The new requirements extend across the full software lifecycle. Design, deployment, and ongoing support for covered AI systems must now meet defined standards for transparency, security, and human oversight. Providers of generative AI tools, interactive AI systems, and deepfake-adjacent applications are directly in scope.
Neil Fowler, SVP of software engineering and hybrid cloud at Rocket Software, argued the moment should be read as a structural opportunity as much as a legal threshold.
For organisations operating within complex and critical IT infrastructures, this step represents not a mere legal constraint, but an opportunity to reaffirm trust at the heart of technological transformation.
Neil Fowler, SVP of software engineering and hybrid cloud at Rocket Software
In practical terms, Fowler pointed to customer data anonymisation and the traceability of data flows — whether for AI training or live analysis — as the governance foundations organisations now need in place. The requirement for structured oversight at every stage of the AI lifecycle translates to concrete operational work: audit trails, defined governance policies, and documented human oversight at key decision points.
From software design to customer support, establishing structured governance is essential to setting clear, consistent, and above all, transparent boundaries. By anticipating these compliance as well as operational risks, organisations accelerate their pace of innovation while maintaining the stability and security that users demand.
Neil Fowler, SVP of software engineering and hybrid cloud at Rocket Software
Rocket Software provides mainframe modernisation and hybrid cloud tooling, meaning its customers frequently run AI applications layered over decades-old infrastructure — the environment where the Act's transparency requirements are hardest to implement without significant rearchitecting. Fowler's framing positions governance investment now as the foundation for faster AI deployment later, rather than a brake on it.
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