Building a future-ready development team for the AI Age
Artificial intelligence is rapidly changing how software is built. From writing simple functions to automating tests and debugging, AI is increasing the pace and precision of development work. AI is becoming a powerful assistant that can help teams build smarter and faster - augmenting developers rather than replacing them.
Modular functions: speed through composition
One of the most promising uses of AI in coding is the generation of small, modular functions. These bite-sized blocks of logic can be reviewed, tested, and assembled into larger systems. Rather than building the entire solution, AI provides developers with high quality building blocks, like well-shaped bricks, that speed up construction but still require human hands to create the full structure.
This modular approach accelerates development without sacrificing control. It gives developers a flexible toolkit that reduces the overhead of writing boilerplate code while preserving architectural oversight.
When AI is used in this way - as a builder of well-defined, discrete components - it becomes easier to manage code quality and reduce the risk of introducing hard-to-trace bugs. The developer remains the architect, ensuring the final product meets performance, security and design requirements.
Debugging, testing and documentation
AI’s pattern recognition abilities make it a handy tool for debugging. It can identify syntax issues, propose optimisations and even highlight logic flaws that might be overlooked. It helps trace runtime problems and can offer context-specific fixes that speed up resolution.
AI can also automate test generation. By analysing code paths and predicting inputs, it improves test coverage and helps catch issues earlier in development. This improves reliability and reduces the burden of manual testing.
Documentation is another area AI can assist. It can explain code behaviour, summarise changes, and improve project visibility. This supports maintainability, collaboration and onboarding. These forms of automation allow developers to focus on design and architecture while helping teams move faster without compromising quality.
Challenges: control, clarity and accountability
Despite its strengths, integrating AI into coding workflows brings important challenges. One of the biggest is explainability. Developers must still understand the code, how it works, why it was written that way and how to adapt it in the future. Relying on AI for complex logic can lead to code that is harder to maintain or audit.
AI may also miss edge cases. While it handles standard code well, it may not anticipate unusual inputs or subtle conditions that a human would catch. Without careful review, these gaps can result in hard-to-detect bugs or security vulnerabilities. Another concern is design ownership. Letting AI make decisions about how features are implemented can lead to inconsistency and architectural drift. Developers need to stay in control of design direction and uphold coding standards to ensure long-term quality and cohesion.
How can developers prepare for the AI Age?
To make the most of AI, developers must go beyond simply knowing how to use tools. They need to assess AI-generated code critically, understand its implications, and know when human input is essential. Recognising the limits of AI is just as important as knowing how to apply it effectively.
Understanding the reasoning behind the code remains a core responsibility. Without that insight, developers risk inheriting systems they can’t explain, maintain, or evolve. Skills like refining AI-generated documentation, spotting missed edge cases and guiding design choices will continue to be essential.
The developers who succeed in an AI-assisted future won’t step away from the craft. They’ll stay close to the work - reviewing, shaping and maintaining code - to make sure software is not just delivered quickly, but is built to last.
techUK - Seizing the AI Opportunity
The UK is a global leader in AI innovation, development and adoption.
The economic growth and productivity gain that AI can unlock is vast, but to fully harness this transformative opportunity, immediate action is required. Our aim is to ensure the UK seizes the opportunities presented by AI technology and continues to be a world leader in AI development.
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Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
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Kai Widdeson
Kai is a full stack developer. Formerly with Barclays Bank, Kai works across many of the Blue Hat projects implementing data driven solutions, AI proof of concepts, and embedded analytics with UK clients