Jakub Chamula of NexIT Solutions on how AI is transforming the Software Development landscape

Transforming Development Projects with AI

Jakub Chamula explains that his agency’s most significant AI initiative is its partnership with Rappit. By leveraging AI-led application generation on this platform, the team can increase developer productivity by up to four times. Instead of manually coding every component, a large portion of an application is generated automatically. This approach not only speeds up development but also reduces technical debt, since components can be regenerated at any time to keep the application modern and maintainable.

Chamula highlights the important role AI tools play in productivity: "We use AI tools to increase productivity and achieve better, more consistent results."In customer-facing projects, AI tools play a crucial role in managing changing requirements, maintaining dependencies, generating project scopes and statements of work, and highlighting areas that need clarification. By automating these repetitive and time-consuming tasks, the team frees up capacity to deliver real business value more efficiently than traditional development methods.

Chamula emphasizes that developer effectiveness is critical. The agency’s approach prioritizes building solutions well first and then delivering them fast. Through the integration of AI in various stages of the development lifecycle, they report a 10% improvement in code quality and a 15% faster delivery time.

AI also enhances specific workflows:

• Testing: AI integration allows for extensive automated testing, catching bugs early while keeping costs manageable. This is particularly important for critical systems where regressions are unacceptable.

• Prototyping: AI accelerates prototype creation, shortening sales cycles and enabling the team to invest more time in delivering results.

• Modernization: Legacy systems often lack documentation. AI can analyze screenshots, documents, and meeting transcripts to provide developers with a clearer understanding of what needs to be done, improving planning, cost predictability, and risk management.

Through these applications, Chamula’s agency (nexit.solutions) demonstrates how AI can be a strategic development partner, increasing speed, consistency, and overall quality while allowing developers to focus on high-value, innovative work.

AI-Driven Improvements in Code Review and Testing

Jakub Chamula highlights that code review and testing have benefited the most from AI integration in their development process. Instead of replacing developers, AI is used as an additional layer of support, enhancing the team’s existing workflows.

"We believe this is because instead of replacing developers, we integrated AI as an additional step in the process."

Testing, in particular, is traditionally time-consuming and costly, especially when maintaining extensive test suites for critical applications. By leveraging AI, the agency can automate aspects of test maintenance and even conduct tasks like visual regression testing autonomously. This allows developers to focus on higher-value work while ensuring the application remains stable and reliable.

AI has also improved code quality. Previously, code would undergo a single review cycle, but with AI support, multiple iterations are now possible. In practice, this means what might have been just a second pass is now five or more, with the code refined at each stage. The result is fewer bugs, greater maintainability, and easier extensibility. While this iterative approach requires slightly more time upfront to address all raised points, the long-term savings in maintenance, bug fixes, and new feature development are significant.

Chamula emphasizes that AI is not yet capable of independent development. AI Developers still need a comprehensive understanding of the codebase, dependencies, and possible approaches. AI assists by automating mundane or non-complex tasks and analyzing dependencies, freeing developers to focus on strategic problem-solving and delivering real business value.

By integrating AI thoughtfully, the agency has transformed code review and testing from a labor-intensive bottleneck into an efficient, high-quality process that supports faster, more reliable development.

Overcoming Challenges in AI Adoption

Jakub Chamula emphasizes that the biggest challenge in adopting AI isn’t the technology itself - it’s how it’s implemented. In his experience, AI works best as an assistant. "AI should be an assistant that helps you with your work, not a replacement for a human employee." The key is designing processes so that developers maintain full oversight, continue learning, and approve all outputs, while still benefiting from AI’s speed and efficiency.

Implementation isn’t one-size-fits-all. Each company’s workflow and existing processes shape how AI should be integrated. Chamula shares that his team had to try completely different approaches multiple times to find the right balance. There were even instances where AI was prohibited from certain tasks after initial setbacks. The takeaway was clear: the problem was never AI itself, but how it was applied.

A central principle Chamula highlights is that AI should adapt to existing processes, not the other way around. When implemented thoughtfully, AI becomes a powerful tool that enhances productivity, improves quality, and supports team learning, without compromising control or introducing unnecessary risks.

Key Advice for Businesses Considering AI

Jakub Chamula offers clear guidance for companies exploring AI-driven solutions, emphasizing that strategy and implementation matter more than the technology itself. He highlights three essential principles:

1. Adapt AI to existing processes, not the other way around. AI works best when it complements current workflows, rather than forcing teams to completely restructure how they operate.

2. Start with high-effort, low-risk processes. By focusing on areas where AI can make a meaningful impact without jeopardizing critical operations, businesses can gain confidence in the technology while seeing tangible results quickly.

