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The genie is out of the bottle; AI is becoming a new normal, and it’s hard to resist its benefits. With AI models generating 90% of all internet traffic, 77% of devices embedding some form of AI, and two out of three people turning to ChatGPT instead of Google, it’s clear that AI’s growth is nothing short of inspiring.
By nature, people fear change. They get comfortable with their routines, workflows, and existing methods, often resisting new and better approaches.
However, in business, embracing AI is no longer optional, it’s the basics for keeping your business afloat.
Altamira keeps building out its expertise around AI, implements advanced technologies into internal processes, and helps our clients along the path to AI adoption.
On this occasion, Alex Budnik, Chief Technical Officer at Altamira, shared his insights regarding the importance of implementing AI in the software development lifecycle, as it is a common client relationship bugbear. Alex has 2 decades of experience in enterprise software development, 15 years of experience in system design, and more than 7 years using AI/ML in enterprise projects.
How AI-powered tools contribute to the software development process?
First things first, client requirements for software development have dramatically changed, especially when comparing them over a five-year period.
The bar has been raised, and our new reality demands that we deliver solutions faster, more cost-efficiently, and with higher quality than ever before.
This change in expectations is driven by the high-speed nature of modern business and technology. Clients are no longer willing to wait for lengthy development cycles, nor are they prepared to pay a premium for slow, inefficient processes.
The good news? Advanced technologies, especially AI-powered tools, offer the flexibility and speed needed to meet these growing demands.
As you know, tools powered by machine learning use natural language processing to understand written descriptions, offer code suggestions, or even complete sections of code.
It is fair to say that by using AI tools in software development lifecycle, you can accelerate development process and minimise human error.
For example, autocompletion and code synthesis powered by AI dramatically improve productivity by predicting the following lines of code or even offering full functions.
These tools continuously improve through machine learning and deep learning, leading to even better development workflows and project outcomes.
AI also improves debugging and testing beyond traditional methods. Advanced tools can automatically identify bugs, security vulnerabilities, and inefficiencies in addition to solutions for improvement.
AI-driven testing systems create dynamic test cases and prioritize the most important ones, helping us to boost software quality and security.
Long story short, at Altamira, we promote the usage of AI-powered tools during the software development cycles and can confidently highlight the aspects where AI is making a difference.
Code generation
One of AI’s most tangible benefits is its ability to drastically accelerate the coding process. Altamira proactively integrates AI-powered tools into our software development process.
Among many others, our go-to tools are GitHub Copilot and Autopilot, which proved their efficiency in reducing errors and speeding up the development.
Remember that AI-driven tools don’t replace software developers but instead serve as smart assistants, catching common mistakes, offering syntax corrections, and automating repetitive tasks.
The result? Faster development cycles, shorter time to market, and a team that feels empowered rather than overwhelmed. This really makes a difference in industries where being first—or at least fast—determines success.
Bug detection and fixing
Think about the time a developer spends debugging or troubleshooting code. Traditional debugging implies laboriously poring over lines of code, searching for errors, and testing fixes.
AI-powered tools immediately highlight potential issues in real time, predict where bugs might occur, and suggest solutions instantly.
It is not about saving time only, it is also about a new way our software developers approach problem-solving, so they can focus on higher-level tasks rather than getting bogged down in the details.
Testing automation
Testing has always been a cornerstone of quality software, but it’s also one of the most time-consuming phases. Manual testing, while thorough, is slow, and even automated testing frameworks require extensive setup and maintenance.
We rely on AI tools for testing and witnessed a drop in manual testing time by 40-50%. As a result, we automatically cover more scenarios than traditional manual testing. On top of it, we can prioritise critical tests, saving time and resources while improving our accuracy.
What does this mean for businesses? It means fewer post-release bugs and reduced costs associated with patching and updates.
Documentation
AI tools use NLP to generate and maintain documentation, making it easier to keep project information up to date. At Altamira, with the help of auto-documentation, we create documentation for APIs, libraries, and projects with no headaches.
Our software engineers work alongside AI, using their in-depth expertise to fine-tune AI-generated outputs.
Still, our expertise remains the top priority for guiding AI’s outputs and ensuring the technology enhances rather than disrupts the development process.
Refactoring and optimization
What’s more interesting is that AI tools help us improve code maintainability and performance by recommending refinements. We can easily spot any inefficient code practices and implement improvements.
We should admit that AI in software development is less about replacement and more about augmentation. We treat AI as a second set of eyes or a brainstorming partner that never gets tired, always has suggestions for improvement, and learns quickly from the best practices in the field.
