Tim Howard, Deputy Director for Major Projects, Cross-Cutting Technical Services, reflects on his experience of integrating generative AI into software development workflows.
It was great the see Government Digital Service recently launch the AI Playbook for the UK Government, further demonstrating the public sector’s appetite to harness the power of AI technologies safely, effectively and responsibly.
At Defra, I’ve been grappling with the impact on software delivery. Is artificial intelligence (AI) going to revolutionise software development, fundamentally changing how we write, test and deploy code? I wanted to explore how generative AI (GenAI) can enhance the software development lifecycle. While GenAI presents immense potential for accelerating coding, reducing manual effort and improving efficiency, it also introduces new challenges that must be managed carefully.
Given the rapid evolution of AI capabilities, establishing a structured framework that ensures GenAI is used responsibly and effectively is crucial. This blog post reflects on our experience with integrating GenAI into software development workflows and the need for a new paradigm in software engineering.
The genesis of the AI software development lifecycle (SDLC) playbook
To support this shift, Steven Dickinson, one of Defra’s superb principal developers, assembled a team – tremendous gratitude to Ryan Sikorsky, Todd Anderson, Ben Wilkes and Adam Fletcher – to build an application so we could learn first-hand how to integrate GenAI into software development workflows effectively. This hands-on approach allows us to understand AI-driven development's practical benefits and challenges, ensuring our methodologies remain grounded in real-world applications.
Traditional software development methodologies have been built around static requirements and predictable change cycles. However, AI-driven development introduces a dynamic and iterative process that requires ongoing monitoring, adaptation and refinement.
To support this shift and share our learnings, we developed the AI SDLC playbook, a guiding framework that:
- establishes best practices for incorporating AI into software development
- provides structured workflows for AI-powered coding and automation
- aligns AI-driven development with ethical and governance standards
As AI capabilities advance rapidly, we must rethink conventional software engineering practices. Without careful oversight, we risk deploying hard-to-maintain code that neglects the well-established best practices developed over time to ensure the delivery of quality enterprise software. Without a structured approach, we risk deploying AI-generated code that is inefficient, biased, or misaligned with best practices.
Leveraging generative AI in software development
We learned that to benefit from these tools truly, you have to know what you are doing when it comes to writing code. Senior developers with years of software development exposure are where these tools provide the most productivity benefits. While GenAI can generate code snippets and offer suggestions, understanding their correctness, efficiency and security requires deep expertise.
GenAI represents a significant transformation in our approach to coding. Unlike traditional AI, which focuses on prediction and automation, GenAI can generate code, suggest optimisations and even debug programs. The capabilities of agentic integrated development environments (IDEs) are changing daily, and we are seeing the proliferation of these tools and ideas in supporting platforms. However, adopting GenAI requires a measured approach.
Some of the key applications of GenAI in software development include:
- automated code generation – AI-powered tools like Cursor and Windsurf assist developers by generating code snippets and suggesting optimisations, improving productivity
- code review and debugging – AI-enhanced tools can analyse code for vulnerabilities, errors and inefficiencies, accelerating the debugging process
- test automation – AI can generate test cases, identify edge cases and improve software quality by automating unit and integration testing
- documentation and code explanation – AI can help generate documentation, summarise code functionality and make software more accessible for developers
However, these advancements come with challenges, such as:
- ensuring code quality – AI-generated code must be rigorously reviewed to maintain security, efficiency and readability
- bias and ethical considerations – AI models are trained on existing codebases, which may contain biases or outdated practices
- maintaining human oversight – while GenAI can assist in coding, final decision-making must remain with human developers to ensure correctness and adherence to best practices
Future directions
As GenAI becomes more integrated into software development, we must adapt our methodologies and governance frameworks. Key focus areas for the future include:
- defining AI-assisted development standards – establishing guidelines for AI-generated code quality, security and best practices
- enhancing AI governance – ensuring AI-powered coding tools align with organisational policies and ethical considerations
- encouraging responsible AI adoption – educating developers on the strengths and limitations of AI-assisted coding to avoid over-reliance on automation
Conclusion
GenAI is reshaping software development, introducing new efficiencies while requiring careful governance. While the benefits of AI-assisted coding are significant, a responsible and structured approach is essential to ensure quality, security and ethical use.
As we move forward in this new paradigm, adopting AI to enhance, rather than replace, human expertise will be key. By thoughtfully integrating GenAI into software development workflows, we can unlock new levels of innovation and productivity while maintaining the highest standards of reliability and governance. We are creating and sharing what we have learned under the Defra AI SDLC Playbook. Let’s embrace this new paradigm with responsibility.
Tim Howard is Deputy Director for Major Projects, Cross-Cutting Technical Services at Defra. Follow Tim on LinkedIn.
1 comment
Comment by Becky Miller posted on
Interesting read, thanks - are you also monitoring the carbon emissions for using AI at these different points in the workflow? This feels like an important ethical consideration, particular for Defra given our legally binding emissions targets.