Digital Transformation
Equal Experts
Full Credential Description
Challenge Balancing the pressure to adopt AI with scalable and responsible engineering Organisations across industries are under increasing pressure to adopt GenAI coding tools to gain efficiency and competitive advantage. The promise of faster software development, automation, and reduced manual effort is compelling, but balancing speed with responsibility and scalability remains a major challenge. Key challenges faced by organisations include: Pressure to deliver faster – Leadership expects rapid AI-driven productivity gains, but without disciplined engineering and AI coding best practices, short-term speed can lead to far worse technical debt due to the AI not checking the code and solving the same problem in multiple ways, leading to an unmaintainable mess in a short space of time. Unstructured AI adoption – In the absence of a shared strategy, teams often experiment inconsistently with AI tools, creating uneven or risky implementations. AI-Generated code quality – While AI can generate code quickly, ensuring security, performance, and maintainability still requires a small pool of senior engineering experts who know what good looks like. Ethical & compliance risks – As AI models reflect biases in their training data, organisations face increasing pressure to address fairness, security, and IP concerns through clear governance. Integration with existing development workflows – For AI to add real value, it has to align with existing CI/CD pipelines and best practices Recog...