DevOps and CI/CD pipelines
AI tools also help us automate tasks within CI/CD pipelines, improving efficiency and deployment speed. Eventually, we can easily detect and address performance issues in real-time, as well as manage tasks like load balancing and scaling automatically.
What are the examples of using AI in software development at Altamira?
Recently, one of our clients envisioned a versatile mobile solution that would give users immediate access to a wide range of educational resources.
The project called for an advanced mobile app capable of handling real-time data processing, offering AI-driven content recommendations, and supporting offline functionality. Moreover, the app needed to host a vast and diverse selection of educational materials across various subjects and skill levels.
A challenge was hidden in the request for compressed delivery times. So, it was decided to use AI tools to facilitate software development, resulting in each and every feature being delivered with the help of AI.
As a result, our team delivered a reliable, feature-packed solution on time and budget that supports both businesses and educational institutions in achieving their goals.
What are the risks related to AI in the software development process?
As you can see, AI offers endless benefits for the software development domain, but it also presents some risks related to security (data breaches, intellectual property risks, input manipulation, etc), biases, and sometimes quality that need careful management.
AI tools may unintentionally reflect or amplify biases if the training data is flawed. As a result, we can end up with unfair outcomes, especially in systems that make decisions or interact with users.
At Altamira, we consistently audit AI outputs for fairness and incorporate bias detection tools to identify and fix potential issues early. We believe that ensuring transparency in how AI makes decisions can also improve accountability.
Besides, we treat AI as a support tool, not a replacement for technical skills. Our software developers keep in practice manual coding, participate in regular skill-building workshops, and review AI outputs critically.
We ensure thorough human oversight in the code review process by:
- Conducting regular security audits and manual inspections of AI-generated code to catch vulnerabilities.
- Implementing automated security checks to complement human review, enhancing overall software security.
How can businesses start integrating AI?
If you are thinking about integrating AI into your development process or adopting AI for improving business outcomes, first it is reasonable to define what you want to achieve. Clarity and transparency in your goals will ensure AI serves a clear purpose.
Define clear objectives
Identify areas or problems within your business that AI could address and set measurable goals, such as reducing manual work, improving accuracy, increasing operational performance, etc.
Assess data readiness
Today, data is our most valuable asset. Evaluate the quality and quantity of your data, as AI thrives on clean and well-organised data. Ensure you have access to relevant datasets or consider ways to collect them.
Choose the right use cases
Start with simple applications like automating repetitive tasks, data analysis, or customer support. Focus on the use cases with clear ROI potential and minimal risk.
Understand the basics of AI tools
Research existing AI tools and platforms that align with your needs. Consider low-code or no-code tools for quicker adoption if your team has limited technical expertise.
Partner with experts
Consult with AI specialists who can help you cope with implementation challenges. Collaborate with partners who also offer scalable AI solutions tailored to your niche.
Run a pilot project
Begin with a small, manageable project to test the feasibility of AI within your operations. Monitor results and gather feedback to refine the implementation.
Upskill your team
Provide basic AI training to your staff so they can work effectively alongside AI systems and encourage cross-functional teams to collaborate on AI initiatives.
Ensure compliance and ethics
Verify that your AI tools comply with relevant regulations and data privacy laws. Establish ethical guidelines to prevent misuse or bias in AI systems.
Evaluate and iterate
Regularly measure the impact of AI on your business goals and use insights to fine-tune processes, expand AI usage, or explore additional applications.
Plan for long-term integration
Treat AI as a strategic asset rather than a one-time investment. Develop a roadmap for scaling AI across other areas of your business.
By taking a measured and practical approach, your team transforms their workflow, achieving faster, better software without disruptions.
The final words
In a business world speed and efficiency determine a business’s ability to compete. So, using artificial intelligence in software development is no longer optional, it’s inevitable. Yet, many companies remain hesitant, whether due to misconceptions, a fear of change, or the sheer complexity of implementation.
The reality is that bypassing AI now means missing out on core benefits that are being realised today in faster development cycles, better code quality, and more efficient teams.
The business implications of AI in software development go beyond faster code and better testing. Companies that integrate AI into their development pipelines often experience the following:
- Increased agility, as faster iteration cycles mean businesses can pivot more quickly in response to market changes.
- Cost savings, such as reduced development time and fewer post-launch bugs, translate directly into lower development and maintenance costs.
- Market advantage: Delivering high-quality products faster than competitors can be the difference between your success and failure.
Contact us to get a free expert consultation regarding your project idea, AI-driven software development, or any other issue that are dragging your business down.
