This study develops an integrated generative artificial intelligence (GAI) framework for improving business process performance in industrial enterprises. The framework treats GAI not as the isolated use of generative tools, but as a governable information systems capability embedded in recurring workflows, enterprise architectures, documented knowledge, and human decision roles. It integrates four functional subframeworks—manufacturing, marketing and sales, accounting and finance, and human resource management—with a shared orchestration and governance layer. This layer coordinates process architecture, approved data and knowledge sources, reusable GAI capabilities, human-in-the-loop validation, traceability, escalation, and performance measurement. A proof-of-concept maturity-readiness validation is conducted in an electronics company using maturity-readiness logic inspired by the Smart Industry Readiness Index (SIRI). The assessment shows an increase in the overall readiness score from 41.60 in the pre-GAI baseline to 79.08 in the post-GAI implementation scenario. Accordingly, the score increase is interpreted as expert-assessed maturity-readiness evidence rather than as a measured causal effect on operational performance. This study contributes a process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption.
Generative artificial intelligence (GAI) is expanding from model-centered research into engineering and manufacturing activities, but its scope and maturity remain uneven. This PRISMA-guided bibliometric and abstract-level thematic review maps peer-reviewed industrial GAI research published from 2022 to 4 June 2026. Searches of Scopus, Web of Science, and the ACM Digital Library identified 492 records; 119 duplicates and 121 ineligible records were removed, leaving 252 studies. Keyword normalization, co-occurrence analysis, dominant and secondary thematic coding, and an abstract-reported evidence characterization were applied. The corpus shows two connected trajectories: engineering generation based on generative models for design, topology, materials, and electronics, and knowledge-intensive industrial intelligence based on large language models, retrieval-augmented generation, knowledge graphs, agents, and human–AI collaboration. Most studies report empirical or computational evaluation (72.2%), but 84.5% remain research-stage; only 0.8% indicate operational industrial evidence in their abstracts. The findings, therefore, distinguish publication activity from deployment maturity. Priority requirements for adoption include domain-grounded data, verification, manufacturability checks, traceability, cybersecurity, intellectual property protection, system integration, workforce preparation, and human accountability. This review contributes a reproducible cross-domain map, an overlap-aware synthesis, and stakeholder-specific guidance for trustworthy industrial GAI.