Spandan Mahapatra
COM-02 | AI for commercial

GenAI Customer Concierge

Building an AI-powered customer concierge on Amazon Bedrock to transform Delta's 91M annual call center interactions and 1.3B app sessions into personalized, proactive service experiences that reduce cost while elevating the premium brand.

Amazon BedrockCall center transformationApp personalizationProactive service
-40%
Call center volume
+15pts
Customer NPS
-$500M
Service cost reduction

The stakes

Business scale and impact that makes this transformation critical.

91M
Annual calls
Delta reservation and service center
1.3B
App interactions
Fly Delta app annual sessions
$1.8B
Customer service cost
Annual contact center operations
200M+
Annual passengers

Current-state friction

Volume

Overwhelming Call Volume

With 91M annual calls, Delta's contact centers face persistent staffing challenges. Average handle times remain high because agents must navigate multiple legacy systems to resolve issues. During IRROPS events, call volume spikes 300-400%, creating hour-long hold times that damage the premium brand.

91M calls/year
Fragmented

Fragmented Digital Experience

Despite 1.3B annual app interactions, the Fly Delta app still routes many complex requests to human agents. The app, website, and call center operate on different technology stacks with inconsistent personalization, creating disjointed experiences for high-value SkyMiles members.

1.3B app sessions/year
Reactive

Reactive Service Model

Current customer service is overwhelmingly reactive — passengers call after problems occur. Delta has the data to anticipate disruptions and proactively offer solutions (rebooking, vouchers, lounge access) but lacks the AI infrastructure to deliver personalized outreach at scale.

85% reactive interactions

Intelligent choices architecture

Four-step agentic decision loop powering autonomous operations.

STEP 01
Sense
What the agents observe
  • ↳ Customer interaction history across call center, app, email, and social channels
  • ↳ SkyMiles profile data including status, preferences, and lifetime value
  • ↳ Real-time flight status, IRROPS events, and downstream connection impacts
  • ↳ Sentiment signals from voice tone analysis and text sentiment scoring
Amazon Bedrock · SkyMiles CRM · Flight status API · Voice analytics platform
STEP 02
Decide
How the agents reason
  • ↳ Intent classification routing queries to the optimal resolution path
  • ↳ Personalized response generation calibrated to customer status and history
  • ↳ Proactive outreach decisions based on disruption impact and customer value
  • ↳ Escalation logic determining when to transfer to human agents with full context
Amazon Bedrock LLM · Intent classifier · Customer value model · Escalation decision engine
STEP 03
Act
What the agents do
  • ↳ Resolve routine inquiries (flight status, rebooking, baggage) via conversational AI
  • ↳ Push proactive notifications with personalized rebooking options during disruptions
  • ↳ Generate and deliver service recovery offers (vouchers, miles, lounge access) within policy bounds
  • ↳ Transfer to human agents with full conversation context and recommended resolution
  • ↳ Process upgrades, seat changes, and SkyMiles transactions end-to-end
Conversational AI platform · Booking modification API · Service recovery engine · Agent desktop integration
STEP 04
Learn
How the agents improve
  • ↳ Resolution effectiveness analysis by query type and customer segment
  • ↳ Conversation quality scoring using human evaluator feedback loops
  • ↳ Customer satisfaction correlation analysis linking AI interactions to NPS
  • ↳ Continuous fine-tuning on Delta-specific domain knowledge and policies
Conversation analytics · NPS correlation engine · RLHF pipeline · Domain knowledge base
A Diamond Medallion member's connecting flight from SLC to JFK is cancelled due to weather. Before the passenger even checks the app, the concierge agent has already identified three rebooking options, secured a Delta Sky Club pass at SLC, booked a partner hotel with shuttle, and sent a push notification with one-tap acceptance — resolving the disruption in 12 seconds with zero human intervention.

Human + AI autonomy levels

L1 — Tool
CURRENT
L2 — Assistant
TARGET
L3 — Supervised agent
L4 — Autonomous agent
L5 — Agentic workforce
Human role
Human as agent
Human as supervisor
Human as exception handler
Human as quality monitor
Human as strategist
AI role
AI as knowledge assistant
AI handles simple queries
AI resolves most queries
AI manages service experience
Personalized experience orchestration
Description
AI-powered knowledge base and response suggestions for human call center agents.
Conversational AI handles flight status, basic rebooking, and FAQ queries; complex issues immediately routed to human agents.
Agent autonomously resolves 60%+ of customer interactions including rebooking, service recovery, and SkyMiles transactions; escalates sensitive and high-value situations.
Full customer service automation including proactive outreach, complex rebooking, and personalized recovery with human intervention only for novel scenarios.
End-to-end customer journey orchestration coordinating concierge, loyalty, pricing, and operations agents for seamless personalized experiences.
Team type
Traditional squads
Human-led with AI copilot
AI-led with human oversight
Autonomous with guardrails
Agent ecosystem
Guardrails
AI suggests responses; human agents control all customer interactions
Limited to predefined query types; financial transactions require human approval
Service recovery value caps; Diamond/360 members get human option; sentiment-triggered escalation
Compensation limits; legal/liability escalation; brand voice compliance; human override always available
Cross-agent customer value optimization; privacy compliance; brand experience standards

TCS agentic AI agents

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

KPI architecture

LevelKPIBaselineTargetBusiness link
L0 BoardCustomer service cost$1.8B/yr$1.3B/yrDirect SG&A reduction
L1 ExecCustomer NPS6277Brand differentiation and loyalty
L2 OpsAI resolution rate15%60%Contact center efficiency
L3 AI OpsAverage handle time8.5 min2.5 minAgent productivity and cost per contact
L4 AI DecisionProactive resolution rate5%40%Customer effort score and loyalty

TCS proof points

TCS IP
Amazon Bedrock + TCS CustomerFirst AI

Delta's strategic deployment of Amazon Bedrock for GenAI customer service, integrated with TCS CustomerFirst platform for airline-specific domain expertise and multi-channel orchestration.

3
Major airline deployments
55%
AI resolution rate achieved
$680M
Annual service cost savings
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 Amazon Bedrock LLM integration and fine-tuning
  • → Governance controls for customer data privacy and response quality
  • → Observability tracking resolution rates, NPS correlation, and conversation quality
← Back to home← AI for commercial

Related use cases

→ COM-01: Revenue Management→ COM-03: Loyalty & SkyMiles→ COM-04: Station Experience