5 ways you can maximize AI's big impact in software development (www.zdnet.com)

Leading financial and payments engineering teams shared five practical ways to amplify AI’s impact on software development: codify policies with engines like Open Policy Agent (OPA) to nudge developers (not block them), modernize platforms and communications to scale AI adoption (Lloyds’ Platform 3.0), embed automation and guardrails (automated testing, security scanning, code coverage) to allow safe innovation, provide continuous feedback and governance as devs shift toward agentic AI (using GitHub, Copilot and careful vetting of generated code), and train non-dev teams (security, audit) to “fight fire with fire” by using AI tooling themselves. Firms emphasized cultural work—platform engineering, visible experiments, and learning loops—so developers keep autonomy while meeting regulatory and security requirements. For the AI/ML community this is a playbook for operationalizing models in regulated, large-scale environments: use policy-as-code (OPA) to enforce compliance across the stack, instrument CI/CD with automated security and quality checks, treat code-generation agents as productivity multipliers that require governance, and invest in cross-functional AI literacy. Technically, the implications include stronger platform-level integration of policy engines, expanded use of code-assist models (and warnings about “vibe coding” by juniors), and a focus on pipeline security for rapidly produced code—shifting risk management from manual audits to continuous, automated enforcement.