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Findings

Proven AI and ML Case Studies: Where the Money Actually Moved

Brandon Sneider · March 2026

Klarna is the most cited AI customer service deployment in the world. It is also the most instructive failure.

Executive Summary

The boring ML delivers the biggest returns. JPMorgan’s fraud detection, UPS’s route optimization, and Walmart’s demand forecasting run on traditional ML (gradient boosting, random forests, neural networks) that has been compounding value for years, not months. Combined, these systems drive billions in annual savings with measured, sustained ROI.
Customer service AI is a cautionary tale, not a success story. Klarna cut 700 jobs, claimed $40M in savings, then reversed course when quality collapsed. Bank of America’s Erica works because it augments humans rather than replacing them: 3.2 billion interactions, 98% resolution without escalation, 19% revenue lift.
Drug discovery AI produced its first real clinical proof in 2025. Insilico Medicine’s AI-designed drug hit positive Phase IIa results, published in Nature Medicine. Target-to-Phase-I in 30 months vs. the industry standard of 4-6 years. The first genuine clinical validation of generative AI in pharma.
Enterprise RAG works in production but does not eliminate hallucinations. Stanford’s empirical study found legal RAG tools hallucinate 17-34% of the time. Morgan Stanley’s deployment hit 98% advisor adoption and contributed to $64B in net new assets. The gap between vendor claims and measured accuracy is significant.
Traditional ML still beats LLMs on structured data. On tabular data (fraud, pricing, recommendations, anomaly detection), XGBoost and gradient boosting achieve 99%+ AUC while running 100x cheaper and 1,000x faster than LLM-based approaches. No production evidence exists that GenAI outperforms classical ML for these use cases.

1. Customer Service AI: The Klarna Correction and What It Reveals

Klarna: The Full Arc

Klarna is the most cited AI customer service deployment in the world. It is also the most instructive failure.

The initial claim (February 2024): Klarna’s OpenAI-powered chatbot handled 2.3 million conversations in its first month, replacing the equivalent of 700 full-time agents. The company projected $40 million in annual profit improvement. Cost per transaction fell 40%, from $0.32 to $0.19. Resolution times dropped 82%.

What actually happened (2025): CEO Sebastian Siemiatkowski publicly admitted “we went too far.” Customer satisfaction declined. Complex issues received generic, repetitive responses. The company began rehiring human agents in May 2025, piloting a hybrid “Uber-style” workforce model with flexible remote agents.

The numbers behind the reversal: While per-transaction costs fell, customer service and operations costs still rose year-over-year. The 40% cost reduction came with unmeasured quality degradation. More than 55% of companies that made AI-driven customer service layoffs now report regretting the decision.

Metric	Klarna’s Claim	What Happened
Conversations handled	2.3M/month by AI	Verified, but quality declined
Cost per transaction	$0.32 to $0.19	Verified, but total costs still rose
Staff replaced	700 FTEs	Rehiring began May 2025
Projected savings	$40M/year	Unverified; CEO acknowledged quality trade-off
Customer satisfaction	Not disclosed initially	Declined; prompted strategic reversal

Source credibility: MEDIUM. The cost metrics come from Klarna’s own reporting. The reversal is independently confirmed by CNBC, Entrepreneur, and CX Dive. No independent audit of the $40M savings figure exists.

What went wrong: Klarna optimized for cost, not customer experience. AI handled volume well but failed on empathy, nuance, and escalation judgment. The company discovered that customer service quality is a revenue driver, not just a cost center.

Bank of America Erica: The Quiet Counter-Example

While Klarna made headlines, Bank of America’s Erica virtual assistant has been compounding value since 2018 with almost no controversy.

Measured outcomes (through 2025):

50 million users, 3.2 billion total interactions
58 million interactions per month
98% of inquiries resolved without human escalation
19% revenue increase through cross-sell suggestions during interactions
60% of interactions are now proactive (Erica initiating outreach)
Equivalent daily workload of 11,000 staff

Why it works: Erica augments the banking experience rather than replacing human bankers. It handles balance inquiries, transaction searches, bill reminders, and spending insights. Complex issues route to humans. The system was built to deflect simple inquiries, not to replace the relationship.

Source credibility: MEDIUM-HIGH. Metrics from Bank of America press releases (August 2025, February 2025), cross-referenced with CX Dive reporting. BofA has reported these numbers consistently over seven years, which adds credibility through sustained disclosure.

