Developers hand-coded complex business logic from scratch. Now, 2,000 engineers use AI to generate it directly from detailed design docs.
A global technology and industrial enterprise manages large-scale system development projects while migrating legacy environments from COBOL to modern languages like Java and Python.
While internal tools could generate basic skeleton code, developers still had to manually write complex business logic from scratch using detailed...
“Of the many generative AI use cases that we gather and share, streamlining system development is a major theme.”
Multinational technology and industrial solutions for social infrastructure.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Related implementations across industries and use cases
Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
Experts manually flipped through manuals to code. Technicians now use natural language to build and test programs 80% faster.
Fixing defects in massive C++ systems took weeks. AI agents now run autonomous compile-test-fix loops, resolving issues in hours.
Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
Experts manually flipped through manuals to code. Technicians now use natural language to build and test programs 80% faster.
Fragmented knowledge across 30 jurisdictions slowed work. Lawyers now use AI to draft and research, minimizing external counsel scope.
Data silos left field teams blind during outages. Now, scanning a QR code instantly delivers live diagnostics and AI-guided repair steps.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.
Developers hand-coded complex business logic from scratch. Now, 2,000 engineers use AI to generate it directly from detailed design docs.
A global technology and industrial enterprise manages large-scale system development projects while migrating legacy environments from COBOL to modern languages like Java and Python.
While internal tools could generate basic skeleton code, developers still had to manually write complex business logic from scratch using detailed...
“Of the many generative AI use cases that we gather and share, streamlining system development is a major theme.”
Multinational technology and industrial solutions for social infrastructure.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Related implementations across industries and use cases
Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
Experts manually flipped through manuals to code. Technicians now use natural language to build and test programs 80% faster.
Fixing defects in massive C++ systems took weeks. AI agents now run autonomous compile-test-fix loops, resolving issues in hours.
Adopting new languages inflated project costs. Now, developers use AI trained on internal code to write and debug unfamiliar languages.
Experts manually flipped through manuals to code. Technicians now use natural language to build and test programs 80% faster.
Fragmented knowledge across 30 jurisdictions slowed work. Lawyers now use AI to draft and research, minimizing external counsel scope.
Data silos left field teams blind during outages. Now, scanning a QR code instantly delivers live diagnostics and AI-guided repair steps.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Custom hardware bottlenecked imaging. A switch to software-defined GPUs now renders photorealistic 3D hearts in real time.