AI pattern
AI that writes, reviews, tests or migrates software code.
25 organizations in 7 countries run these use cases in public. 70% of those 27 deployments disclose a measurable outcome.
An AI assistant on a developer portal and in its documentation that answers integration questions, recommends the right endpoints, helps debug connections and generates sample calls, grounded in the API catalogue, reference docs and test material, so clients and partners integrate faster with fewer support tickets.
An AI assistant in the developer's IDE and code review flow that completes and generates code, explains unfamiliar modules, drafts unit tests and reviews pull requests for common defects, while generated code goes through the same review, testing and change controls as any other code.
AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.
AI that reads legacy code such as COBOL, PL/I or old Java, explains what each program does, maps its data flows and dependencies, drafts the equivalent modern code or specification, and generates the regression tests needed to prove the new system behaves like the old one.
AI that takes security findings from scanners, fuzzers and bug reports, filters out duplicates and false positives, reproduces and ranks the real ones, and drafts a code fix with a test for each, which a developer reviews and merges through the normal change process.
An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.
Computed from the 27 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Productivity gain | 20% | 8.7% to 42.4% | 3 | 2 organization, 1 vendor |
ANZ, Accelerant Holdings, Accenture, Airbnb, Amazon, Bank of America, Bayer, CME Group, CircleCI, Citi, Europ Assistance, Generali France, Google, Kinsale Capital Group, Labelbox, LinkedIn, MAIF, Mapbox, Meta, Morgan Stanley, PatientPoint, Toyota Motor Europe, U.S. Bank, Uber Technologies, monday.com.