Delta Air Lines: Measured but Modest

Delta’s AI-powered virtual assistant reduced call center volume by 25%, improved satisfaction scores by 15%, and cut response times by 40%. Annual savings: approximately $1 million.

Source credibility: MEDIUM. Company-reported metrics from CES 2025 and Delta investor communications. The numbers are modest enough to be plausible, which paradoxically increases their credibility.

2. Fraud Detection and Financial ML: The Proven Workhorse

Fraud detection is where ML has the longest track record of hard, measurable ROI. These are not pilot programs. They are production systems running on billions of transactions.

JPMorgan Chase

JPMorgan operates 400+ AI use cases across the bank, but fraud detection is where the numbers are clearest.

Measured outcomes:

$1.5 billion in fraud losses prevented through real-time AI detection
95% reduction in false positives for anti-money laundering surveillance
Fraud detection operates 300x faster than legacy rule-based systems
98% accuracy in real-time transaction analysis
Fraud costs held flat despite 12% compound annual growth in attack volume

Investment scale: JPMorgan spent $17 billion on technology in 2024, with AI as a core component. The bank reports $2 billion in annual AI-driven benefits, roughly matching its AI investment costs, with 30-40% year-over-year growth in benefits.

Source credibility: MEDIUM-HIGH. The $1.5 billion figure comes from Reuters (May 2025) and JPMorgan investor presentations. The 95% false-positive reduction is company-reported but aligns with independent industry benchmarks. Constellation Research has independently analyzed JPMorgan’s AI investments.

Stripe Radar

Stripe’s ML-based fraud detection processes billions of transactions and has been learning from network data for over a decade.

Measured outcomes (2024):

$6 billion in falsely declined legitimate transactions recovered through AI-based Authorization Boost, a 60% year-over-year increase
17% reduction in dispute rates for Radar users, while industry-wide ecommerce fraud rose 15%
0.1% false block rate on legitimate payments
Fraud assessment on 1,000+ transaction characteristics in under 100 milliseconds
80% reduction in carding attacks over two years

Source credibility: MEDIUM. These are Stripe’s own figures from their 2024 annual review. The $6 billion recovery figure is specific enough to be verifiable by Stripe’s merchant base. No independent audit published.

PayPal

PayPal’s ML models process 25 billion transactions annually, using real-time signal analysis across both merchant and consumer activity.

Source credibility: LOW-MEDIUM. PayPal provides limited quantified outcomes publicly, relying more on capability descriptions than measured results. The scale of their data network (both-side visibility) is a genuine competitive advantage, but specific ROI figures are not independently available.

What sustained results look like: JPMorgan and Stripe have reported consistent improvements over multiple years. This is not a one-quarter story. The ML models compound: more data produces better models, which catch more fraud, which generates more labeled data. This flywheel is the real competitive moat, and it runs on traditional ML (gradient boosting, random forests, neural networks), not LLMs.

3. Supply Chain and Demand Forecasting: Billions in Quiet Value

UPS ORION

UPS’s On Road Integrated Optimization and Navigation system is one of the longest-running, best-documented ML deployments in any industry. Launched in 2013, fully deployed by 2016, still compounding value a decade later.

Measured outcomes:

100 million fewer driving miles per year
$300-400 million in annual cost savings
10 million gallons of fuel saved annually
100,000 metric tons of CO2 emissions eliminated per year
Handled a 15% volume spike during 2024 holiday season without adding vehicles
55,000 U.S. drivers using ORION-optimized routes in 2025

Investment: $250 million. ROI achieved within two years. Now in its tenth year of sustained returns.

Source credibility: HIGH. UPS has disclosed ORION metrics in SEC filings, investor presentations, and sustainability reports consistently since 2015. The $250M investment and $400M annual savings have been independently reported by Supply Chain Dive, logistics analysts, and academic case studies.

Walmart

Walmart’s ML platform (“Element”) runs demand forecasting using multi-horizon recurrent neural networks built entirely in-house.

Measured outcomes:

Inventory accuracy improved up to 90%
30 million unnecessary driving miles eliminated through route optimization
Automated supplier negotiations achieving 68% success rate with 3% average cost savings
$55 million saved from a single internal ML system
1.5% average cost savings plus 35 extra days of extended payment terms through Pactum AI negotiations
Now selling their route optimization technology as SaaS to other businesses

Source credibility: MEDIUM. Walmart discloses some metrics through corporate communications and Supply Chain Dive reporting. The $55M single-system savings is company-reported. Walmart does not break out AI-specific ROI in SEC filings.

