AI productivity gains for software engineers.
Following on from my recent guest blog on “Building a future-ready development team for the AI Age” for techUK’s AI Adoption Hub, I’ve put together this 3-part blog series sharing useful insights on how how scaling businesses can adopt artificial intelligence responsibly to improve productivity while avoid some common pitfalls.
Part 1: Improving productivity
How AI can help speed up development: a practical guide for software engineers.
Part 2: Responsible AI Adoption
A consultant’s guide to AI adoption: insights on governance and risk management.
Part 3: Avoiding the pitfalls
Building AI-powered products and features: what founders need to know.
As a full stack developer, I work across many Blue Hat client projects implementing data-driven solutions, AI proof of concepts and embedded analytics. The insights in this blog series are collated from undertaking fast-moving, real-world projects. So, let’s kick off with Part 1.
How AI can help speed up development: a practical guide for software engineers.
If you're a developer, you've probably spent more time than you'd like rewriting the same utility functions or digging through logs to fix bugs that shouldn't have taken hours. These repetitive tasks eat away at your time and energy, especially when deadlines are tight.
Fortunately, AI tools like GitHub Copilot and ChatGPT are stepping in to help. They're changing how developers work by automating common tasks, speeding up routine parts of the job, and helping us stay focused on solving real problems.
In this article, we’ll dive into practical ways AI is making everyday development faster and less tedious. Whether you’re a solo dev, part of a large engineering team, or leading one, this guide will show you how AI can slot into your workflow and free up time for the work that really matters.
The productivity challenge in software development
Ask any developer how they spend their day, and you'll hear a familiar story: only a fraction of their time is spent writing new, valuable code. According to SonarSource, developers spend just 32% of their time on writing new or improving existing code, with the rest going to maintenance, testing, security, meetings, and operational tasks.
Some of the biggest time drains include:
Rewriting boilerplate code
Fixing bugs and tracing errors
Writing and updating tests
Documenting code changes
Reviewing peers' code
All of this slows down releases, adds to technical debt, and can leave teams feeling burnt out. The inefficiencies aren’t just annoying, they cost real time and money. This is where AI comes in. By automating or accelerating many of these tasks, AI tools give developers more space to think creatively and build better software.
The AI-powered development tools landscape
The ecosystem of AI development tools has grown rapidly in recent years. Here’s a look at some of the most widely used tools today:
GitHub Copilot: Acts as an autocomplete on steroids, suggesting whole lines or blocks of code based on natural language comments or code context.
ChatGPT: Used for code generation, debugging, learning new APIs, and even writing documentation.
Cursor: A full-featured AI-powered code editor built on top of VS Code, designed to integrate AI into your workflow more deeply. Cursor doesn’t just assist, it actively collaborates, offering refactors, explanations, and in-editor help like a pair programmer
Tabnine: Focuses on context-aware code completions powered by local or cloud-based AI models.
Amazon CodeWhisperer, Windsurf (formerly Codeium), Cody: Emerging tools offering similar capabilities tailored for specific ecosystems or integration models.
These tools typically fall into the following categories:
Test and QA tools automate test generation and improve test coverage. Examples include Diffblue Cover and Testim.
While no tool is a silver bullet, their combined power can dramatically enhance a developer’s productivity when used wisely.
Five key areas where AI boosts developer productivity
#1 Code generation and boilerplate reduction
AI is great at filling in the blanks. If you're writing boilerplate code, like a new REST endpoint or database model, AI tools can often write the skeleton for you.
Example: Type a comment like "Create a user login API" and Copilot can generate most of the function, complete with parameters and error handling.
Tips:
Write clear, concise comments to guide the AI
Keep your functions small and well-named
Always double-check the output. AI code can look right but have subtle bugs
#2 Debugging and error resolution
We’ve all stared at cryptic stack traces, trying to make sense of what went wrong. AI can help make debugging faster and a lot less painful.
Example: Drop an error message into ChatGPT, and it’ll often tell you exactly what the problem is and how to fix it.
Tips:
Use AI alongside your logs and monitoring tools and ask "what could cause this error?"
Pre-emptively ask AI to analyse some code and look for edge cases which might cause problems.
#3 Test generation and coverage
Writing tests can be a grind, but skipping them isn't an option. AI helps bridge the gap by generating tests from your functions.
Example: Give ChatGPT a function, and it can spit out a handful of unit tests with common input cases and assertions.
Tips:
Start with generated tests, then tweak them to fit your app’s logic
Be clear about expected inputs and edge cases when prompting
#4 Documentation creation and maintenance
Keeping docs up to date is one of those tasks everyone avoids. AI makes it easier to stay on top of it.
Example: Mintlify or ChatGPT can generate docstrings or markdown docs based on your code and comments.
Tips:
Use AI to draft docs, but do a human pass for clarity and accuracy
Hook doc tools into your CI/CD to keep things in sync automatically
#5 Code review and quality improvement
AI can help spot issues before your teammates do, saving time in reviews and improving code quality.
Example: Paste a code snippet into ChatGPT and ask for a review. It'll point out logic issues, suggest naming improvements, or flag potential bugs.
Tips:
Use AI for early feedback, not as a replacement for human reviews
Let it handle style and syntax issues so reviewers can focus on architecture
Integration into development workflows
To really benefit from AI, you need to weave it into your existing tools and processes. The most popular AI tools now offer IDE plugins (for VS Code, IntelliJ, etc) so you get help right where you code.
Teams can also use bots in Slack or GitHub to offer AI-generated suggestions on pull requests or answer technical questions on demand.
Some things to keep in mind:
Share prompting tips and common use cases with your team
Be careful with sensitive code, some tools send data to cloud services
Track how much time AI tools save to help justify broader adoption
AI works best when it becomes part of the workflow, not a separate tool you have to remember to use.
What to do next
AI is already making life easier for developers. From cutting down boilerplate to simplifying debugging and documentation, these tools are becoming must-haves for modern dev teams.
In Part 2, we’ll look at the flip side: How to adopt AI responsibly, covering security, ethics, and long-term impact on team workflows.
Until then, I’ll leave you with an exercise (and a free download below) to try out:
Pick one AI tool, e.g. Microsoft Copilot or Cursor, and use it for a week in your regular workflow.
See where it saves you the most time, and consider how you might roll it out across your team.
How Can Blue Hat Help?
We’re an experienced collective of senior technology leaders with a mission to help scaling SaaS businesses achieve their technology and product goals faster and more cost-effectively. We work closely with our clients to effectively bolster their leadership and development teams to tackle their most pressing technology and product problems.
Thanks for reading. If you’re not already, please follow us on LinkedIn to stay tuned for insights on data and artificial intelligence. Feel free to get in touch to arrange some time to talk with one of our Partners.
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