Enterprise AI Case Studies: Evidence & Sources

By industry · the full story behind each cell

25 cases, each one explained

01 · Finance · 5 cases

Tighter risk control, fewer people

SUPPORT · KLARNA × OPENAI1

1 AI = 700 agents

Two-thirds of support chats taken over in month one; resolution 11 min → 2 min; a projected $40M profit improvement for 2024

CONTRACT REVIEW · JPMORGAN2

360,000 hrs/yr → seconds

Commercial loan review automated end to end — the annual workload of 180 people

AML · HSBC × GOOGLE3

Triage −60%, 2–4× more caught

Machine learning replaced the rules engine as the primary monitoring system

BRANCH DESK · ICBC × PANGU4

5 steps → 1 at the counter

The model generates the handling path; 5+ minutes saved per transaction

ENGINEERING · EMIRATES NBD5

1,000 engineers +20%

≈ 200 engineers gained for free — a leading Dubai bank

Illustrative estimate, not a forecast · a 200-seat support desk → ≈120 seats of workload; a 10-person review team → 3 saved. Exact figures come out in week one of a Bootcamp.

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02 · Energy & industry · 4 cases

The heavier the assets, the more AI saves

GROUP-WIDE · ADNOC6

$500M in 2023

30+ AI tools across the value chain; value measured and publicly disclosed

MINING · SHANDONG ENERGY × PANGU7

One mine, ¥20M more per year

Clean coal yield +0.2%; new use cases shipped in 1 month instead of 6

UTILITIES · JACOBS8

Plant-wide power −20%

Another 20% saved at a water plant already optimized the traditional way

MAINTENANCE · CROSS-INDUSTRY9

Downtime −30–50%

Predictive maintenance: cost −10–40%, asset life +20–40%

Illustrative estimate, not a forecast · loss per hour of downtime × annual downtime hours × 30% — the starting math for predictive maintenance alone.

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03 · Manufacturing & QA · 4 cases

The inspection line is where AI replaces the most people

PANEL QA · TCL CSOT10

The work of 140 inspectors

QA headcount −50%; per-image judgment 2s → 0.5s; ¥10M+ saved

LINE VISION · PEGATRON × NVIDIA11

Labor −7% · defects −67%

Vision AI on the assembly line cuts headcount and defect rate together

FABRIC INSPECTION · CASIA VISION12

Inspection cost −70%

CASIA Vision case: 30 people → 15, 3–5× faster; missed defects 20–30% → under 3%

FINAL ASSEMBLY · AIRBUS × PALANTIR13

A350 deliveries +33%

Capacity is revenue — production bottlenecks cleared with data

Illustrative estimate, not a forecast · a 10-person QA crew at −50% ≈ 5 people saved; on three shifts, 15.

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04 · Supply chain & logistics · 4 cases

One AI negotiating with 2,000 suppliers at once

PROCUREMENT · WALMART14

Purchasing cost −3%

AI negotiates with 2,000 tail suppliers simultaneously; cycles of weeks → 11 days; 68% close rate

WAREHOUSE PICKING · DHL × LOCUS15

+30–180% per person-hour

Cobots + AI dispatch, proven across 40+ warehouses and a billion picks

PARCEL SORTING · ZTO16

40 people, 3× the volume

Automated sorting at 750,000 parcels/day — same crew, triple throughput

DEMAND FORECASTING · CROSS-INDUSTRY17

Forecast error −20–50%

Inventory −20–30%, logistics cost −5–20%

Illustrative estimate, not a forecast · $70M annual procurement with tail categories at −3% ≈ around $500k saved per year.

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05 · Services & chains · 4 cases

The thicker the payroll, the easier the math

PROPERTY SECURITY · ONEWO18

1,200 staff redeployed

AI remote patrol replaces on-site shifts; 2.3× per-person efficiency across 479 sites

HOTELS · ATOUR GROUP19

¥7.5M saved per year

300,000+ work hours of operations and service automated

RESTAURANT CHAINS · YUM CHINA20

Beats store managers by 10–15 pts

Sales forecasting and ordering across 16,000 stores; 95% of managers use it daily

GOVERNMENT · DUBAI㉑

Operating cost −35%

60%+ of routine inquiries resolved by AI outright; service 25% faster

Illustrative estimate, not a forecast · a 1,000-person property or chain business on the Onewo ratio ≈ 100+ people redeployed.

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06 · The mid-cap chapter · 4 cases

This is not a giants-only game

400-PERSON COMPANY · SYNTHESIA㉒

Support demand grew, headcount unchanged

Intercom reports 87% self-serve resolution; handling time 5 days → 4.6 hours

REGIONAL CREDIT UNION · NEIGHBORHOOD㉓

90% of calls taken in 48 hours

AI call handling paid off within two days; $4.4M saved on support operations

MID-SIZE CONSULTANCY · ELIASSEN㉔

6,000 hrs/yr = 3 full-timers

Financial close automated across 80+ branch offices

SPECIALIST SME · GOLDSTONE ROBOTICS㉕

Service tickets: 2 weeks → 1–3 days

DingTalk AI ticket assistant serving 1,000 service outlets

Illustrative estimate, not a forecast · Forrester's independent model for a 200-person, $35M-revenue firm: operating cost −20%, net income +6%.

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Two deep reads · mid-caps count too

You don't have to be a giant to deserve this ledger

DEEP READ · SYNTHESIA · 400 PEOPLE

Customers grew 7× — support headcount grew by zero

The struggle: customers seeking support each month rose from 40,000 to 316,000 in four months — the old playbook meant growing the support team 7× too.

What the AI did: holds the front line; routine questions resolved outright, complex ones escalated to a human, missed answers fed back into the knowledge base.

87%self-serve resolution

5 days → 4.6 hrsresolution time

+0support hires

DEEP READ · NEIGHBORHOOD CU · REGIONAL CREDIT UNION

One hotline, $4.4M saved per year

The struggle: the hotline caught only half of inbound calls; peak-hour queues, member complaints, and hiring costs that didn't add up.

What the AI did: voice AI answers every call first, handles routine business end to end, routes complex matters to a human agent.

48 hourstakeover 50→90%

$4.4Moperating savings

43,000 hrsmember wait time cut

Appendix · where every number comes from

Sources, graded

[Report] media / official / research reports ｜ [Vendor] vendor-published named cases. Methodology references: AWS Enterprise Agentic AI Guide 2026 · MIT NANDA 2025 · IDC 2025 · Menlo Ventures 2025 · Forrester 2024.

How to cite these cases

The figures describe historical third-party reports, not TokenPower deliveries or current performance guarantees. Keep the organization, reporting period and metric together when citing a case. Source labels without a link are not independently verified here.

Synthesia: the 87% figure is a historical self-service rate reported in Intercom’s help material, not the same metric as the Fin resolution rate in the current case study. Compare the case study.

The headcount and the money peers have already saved

25 cases, each one explained

Tighter risk control, fewer people

The heavier the assets, the more AI saves

The inspection line is where AI replaces the most people

One AI negotiating with 2,000 suppliers at once

The thicker the payroll, the easier the math

This is not a giants-only game

You don't have to be a giant to deserve this ledger

Customers grew 7× — support headcount grew by zero

One hotline, $4.4M saved per year

Sources, graded

How to cite these cases

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