🛍️
Shopify
E-commerce · 12,000+ employees · CA
// stack
ChatGPTClaudeGitHub CopilotCursor
// use case
Internal "Sidekick" merchant assistant + ~80% of engineers using AI editors daily
// outcome
→ +22% PR throughput; 35% drop in L1 support tickets
Live AI deployments at operating companies
Not vendor case studies — a working picture of which AIs are running inside real businesses, who they replaced, and what changed in the metrics.
🏦
Klarna
Fintech · 5,400 employees · SE
// stack
ChatGPT EnterpriseOpenAI Assistants
// use case
Customer service AI agent handling tier-1 conversations end-to-end
// outcome
→ Equivalent of 700 FT agents; 25% lower repeat inquiries
🏛️
JPMorgan
Banking · 300,000 employees · US
// stack
LLM Suite (internal)Claude
// use case
Document review, KYC drafting, code modernisation in CIB
// outcome
→ Hours saved per analyst per week: ~4
🏦
DBS Bank
Banking · 36,000 employees · SG
// stack
GeminiVertex AIBigQuery ML
// use case
Risk scoring, compliance copilots, multilingual customer support
// outcome
→ 20% reduction in compliance review cycle time
🚗
Mercedes-Benz
Automotive · 170,000 employees · DE
// stack
Microsoft CopilotAzure OpenAI
// use case
In-car voice assistant, R&D code review
// outcome
→ CSAT on voice up 31%
🛡️
Palantir
Defence / Data · 4,000 employees · US
// stack
AIPClaudeLlama
// use case
Embedded LLMs in Foundry workflows for analysts
// outcome
→ Decision time on operational queries: 8 min → 90 sec
🎧
Spotify
Media · 8,200 employees · SE
// stack
ElevenLabsOpenAISuno (eval)
// use case
AI DJ voice generation, multilingual podcast dubbing
// outcome
→ 11 languages now serviced from a single English source
🛒
Walmart
Retail · 2.1M employees · US
// stack
ElementAnthropic ClaudeVertex AI
// use case
Negotiation copilot for suppliers; in-store associate assistant
// outcome
→ ~3% better terms across 70+ supplier negotiations
💊
Novo Nordisk
Pharma · 64,000 employees · DK
// stack
ChatGPTClaudeGemini Pro
// use case
Clinical trial document drafting and regulatory submissions
// outcome
→ Submission prep ~40% faster
🧴
Unilever
CPG · 128,000 employees · UK
// stack
JasperMidjourneyAdobe Firefly
// use case
Brand-safe creative variants for 400+ markets
// outcome
→ Marketing asset cost / brand: -55%
🏁
Oracle Red Bull Racing
Motorsport · 1,300 employees · UK
// stack
Oracle AIOpenAICustom RAG
// use case
Race-day strategy modelling and telemetry summarisation
// outcome
→ Strategist throughput +18% in mid-race calls
🧭
BCG
Consulting · 32,000 employees · US
// stack
ChatGPT EnterpriseClaudeHex
// use case
Decks, models, partner research synthesis
// outcome
→ ~20–40% time saving on knowledge work tasks