Amazon

Amazon’s ML-driven supply chain is the largest in the world but among the least transparent in specific ROI disclosure.

What is known:

CEO Andy Jassy has stated AI-driven improvements save “pennies per package” that translate to billions at Amazon’s scale
98% on-time delivery maintained during 2024 holiday surge through AI forecasting agents
25% reduction in holding costs attributed to agentic supply chain systems
Amazon Forecast (their internal ML tool) now available as an AWS product, which itself validates its effectiveness

Source credibility: LOW-MEDIUM. Amazon discloses minimal quantified AI ROI. The “pennies per package” framing from earnings calls is deliberately vague. The 98% on-time delivery and 25% holding cost reduction come from industry analysis, not Amazon’s own disclosures.

4. Predictive Maintenance: Industrial ML With Hard ROI

Siemens (Senseye Predictive Maintenance)

Siemens acquired Senseye in 2022 and deployed its ML-based predictive maintenance across industrial customers.

Measured outcomes:

50% reduction in unplanned downtime at deployed sites
Full ROI achieved in under three months
Remote monitoring of 10,000+ machines across 100 equipment types
Sachsenmilch (European dairy): detected failing pump before breakdown, six-figure savings from a single incident
System automatically learns from sensor data to detect anomalies and predict failures months in advance

Source credibility: MEDIUM. Metrics from Siemens product materials and customer case studies. The 50% downtime reduction and 3-month ROI are vendor claims, though Sachsenmilch is a named, verifiable customer.

GE Vernova (SmartSignal)

GE’s predictive maintenance platform has the longest track record in industrial AI.

Measured outcomes:

$1.6 billion in customer savings documented by GE’s Industrial Managed Services team
7,000+ critical assets monitored worldwide
Customer-managed deployments push total avoided costs “into the tens of billions” (GE’s estimate)
75% reduction in borescope inspection time through AI-powered analysis

Source credibility: MEDIUM. The $1.6 billion figure is GE’s own reporting but is specific, attributed to their managed services team, and has been cited consistently over multiple years. Gartner positioned GE as a leader in asset performance management in its 2025 Market Guide.

Rolls-Royce TotalCare

Rolls-Royce monitors jet engines in real time through its TotalCare program, using ML-based health monitoring.

Measured outcomes:

400 unplanned maintenance events prevented annually across the fleet
25% extension of service intervals between engine overhauls
75% reduction in internal engine inspection time through AI-powered borescope tools
25% reduction in unplanned downtime

Source credibility: MEDIUM. Rolls-Royce discloses metrics through product materials and press releases. The 400 prevented events figure is specific and has been reported consistently. IBM and Microsoft are named technology partners, adding verification through partner disclosures.

What sustained value looks like: All three companies have been running these systems for 5+ years. The models improve continuously because every avoided failure produces training data that makes the next prediction better. This is the same flywheel dynamic as fraud detection: traditional ML compounding quietly over years.

5. Drug Discovery and Healthcare ML: The First Real Proof

Insilico Medicine: The Landmark Case

Insilico Medicine produced the first clinically validated AI-designed drug for an AI-discovered target. This is the single most important proof point in AI drug discovery to date.

Measured outcomes:

ISM001-055 (Rentosertib): positive Phase IIa results for idiopathic pulmonary fibrosis, published in Nature Medicine (June 2025)
Patients on 60mg dose showed +98.4 mL improvement in lung function vs. -20.3 mL decline in placebo group
Double-blind, placebo-controlled trial: 71 patients across 21 sites
Novel target discovery to Phase I in under 30 months (industry standard: 4-6 years)
22 preclinical candidates nominated from 2021-2024, each requiring only 60-200 molecules synthesized (vs. thousands in traditional drug discovery)
Average 12-18 months from project initiation to preclinical candidate nomination

Source credibility: HIGH. Phase IIa results published in Nature Medicine (peer-reviewed). Clinical trial registered and conducted across 21 sites in China. This is the strongest evidence in the AI drug discovery space because it is independently verifiable through a top-tier journal.

What this proves and what it does not: Insilico demonstrates that AI can dramatically accelerate the target-identification-to-clinical-candidate pipeline. It does not yet prove that AI-designed drugs will succeed in Phase III trials or reach market approval. The path from Phase IIa to approved drug remains long and uncertain.

