical.

53.6%

GHG reduction

Vs 2005 baseline on intensity basis

13M→400M

SAF gallons trajectory

Current to 2035 target

CSRD + CORSIA

Dual regulatory framework

EU and ICAO compliance

$11.17B

Fuel cost exposure

Transition risk from carbon pricing

Current-state friction

Data

Carbon Accounting Complexity

Tracking GHG emissions across 5,400+ daily flights, 52 countries, Monroe Energy refinery operations, and ground equipment requires integrating dozens of data sources with varying quality and granularity. Manual carbon accounting processes struggle to produce the real-time visibility needed for operational sustainability decisions.

50+ emission data sources

SAF

SAF Supply Chain Scaling

Delta aims to scale SAF procurement from 13M gallons today to 400M gallons by 2035 — a 30x increase. The SAF market is fragmented with inconsistent quality certifications, variable pricing, and limited supply visibility. AI-driven procurement optimization is essential to navigate this emerging market.

30x SAF scaling needed by 2035

Compliance

Dual Regulatory Framework Challenge

Delta must comply with both CSRD (EU Corporate Sustainability Reporting Directive) and CORSIA (ICAO's Carbon Offsetting and Reduction Scheme for International Aviation) — two overlapping but distinct frameworks with different methodologies, timelines, and reporting requirements across jurisdictions.

2 major frameworks, 52 countries

Intelligent choices architecture

Four-step agentic decision loop powering autonomous operations.

STEP 01

Sense

What the agents observe

↳ Flight-level fuel burn data from ACARS and post-flight reports across all operations
↳ Monroe Energy refinery emissions, energy consumption, and production metrics
↳ SAF market pricing, availability, and certification status from global suppliers
↳ Regulatory updates from CSRD, CORSIA, EPA, and international environmental agencies

ACARS fuel data · Monroe Energy EMS · SAF market intelligence platform · Regulatory monitoring service

STEP 02

Decide

How the agents reason

↳ Carbon intensity optimization recommending route and fleet decisions for emissions targets
↳ SAF procurement optimization balancing price, availability, and certification requirements
↳ CSRD/CORSIA reporting strategy determining optimal methodology choices by jurisdiction
↳ Carbon credit portfolio optimization for offset requirements

Carbon optimization model · SAF procurement optimizer · Regulatory compliance engine · Carbon credit analyzer

STEP 03

Act

What the agents do

↳ Generate automated carbon accounting reports by flight, route, and entity
↳ Execute SAF procurement recommendations within approved supplier frameworks
↳ Produce CSRD and CORSIA compliance filings with supporting documentation
↳ Publish sustainability dashboards for investor relations and public reporting

Carbon reporting engine · SAF procurement system · Compliance filing platform · Sustainability dashboard

STEP 04

Learn

How the agents improve

↳ Emissions reduction effectiveness analysis by initiative and investment type
↳ SAF supplier performance tracking on quality, delivery, and pricing trends
↳ Regulatory interpretation updates as CSRD and CORSIA rules evolve
↳ Fleet renewal and route optimization impact assessment on sustainability targets

Sustainability analytics · Supplier scorecard · Regulatory change tracker · Fleet emissions modeler

With Q3 CORSIA reporting due in 45 days, the ESG agent aggregates flight-level emissions data for 487,000 international flights, identifies a 2.3% data gap in African route fuel reports, auto-generates data quality corrections using aircraft performance models, produces the complete CORSIA filing with 99.7% automated data coverage, and flags 3 jurisdictions where CSRD methodology differs from CORSIA — giving the sustainability team 30 days to review instead of scrambling for 2 weeks.

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 sustainability dashboard

AI recommends actions

AI manages routine reporting

AI manages ESG operations

Sustainability ecosystem orchestration

Description

Carbon accounting and sustainability performance dashboards for ESG team and investor relations.

AI generates emissions calculations, SAF procurement recommendations, and compliance filing drafts; ESG team reviews and approves all outputs.

Agent autonomously produces routine emissions calculations, SAF market analyses, and internal sustainability reports; ESG team focuses on strategy, CSRD/CORSIA filings, and stakeholder engagement.

Full sustainability operations automation from data collection through reporting with human intervention for novel regulatory interpretations and strategic decisions.

Cross-agent coordination of sustainability with fleet planning, fuel procurement, operations, and finance for integrated ESG-informed decision-making across the enterprise.

Team type

Traditional squads

Human-led with AI copilot

AI-led with human oversight

Autonomous with guardrails

Agent ecosystem

Guardrails

Read-only analytics; all reporting and compliance filings managed manually

All public-facing reports require team approval; regulatory filings require legal review

External reports and regulatory filings require approval; SAF procurement above thresholds needs sign-off; investor communications always human-reviewed

Regulatory filing accuracy standards immutable; public commitment verification; legal review on new compliance frameworks

Cross-agent sustainability impact assessment; regulatory compliance immutable; strategic net-zero direction by CSO

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	GHG intensity reduction	53.6%	60%	Net-zero 2050 trajectory and investor confidence
L1 Exec	SAF procurement volume	13M gal	100M gal	Sustainability commitment delivery and regulatory readiness
L2 Ops	Carbon data automation	40%	90%	Reporting accuracy and team productivity
L3 AI Ops	Automated compliance filings	10%	70%	Regulatory risk reduction and cost efficiency
L4 AI Decision	Emissions forecast accuracy	±12%	±5%	Strategic planning for net-zero trajectory

TCS proof points

TCS IP

TCS Sustainability Intelligence Platform

Enterprise ESG management platform combining automated carbon accounting, regulatory compliance, and sustainability analytics for complex global organizations with multi-framework reporting requirements.

Global enterprise deployments

85%

Carbon data automation achieved

Regulatory frameworks supported

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 emissions prediction and SAF optimization models
→ Governance controls for regulatory compliance verification and audit trails
→ Observability tracking emissions metrics, SAF procurement, and compliance filing status

← Back to home ← AI for global business services

Related use cases

→ GBS-01: Finance Automation → OPS-04: Flight Dispatch → OPS-02: Predictive Maintenance