Generative AI is rapidly shifting from experimental to essential in modern software development. This panel will explore how organizations can strategically embed generative AI into the software engineering lifecycle — from design and coding to testing and maintenance — to drive productivity, code quality, and innovation. Experts will discuss practical steps, governance, and overcoming integration barriers.

• Identifying high-value entry points for generative AI within existing SDLC stages, including coding, documentation, and QA.
• Balancing AI-generated code with human oversight to ensure security, compliance, and maintainability.
• Establishing governance frameworks for responsible AI use across engineering teams.
• Managing cultural and workflow shifts: equipping developers to collaborate effectively with AI tools.
• Measuring ROI and success metrics when integrating generative AI into engineering processes.

Generative AI is evolving from passive insight generation to active process transformation. This session examines how organisations can harness support assistants, and developer tools to reimagine workflows—while managing risk, compliance, and without impeding on innovation.

• Deploy AI-powered assistants that go beyond knowledge retrieval to actively support decision-making and task execution
• Integrate agentic workflows into core business processes to enable autonomous, multi-step task automation
• Accelerate the software development lifecycle with Gen-AI tools for code generation, testing, and documentation
• Establish a culture of safe, continuous AI experimentation in regulated environments through sandboxing, auditability, and compliance alignment
• Evaluate open source and proprietary AI solutions to strike the right balance between customisation, speed, and control