Recursion Pharmaceuticals

Recursion merged with Exscientia in November 2024 to create the largest vertically integrated AI drug discovery platform.

Measured outcomes:

REC-617 (CDK7 inhibitor): Phase I data showing early anti-tumor activity across 29 patients with advanced solid tumors. One confirmed partial response, five stable disease cases.
REC-4881 (MEK1/2 inhibitor): demonstrated reduced polyp burden in Phase 1b/2 for Familial Adenomatous Polyposis
Seven clinical readouts expected within 18 months (as of late 2025)

Source credibility: MEDIUM-HIGH. Clinical data from registered trials and company investor communications. Recursion’s pipeline is publicly trackable through clinicaltrials.gov.

The Broader Evidence

Industry-wide, AI-designed drugs show 80-90% success rates in Phase I trials vs. 40-65% for traditionally designed drugs. Tufts CSDD found AI/ML decreased trial planning time by 18% through smarter protocol design and site selection (2025 DIA Global Annual Meeting survey).

The honest caveat: Phase I success measures safety, not efficacy. The real test is Phase II/III, where most drugs still fail. AI has proven it can get drugs to the clinic faster and cheaper. It has not yet proven it can get better drugs to market.

6. Enterprise RAG Deployments: Promise Meets Reality

The Stanford Reality Check

Stanford’s empirical study (Magesh et al., Journal of Empirical Legal Studies, 2025) is the most rigorous independent evaluation of RAG-based AI in production. It tested legal AI research tools that claim to be “hallucination-free.”

Key findings:

Lexis+ AI: 17%+ hallucination rate
Westlaw AI-Assisted Research: 34%+ hallucination rate
Raw GPT-4: 43% hallucination rate
RAG reduces hallucinations vs. raw LLMs but does not eliminate them
Vendor claims of “hallucination-free” legal research are demonstrably false

Source credibility: HIGH. Preregistered empirical study by Stanford researchers (Manning, Ho), peer-reviewed in the Journal of Empirical Legal Studies. First study of its kind.

What this means: RAG is a meaningful improvement over raw LLMs. It is not the reliability guarantee that vendors sell. Any organization deploying RAG in high-stakes domains (legal, medical, financial compliance) needs human verification workflows.

Morgan Stanley: The Production Success Story

Morgan Stanley’s GPT-4 powered RAG system for wealth management advisors is the strongest enterprise RAG deployment case study available.

Measured outcomes:

98% adoption among advisor teams (16,000+ financial advisors)
Access to 100,000+ research reports and documents
Document usage increased from 20% to 80% after deployment
Advisors onboarded in under 30 minutes
Q3 2024: $64 billion in net new assets; 100,000 new clients acquired
Executives directly attributed performance improvements to AI-enabled efficiency gains

Source credibility: MEDIUM-HIGH. Adoption figures from Morgan Stanley press releases and OpenAI case study. The $64B net new assets figure is from Morgan Stanley’s quarterly earnings (SEC filing). The causal attribution to AI is company-claimed and should be treated with appropriate skepticism; many factors drive asset gathering.

Enterprise RAG: Where It Works vs. Where It Fails

Use Case	RAG Effectiveness	Evidence
Internal knowledge base search	HIGH	85% reduction in search time (102 min to 15 min); multiple enterprise deployments
Financial advisor research	HIGH	Morgan Stanley: 98% adoption, measurable business impact
Customer support tier-1 deflection	MODERATE-HIGH	Cynet: satisfaction improved from 79 to 93; 50% of tickets resolved at tier 1
Legal research	MODERATE	Accuracy improved vs. raw LLMs but 17-34% hallucination rate remains (Stanford)
Compliance/regulatory	EARLY	Banks deploying but limited published accuracy data

7. Traditional ML vs. GenAI: Where Boring Wins

This is the section most vendors hope you never read. The largest share of ML-driven business value in production today comes from classical machine learning, not large language models.

The Tabular Data Verdict

A comprehensive benchmark published in 2025 (Neural Computing and Applications) tested machine learning and deep learning models across diverse tabular datasets. The findings are unambiguous.

XGBoost and CatBoost outperform deep learning and LLM-based approaches on structured data, while being an order of magnitude more computationally efficient. The consensus from the R Consortium, Kaggle competitions, and academic benchmarks is that gradient boosting machines remain the optimal choice for tabular data tasks.

