You’ve read the headlines, seen the demos, and maybe even experimented with AI in a sandbox. But turning that curiosity into real delivery gains without sacrificing control? That’s the part that keeps many leaders stuck on the fence. Here’s the good news: it’s possible to accelerate software development with AI - safely, strategically, and without falling into the trap of overpromising and underdelivering. But it takes more than just tools. You need clarity, caution, and a clear starting point.
A QUICK SUMMARY – FOR THE BUSY ONES
Keep reading to see how top engineering leaders are using AI to reclaim hours per sprint, slash debugging time by 40%, and still keep technical debt under control.
TABLE OF CONTENTS
If the concept of AI makes you feel excited and paralyzed with fear at the same time and you’ve been on the fence for months, it’s high time to make up your mind. How to use AI in software development properly? You may still not know it exactly and this can affect your business decisions greatly. The fervor around AI can be a bit overwhelming but the truth is that Artificial Intelligence can be a very efficient means to accelerate software delivery, provided all the risks and loose ends are managed. And here’s how to do it.
In this article, you’ll find some actionable insights, plug-and-play recommendations, and expert advice on how to use AI in software development right and make the most of it. What’s more, you’ll get some priceless, first-hand recommendations, warnings, and insights from senior engineers, founders, and CTOs who’ve tested AI first, in real-world environments.
As a tech person, you’ve had so many things to think about lately. You feel that you are under real, escalating pressure to deliver more value faster and with fewer resources. On the other hand, you know that you can never sacrifice quality or strategic control. You are aware that as companies scale, their systems become more complex, and traditional processes can bottleneck delivery. And Artificial Intelligence allows for intelligent scaling without multiplying overhead.
You can wonder endlessly what will cost you more organization- and money-wise – trying AI or not – and those considerations may truly drag you down.
The best you can do is ignore the hype surrounding AI and focus on the facts.
And the truth is that AI offers a way to streamline delivery without sacrificing quality but only if wielded wisely.
Jumping on the AI bandwagon hastily can be tricky and dangerous. AI in software development is a powerful tool, but you won’t go far without thorough preparations, proper resources, and learning from the mistakes (and successes!) of others. Some of the grave mistakes to avoid include skipping the foundation work, lack of proper governance structures for AI initiatives, and treating AI implementation as a solely technical challenge, with human factor (or people’s resistance to change) ignored.
Of course, you may hesitate due to many other reasons. One of them is that AI promises faster delivery but you must ensure that this speed doesn’t come at the cost of poorly structured code or security vulnerabilities. The thing is that, although expectations are high, AI is not an almighty magic wand. Using it requires a lot of work and knowledge and is a very demanding endeavor.
However, hard work pays off and the results can be truly spectacular. AI can accelerate software development greatly while balancing quality, security, architecture integrity, and team control.
Some of the benefits of speeding up software delivery with AI include:
In brief, AI can speed up everything from image and content generation to code generation, testing, debugging, and code reviews.
There are countless aspects and areas where AI delivers a true speed acceleration in the software delivery cycle. Some of them include:
"Daily standups used to eat up valuable dev time – until one team brought in an AI dev agent to summarize git activity, commits, and ticket progress. The result? Standups became asynchronous and wrapped in under five minutes. That shift reclaimed 3-4 hours per sprint for actual coding, and it had a quiet bonus: less context-switching meant engineers stayed in flow longer. Sometimes, the biggest speed wins come from trimming the small daily frictions." - Jeffrey Zhou, CEO & Founder, Fig Loans
AI changes the delivery curve for the better, e.g. by monitoring system performance and helping predict possible delays. Some of the other areas that may gain a lot thanks to AI are onboarding junior developers, test coverage, cycle time, and code review throughput.
And here are some numbers to prove it:
AI has been most impactful in code generation and debugging. Our team has seen up to 70% time savings on routine coding tasks, particularly in areas like boilerplate code generation and API integration. Specifically, we've achieved:
- 3x faster development of basic CRUD operations
- 65% reduction in time spent on unit test writing
- 40% decrease in initial bug detection time.
