AI-assisted code generation promises speed — but without clear architectural boundaries, the result is risk, not results. Generated code isn't inherently secure, maintainable, or testable. What matters is the framework it's produced in: precise architectural constraints limit what's possible so that entire classes of errors are structurally eliminated. How this works in practice — and why A12 acts as a safety net.

Agentic coding promises drastically faster software development—but without adapting the Software Development Lifecycle (SDLC), organizations risk quality degradation and integration chaos. Why the V-Model is making a surprising comeback in the age of agentic development, and how quality gates, traceability, and a new understanding of engineering make all the difference.

All companies are currently working on AI pilots, have defined use cases, are deploying tools, and some are already incorporating AI into their workflows or working with AI agents. The expectation is clear: to become faster, work more efficiently, and solve current problems. But in reality, when AI actually works, a pattern emerges that many did not anticipate. It is no longer just about efficiency. Our thesis: AI does not automatically accelerate work, but rather ruthlessly exposes where the organization is unclear.

AI tools promise fast test automation — but without a solid test strategy, clean architecture, and strong engineering expertise, they lead to unstable tests, rising maintenance costs, and uncontrolled sprawl. This article explains why AI is an amplifier, not a substitute for a methodical approach and sound structural design.

IBM stock crashed 13 percent in a single day, triggered by a single blog post: Anthropic can use Claude Code to automatically modernize COBOL legacy systems, something that previously took years and armies of consultants. The market understood what that means. But have the companies sitting on these systems understood it too?

Code is cheap. Software isn’t. In a world where AI can generate code in minutes, the real challenge isn’t typing faster—it’s building systems that are reliable, secure, and maintainable for years. Discover why enterprise experience is the new currency of software. A comment by Jan Jikeli, Head of AI at mgm technology partners.

Companies and institutions face the challenge of rapidly integrating the capabilities of modern AI into products while ensuring data privacy, compliance, and clear accountability. Three operational approaches—Public API, Cloud in your own tenant, and On-Premise—offer different advantages and disadvantages. This article outlines what they are best suited for, the limitations to consider, and how they can be combined into a robust hybrid architecture through an API gateway and agent orchestration.

Artificial intelligence is evolving at a rapid pace. New models, tools, and platforms emerge every month. This leaves companies facing a key question: Which innovations are worth adopting for real-world use—and which ones aren’t? That’s exactly where the mgm Enterprise AI Radar 2025 comes in.

Many companies are starting to use AI – but most prototypes remain stuck in the pilot stage. The reason for this is rarely the technology itself, but rather a lack of strategy, organisation and culture. We show in a practical way which steps are now crucial for AI to evolve from an individual project into a sustainable value driver.

They optimise internal processes and enable new, data-based business models. At the same time, legal, ethical and practical challenges arise: How can AI be...