Methodology

How AI deployment evidence is collected and ranked

AI Use Case Hub turns public deployment evidence into adoption intelligence. The ranking is meant to help readers find useful, specific, and differentiated real-world AI implementations faster, while keeping the source context close enough to verify.

Default ranking sort

Time-adjusted innovativeness

A directional score designed for discovery, not a formal audit or endorsement.

Open Deployments Send corrections or ideas

The process

The platform combines automated collection with structured normalization so each case can be compared across the same dimensions.

1. Source discovery

The corpus begins with public customer stories, provider and partner material, technical documentation, press releases, research, and other visible sources. A source is evidence, not an independent audit of every statement it contains.

2. Deployment extraction

One source can contain zero, one, or several documented deployments. Extraction identifies independent deployment subjects instead of treating every organization in an article as one implementation.

3. Entity attribution

Each deployment distinguishes its owner or customer from cloud provider, vendor, implementation partner, technology, and organizations that are only mentioned or used as comparison examples. Internal provider deployments require explicit evidence.

4. Claim provenance

Important status and outcome claims retain the source reference, quoted supporting text, deployment ID, subject entity, claim type, confidence, and evidence context. A metric is not attributed merely because it appears elsewhere in the source.

5. Outcome normalization

Numbers preserve their reported semantics: exact values, ranges, approximate values, up-to, at-least, more-than, less-than, multipliers, durations, currencies, and absolute counts remain distinct. A range is one claim, never two observations.

6. Actual versus intended outcomes

Claims are labeled actual, target, projected, planned, estimated, or unknown. Default outcome benchmarks include actual claims only. Targets and plans remain visible as qualified evidence but do not become reported outcomes.

7. Deployment maturity

Exploring, PoC, Pilot, Production, and Scaled Production require explicit source language. Unknown means maturity evidence was evaluated but no deployment-stage statement was found; it does not imply a failed deployment.

8. Evidence Strength

Evidence Strength is a deterministic assessment of observable signals including named customer identity, source quality, source linkage, technical detail, outcome support, maturity evidence, and recency. It is separate from Innovativeness.

9. Data-quality validation

The pipeline flags missing source references, ambiguous ownership, provider-customer confusion, multiple deployments, metric-owner mismatches, split ranges, duplicate claims, target leakage, and unsupported maturity. High-severity findings require review.

10. Aggregate analytics

Every aggregate states its eligible denominator. Metrics are deduplicated by claim ID. Range claims retain their range in the UI and may use one disclosed midpoint analysis value; bounded claims are not silently treated as exact observations.

What innovativeness means

Innovativeness is a practical 1 to 5 assessment of how differentiated a deployment appears from the public evidence. It is not a judgment that every organization should copy the case, and it is not proof of commercial impact.

Foundational

Useful but common adoption patterns, such as basic automation or early assistant use.

Incremental

Clear operational improvement, usually extending a familiar workflow or known AI pattern.

Differentiated

A more specific implementation with visible business context, domain adaptation, or workflow integration.

Advanced

Strong evidence of production maturity, complex orchestration, or measurable transformation.

Breakthrough

Rare cases that appear unusually ambitious, novel, or strategically important for the category.

What the ranking is not

The ranking is a research and discovery aid. It should not be read as a vendor endorsement, procurement recommendation, legal claim, or proof that a project delivered all stated benefits.

Public evidence coverage is not market share and is affected by provider publishing behavior, language, discoverability, and source coverage.
Reported outcomes are generally not independently audited and should be checked against the linked source for high-stakes decisions.
Absence of evidence is not evidence of absence; a deployment can exist without enough public material to classify its status or outcomes.
Deployment status can remain Unknown when the source does not explicitly state a stage.
Older records can have less detailed public evidence and may be queued for newer normalization or evidence processing versions.
A high rank or Evidence Strength does not prove financial return, customer satisfaction, model quality, or long-term production success.

Ranking principles

These principles keep the ranking useful for people comparing real AI adoption patterns across industries and providers.

Evidence over claims

Pages should point back to public source material whenever possible. Thin or unclear claims should be treated as weaker evidence.

Real-world specificity

Concrete customers, partners, industries, locations, technologies, and deployment context make a case more useful than generic AI announcements.

Comparability

The ranking is designed to help readers compare cases across providers, industries, countries, companies, and AI capabilities.

Freshness with memory

Newer cases can matter because AI adoption changes quickly, but older high-quality examples remain valuable when they show durable patterns.

Methodology FAQ

How does AI Use Case Hub rank cases?

Deployments default to time-adjusted Innovativeness. Quantified outcomes can inform the assessment only when their attribution and claim status are suitable. Evidence Strength remains a separate guardrail for how much public support the record has.

What does innovativeness mean?

Innovativeness is a directional 1 to 5 assessment of how differentiated an AI deployment appears based on public evidence, business context, technical ambition, and the maturity of the implementation.

How are reported outcomes handled?

Reported outcomes stay tied to their source claim and deployment subject. Actual claims are eligible for default benchmarks; targets, projections, plans, and estimates are displayed separately. Ranges remain ranges in the UI and can contribute one clearly marked midpoint analysis value, never two endpoint observations.

What do Evidence Strength and Deployment Maturity mean?

Evidence Strength summarizes the available source support for a case. Deployment Maturity describes only the stage explicitly documented in the source; Unknown means the public material did not establish a stage, not that the deployment failed or did not exist.

Are the rankings an endorsement?

No. Rankings are a discovery aid, not an endorsement, audit, vendor comparison, or guarantee of business impact. Readers should verify important details with the linked sources.

Can a case be corrected?

Yes. The site welcomes corrections, source suggestions, and collaboration ideas through the contact options on the About page.