Where Traditional ML Dominates in Production

Domain	ML Method	Why It Wins	Production Evidence
Fraud detection	Gradient boosting ensembles (XGBoost, LightGBM, CatBoost)	Sub-100ms inference, 99%+ AUC, interpretable	JPMorgan, Stripe, every major bank
Credit scoring	Logistic regression, gradient boosting	Regulatory requirement for explainability; XGBoost achieves 99% AUC on credit card fraud	Industry standard for 20+ years
Demand forecasting	Neural networks, gradient boosting, time series models	Years of training data, proven at scale	Walmart, Amazon, UPS
Pricing optimization	Gradient boosting, regression models	Real-time inference at scale, interpretable	Airlines, hospitality, ecommerce
Anomaly detection	Isolation forests, autoencoders, gradient boosting	Works on structured sensor/transaction data	Manufacturing, cybersecurity, telecom
Recommendation engines	Collaborative filtering, matrix factorization, two-tower neural nets	Latency requirements; cost per inference matters at billions of recommendations	Netflix, Spotify, Amazon

Why LLMs Do Not Replace Traditional ML Here

Three fundamental constraints keep LLMs out of these production workloads:

Latency. Fraud detection requires sub-100ms decisions. LLM inference takes seconds. A gradient boosting model scores a transaction in single-digit milliseconds.
Cost. Running an LLM on every transaction, recommendation, or pricing decision at the scale of JPMorgan (billions of transactions) or Amazon (billions of recommendations) would cost orders of magnitude more than traditional ML. An XGBoost model runs on a single CPU.
Explainability. Regulators require that credit decisions, fraud flags, and pricing changes be explainable. Traditional ML models produce feature importance scores. LLMs produce prose, which does not satisfy regulatory requirements.

The stacking ensemble result (2025): A fraud detection framework combining XGBoost, LightGBM, and CatBoost with explainable AI techniques achieved 99% accuracy and 0.99 AUC-ROC. No LLM-based system has matched this on production fraud data.

Where GenAI Adds Value Alongside Traditional ML

GenAI is not useless in these domains. It adds value in specific, bounded roles:

Feature engineering: LLM embeddings can encode unstructured text (customer support notes, product descriptions) into features that traditional ML models consume
Anomaly explanation: After a traditional ML model flags a transaction, an LLM can generate a human-readable explanation
Synthetic data generation: LLMs can generate realistic synthetic training data for rare fraud patterns

The pattern is clear: traditional ML makes the decision; GenAI provides context around it.

Key Data Points

Company	Domain	Measured Outcome	Sustained?	Source Credibility
JPMorgan	Fraud detection	$1.5B in prevented losses; 95% fewer AML false positives	Yes (multi-year)	MEDIUM-HIGH
Stripe	Fraud detection	$6B in recovered false declines (2024); 80% carding reduction	Yes (10+ years of ML)	MEDIUM
UPS	Route optimization	$300-400M annual savings; 100M fewer miles	Yes (10+ years)	HIGH
Walmart	Demand forecasting	$55M from single system; 90% inventory accuracy	Yes (multi-year)	MEDIUM
Bank of America	Customer service AI	3.2B interactions; 98% resolution; 19% revenue lift	Yes (7 years)	MEDIUM-HIGH
Morgan Stanley	Enterprise RAG	98% advisor adoption; $64B net new assets (Q3 2024)	Yes (2+ years)	MEDIUM-HIGH
Klarna	Customer service AI	$0.32 to $0.19 per transaction, then reversed course	No (reversed in 2025)	MEDIUM
Insilico Medicine	Drug discovery	Phase IIa positive results; 30-month target-to-Phase-I	Too early to judge	HIGH
Siemens	Predictive maintenance	50% downtime reduction; ROI in under 3 months	Yes (3+ years)	MEDIUM
GE Vernova	Predictive maintenance	$1.6B in documented customer savings	Yes (5+ years)	MEDIUM
Rolls-Royce	Predictive maintenance	400 prevented events/year; 25% longer service intervals	Yes (5+ years)	MEDIUM

What This Means for Your Organization

Start with boring ML, not GenAI. The largest, most sustained returns in this research come from traditional machine learning applied to structured business problems: fraud, forecasting, routing, pricing, anomaly detection. If your organization has not deployed XGBoost on your transaction data, you are leaving money on the table while debating which LLM to buy. These systems are cheaper to build, faster to deploy, and more explainable to regulators and boards than anything involving a large language model.