3. "Always begin with people, not AI." Chamula stresses that AI should not be left to write requirements, develop software, manage entire projects, or make key decisions independently. Instead, it should support humans by improving specific steps - making tasks faster, better, and more cost-effective - while humans retain control and oversight.

Following these principles ensures that AI adoption is practical, sustainable, and aligned with business goals, allowing teams to leverage AI as a true productivity enhancer rather than a disruptive experiment.

Emerging AI Trends in Software Development

"I think the biggest trend will be building your own army of highly specialized AI agents."

Jakub Chamula predicts that one of the most significant trends in the next 3–5 years will be the creation of highly specialized AI agents. He envisions businesses building an “army” of AI tools, each tailored for specific tasks within development processes. While this currently requires considerable effort due to the early stage of AI process integration, Chamula believes that once the initial friction is overcome, these specialized agents will handle even the smallest steps in workflows without compromising quality.

Another trend he anticipates is the expansion of AI into more industries such as insurance, banking, and construction. Critical processes in these sectors - like risk assessment or visual analysis - currently lack reliable AI solutions. As AI technologies mature, these processes are likely to adopt AI more broadly, though regulatory considerations will play a major role. Chamula notes that highly regulated industries will see new rules emerging to ensure safe and responsible AI implementation.

Overall, he foresees a future where AI becomes ubiquitous in software development, not just for coding or testing, but as a suite of specialized agents supporting a wide range of tasks across multiple industries.

Preparing Teams for the AI-Driven Future

Jakub emphasizes that training and upskilling are essential as AI reshapes development workflows. For his team, understanding the correct use of AI is a top priority. "The correct use of AI is one of the most important steps during implementation, which is why we've designed our processes to make misuse more difficult." While the agency has designed processes to minimize misuse, human training remains crucial to ensure AI is applied effectively and responsibly.

Chamula points out that AI doesn’t reduce the need for decision-making - in fact, it often increases it. Developers must interpret AI outputs, choose the best approaches, and apply context-specific judgment. As a result, critical thinking and decision-making skills have become more important than ever.

In addition to these soft skills, training on how to use specific AI tools efficiently ensures that the team can maximize productivity without compromising quality. By focusing on both human judgment and technical proficiency, the agency prepares its developers to thrive in an AI-augmented development environment.

The Evolving Role of Human Developers in an AI-First World

Jakub believes that even in an AI-driven development landscape, human developers remain central. As AI takes over repetitive tasks and assists with routine problem-solving, developers’ roles are shifting toward finding optimal solutions, generating ideas, and making strategic decisions.

AI can support development by analyzing data, generating code, and highlighting patterns, but it cannot originate new ideas or invent novel approaches to solving problems. Many challenges have multiple possible solutions, and identifying the most effective one often requires human insight and creativity.

Chamula emphasizes that while AI can accelerate processes, it is the human developer who evaluates options, considers trade-offs, and ultimately determines the optimal solution for a specific problem.

"AI will help us with our work more and more, pushing more decisions to us as a result. AI cannot come up with new ideas on how to solve problems. There are many solutions to the problem. Some are more specific than the others. The optimal solution for specific ones may not have been found," notes Chamula.

In essence, AI enhances human capabilities, but creativity, judgment, and problem-solving remain firmly in the hands of skilled developers, making them indispensable in the AI-first development world.

How AI Is Shaping Client Expectations

"In my view, discussions about AI in development tend to fall into two categories: the overly optimistic and the pessimistic."

Jakub Chamula observes that AI is changing what clients expect from IT service providers, though often with misconceptions about its capabilities. He notes that conversations about AI tend to swing between overly optimistic promises and pessimistic doubts. The reality, he says, is that AI makes development faster, more cost-effective, and can even improve quality, but it is not a substitute for skilled developers.

"In my view, discussions about AI in development tend to fall into two categories: the overly optimistic and the pessimistic. The reality is that AI makes the development process more cost-effective, faster, and can even improve quality," notes Jaukb.

However, he notes that AI cannot replace human developers.

AI cannot make critical design decisions, generate truly novel ideas, or fully understand a business’s unique context. It also struggles with highly complex systems. Yet, when implemented thoughtfully, AI tools enhance workflows, allowing teams to deliver better results more efficiently.

As a result, Chamula predicts that clients’ expectations will evolve. They will increasingly demand faster delivery and more cost-effective solutions, assuming that providers leverage AI intelligently. Service providers who adopt AI effectively will be positioned to meet these expectations, balancing speed and efficiency with the human insight needed for high-quality, context-aware solutions.