To maintain quality, we've implemented a hybrid approach:
- All AI-generated code must pass through human review,
- Critical security and core business logic remain primarily human-driven,
- We use AI as a suggestion engine rather than accepting its output wholesale. - Fei Chen, Founder & CEO, Intellectia.Ai
AI can speed up software delivery greatly, but there are some inevitable costs to consider before you go for it. The major hidden cost of speed is technical debt, as AI writes syntactically correct code that passes tests but doesn’t scale.
On top of that, AI lacks system-wide awareness which may result in misaligned implementations. Inefficient query logic works but causes scaling issues. Also, AI-generated logic can skip default safety mechanisms in some cases.
"One of the smartest moves I've seen from a CTO aiming to boost delivery speed with AI was setting up "human-gated context switching." In this setup, AI was allowed to assist with complex logic changes only when a developer enabled a special context-aware review mode in the IDE. It wasn't about slowing down AI – it was about making sure the system understood when stakes were high. That single policy reduced silent technical debt by 68% compared to teams using AI without guardrails. If you're starting tomorrow, focus less on what AI "can" do and more on when it "should" be double-checked. Speed matters – but speed with blind spots is just faster failure." - Grant Aldrich, CEO, Preppy
There are also other possible issues to watch out for to avoid failure of your AI project. Some of them include:
The high failure rate of AI initiatives only calls for using high-class advisors’ expertise, high-quality underlying data, the right technological skills, meticulous planning, and top-notch AI development tools.
AI is not omnipotent nor all-knowing and you should never give it too much power. If you don’t want to lose control, you’d better treat AI like a junior developer, with mandatory human reviews carried out regularly and teams trained on tool strengths, blind spots, and when to override. You should ensure that sufficient infrastructure and talent are in place, too, and only use AI in projects where it can truly bring something significant to the table.
How to use AI in software development to start with a bang? Well, you should focus primarily on documentation and test generation – with easy entry points and immediate ROI – and internal tooling like dashboards. Also, it’s good to ensure safe environments to test AI workflows and perform code reviews with AI augmentation, combined with static analysis for quality and productivity boosts. You need to pay attention to cloud cost and architecture optimization – and look for efficiencies before tackling core features.
For CTOs looking to start tomorrow, I'd recommend beginning with documentation and test generation. These areas offer quick wins with minimal risk. We saw immediate productivity gains when we introduced AI for API documentation, cutting documentation time by 60%. A practical first step would be to pilot AI tools in a non-critical project, focusing on:
- Automated test generation,
- Code documentation,
- Basic feature implementation.
I've seen too many teams rush to implement AI everywhere. Start small, measure results, and scale what works." - Fei Chen, Founder & CEO, Intellectia.Ai
"The biggest technical debt risk I've seen isn't from the AI itself but from skipping proper security validation. Organizations rush cloud migrations to capture AI benefits without adequately configuring security controls, creating significant vulnerabilities. We recommend maintaining human oversight specifically for edge case security scenarios. For CTOs looking to implement AI tomorrow, focus first on consolidating your technology providers. The complexity of managing multiple vendors with disconnected AI strategies creates more problems than solutions. Start by mapping your current providers, identifying opportunities to consolidate, then implement AI solutions through fewer trusted partners with proven integration frameworks." - Ryan Carter, CEO/Founder, NetSharx
AI is now at the very center of leadership discussions and there are good reasons for that. There’s a lot to gain, especially if you are at the forefront of AI-related revolution. If you’re still wondering how to use AI in software development and when to do it, the time is now.
AI can accelerate software development by streamlining workflows, enhancing decision-making, speeding up the testing and debugging phases, identifying issues early, and letting human brains focus on more complex tasks. It’s vital not to treat AI as a replacement but rather as an enhancement of your current staff.
AI implementation is about shifting team focus to what matters most. It requires creativity, time, money, and full attention, but the results can be very impressive. And you can expect to get much more time, money, and freed attention in return.
Want to accelerate the software development process with AI safely but you think it’s too overwhelming? If you’d like to learn from seasoned industry experts what AI in software development has in store for you, contact Brainhub today.
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Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.
Software development enthusiast with 7 years of professional experience in the tech industry. Experienced in outsourcing market analysis, with a special focus on nearshoring. In the meantime, our expert in explaining tech, business, and digital topics in an accessible way. Writer and translator after hours.
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