Treat customer service AI as augmentation, not replacement. Klarna’s reversal is not an anomaly; it is a pattern. The organizations seeing sustained value (Bank of America, Morgan Stanley, Delta) are those that use AI to make human workers more effective, not to eliminate them. The economics are counterintuitive: the cost savings from replacing humans look compelling in a spreadsheet, but customer experience degradation erodes revenue in ways that take 6-12 months to show up in the numbers. Bank of America’s Erica resolves 98% of inquiries without escalation because it was designed for the inquiries that do not need a human, not for the ones that do.

Deploy enterprise RAG with human verification, not vendor trust. The Stanford study demolishes the claim that RAG eliminates hallucinations. It reduces them. Morgan Stanley’s success came from deploying RAG where the cost of a wrong answer is low (advisor research productivity) and where human judgment remains in the loop (advisors still make the client recommendations). Any deployment in legal, compliance, or medical contexts requires verification workflows that assume the system will be wrong 15-30% of the time. Plan for that, and RAG becomes a genuine productivity multiplier. Ignore it, and you are shipping liability.

If you are evaluating which AI use cases in your organization have genuine evidence behind them versus vendor-driven hype, that distinction is worth a focused conversation.

Sources

Klarna AI assistant press release and subsequent reversal coverage. Klarna International (Feb 2024); CNBC (May 2025); Entrepreneur (2025); CX Dive (2025). Credibility: MEDIUM (company-reported metrics; reversal independently confirmed).
Bank of America Erica metrics. BofA press releases (Apr 2024, Feb 2025, Aug 2025, Mar 2026). Credibility: MEDIUM-HIGH (seven years of consistent reporting).
JPMorgan AI and fraud detection. Reuters (May 2025); Constellation Research; JPMorgan investor presentations. Credibility: MEDIUM-HIGH (mix of independent reporting and company disclosure).
Stripe Radar and Adaptive Acceptance results. Stripe annual review (2024); Stripe blog and documentation. Credibility: MEDIUM (company-reported; specific and verifiable by merchants).
UPS ORION system. SEC filings; Supply Chain Dive; UPS investor presentations and sustainability reports (2015-2025). Credibility: HIGH (consistent multi-year disclosure in regulatory filings).
Walmart ML platform. Supply Chain Dive (2024-2025); CIO Dive; Walmart corporate communications. Credibility: MEDIUM (company-reported; some figures from industry reporting).
Siemens Senseye Predictive Maintenance. Siemens product materials and customer case studies (2024). Credibility: MEDIUM (vendor materials with named customers).
GE Vernova SmartSignal. GE Vernova product documentation; Gartner Market Guide 2025. Credibility: MEDIUM (company-reported with Gartner independent positioning).
Rolls-Royce TotalCare. Rolls-Royce press releases; RTInsights; IBM partner materials. Credibility: MEDIUM (company-reported with partner corroboration).
Insilico Medicine ISM001-055 (Rentosertib). Nature Medicine (Jun 2025); EurekAlert; clinicaltrials.gov. Credibility: HIGH (peer-reviewed in top journal; registered clinical trial).
Recursion Pharmaceuticals pipeline. Recursion investor relations; BioPharma Dive; clinicaltrials.gov. Credibility: MEDIUM-HIGH (public company filings; registered trials).
Stanford Legal RAG Hallucination Study. Magesh et al., Journal of Empirical Legal Studies (2025); Stanford HAI. Credibility: HIGH (preregistered, peer-reviewed, independent).
Morgan Stanley AI deployment. Morgan Stanley press releases; OpenAI case study; CNBC (Jun 2024); Morgan Stanley Q3 2024 earnings. Credibility: MEDIUM-HIGH (mix of SEC filings and vendor case study).
Tabular data ML benchmarks. Neural Computing and Applications (2025); R Consortium; arxiv.org (2024-2025). Credibility: HIGH (academic benchmarks, reproducible).
Tufts CSDD AI in clinical trials. DIA Global Annual Meeting survey (2025). Credibility: MEDIUM-HIGH (academic research center).
Delta Air Lines AI. CES 2025 presentation; CX Dive; Delta investor communications. Credibility: MEDIUM (company-reported).

Brandon Sneider | brandon@brandonsneider.com March 2026