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“65% of respondents report that their organizations are regularly using generative AI, nearly double the percentage from ten months earlier.”

— McKinsey & Company, The State of AI in Early 2024 | Source

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We are witnessing a technological revolution that is redefining the rules of the game in many industries, and software development is no exception. We’re talking about generative artificial intelligence (Generative AI), a technology that can create new content, including code, images or text, based on patterns learned from massive data sets. For business and IT leaders, understanding its current capabilities and future potential is becoming crucial to staying competitive and driving innovation. At ARDURA Consulting, we see generative AI transforming development processes, opening the door to unprecedented efficiency and creativity, but also posing new challenges.

What is generative AI and how is it changing the approach to software development?

Generative AI is a branch of artificial intelligence that focuses on models capable of generating new, original data that resemble the data they were trained on. In the context of software development, this means tools capable of writing code snippets, suggesting solutions to programming problems, creating documentation and even helping design interfaces. Instead of starting from scratch, developers can use AI as an intelligent partner to speed up routine tasks and provide inspiration.

This technology fundamentally changes the approach to the development process. It shifts the focus from manually writing every line of code to strategically using AI tools, verifying their results and focusing on the more complex, creative aspects of software development. This is not automation in the traditional sense, but rather **human-machine collaboration ** on a new, more interactive level. Generative AI is becoming a catalyst that can significantly increase productivity and reduce the time it takes to deliver valuable digital solutions.

What are the key generative AI tools used in development today?

The market for generative AI tools for programmers is growing at a dizzying pace. Currently at the forefront are so-called AI coding assistants that integrate directly with development environments (IDEs ). The best-known example is GitHub Copilot, based on OpenAI models, which analyzes the context of code and comments, suggesting whole blocks of code or single lines in real time. Other popular tools include Amazon CodeWhisperer, Tabnine or Google’s Duet AI, each with its own strengths and underlying models.

In addition to assistants running in IDEs, developers are increasingly using publicly available language models (such as ChatGPT, Claude, Gemini) through their web interfaces or APIs. They are used for rapid prototyping, generating code snippets for specific tasks, clarifying complex concepts, refactoring existing code or even creating documentation. Choosing the right tool depends on the specifics of the project, programming language, budget and team preferences.

How does generative AI support the process of writing code?

Generative AI is becoming a versatile support at various stages of writing code. Its most direct application is code autocomplete on steroids - AI assistants can suggest not only single keywords, but entire functions or classes, based on context and comments. This significantly speeds up the coding process, especially with repetitive patterns.

AI can also help solve programming problems. Instead of searching Stack Overflow, a programmer can pose a question to the AI model, describing the problem and expected behavior, and receive a code suggestion or explanation in response. It is also a valuable tool for refactoring and optimizing existing code, where AI can suggest more elegant, efficient or readable solutions. In addition, generative AI is great at translating code between different programming languages or **generating code based on a natural language description **, making it easier to prototype and explore new technologies.

How to effectively use AI to automate repetitive programming tasks?

One of the greatest promises of generative AI is to free developers from monotonous, repetitive tasks, allowing them to focus on creative problem solving. To effectively realize this potential, it is important to strategically identify the areas where automation will bring the greatest benefit.

Tasks such as generating standard “boilerplate” code (e.g., project configuration, basic data structures), writing unit tests for simple functions, **creating API documentatio ** based on function signatures, or formatting and linting code according to accepted standards are excellent candidates. AI can also help generate scripts for auxiliary tasks, such as data migrations or simple CRUD (Create, Read, Update, Delete) operations.

The key to success is precisely defining tasks in prompts, providing appropriate context (e.g., snippets of existing code, coding standards) and implementing processes to verify the generated code. Automation does not mean giving up control - rather, it requires shifting effort from the writing itself to intelligently directing AI and ensuring the quality of its output.

Can generative AI completely replace programmers?

This question raises a lot of eyebrows, but the current state of technology and predictions for the near future indicate that the answer is a definite no. Generative AI is an extremely powerful tool, but it lacks the true understanding, creativity, ability to solve complex, ambiguous business problems and critical thinking that are the domain of human programmers.

AI is great at handling well-defined, repeatable tasks based on existing patterns. But creating innovative software requires much more - understanding user needs, making architectural decisions, dealing with incomplete requirements and adapting to unforeseen situations. AI models can make mistakes, generate code that is inefficient or vulnerable, and above all - they caot take responsibility for the solution they create.

The future lies not in replacing programmers, but in **synergy and collaboration **. AI will become an extremely sophisticated assistant that will multiply the productivity of programmers, allowing them to focus on higher value-added tasks. The role of the programmer will evolve into an architect, strategist and “conductor” of AI tools.

What are the limitations of generative AI in software development?

Despite its impressive capabilities, generative AI has significant limitations that must be considered when implementing it in software development. First and foremost, these models can “hallucinate,” generating code that looks correct but is logically flawed, incomplete or does not work as expected. They lack a deep understanding of the business context and nuances of requirements, which can lead to superficial or suboptimal solutions.

The generated code may also contain security vulnerabilities, as the models learn from huge data sets, including code containing vulnerabilities. There is also a risk of duplicating bugs or bad practices present in the training data. Models struggle with complex algorithmic reasoning and often perform worse on tasks that require innovation or going beyond known patterns. Moreover, understanding and debugging AI-generated code can be more difficult than code written by humans. Finally, licensing and copyright issues for AI-generated code are still not fully settled. Awareness of these limitations is crucial for the responsible use of this technology.

Fiche 1: AI limitations in coding - remember!

  • Hallucinations and errors: AI can be wrong. Always verify the code!

  • Lack of business context: AI does not understand the nuances of your business.

  • Security risk: The code generated may contain vulnerabilities. Audit!

  • Replicating bad practices: AI learns from what it has seen - good and bad.

  • Complexity problems: Innovative algorithms are still the domain of humans.

  • Legal issues: Licenses and copyrights for AI code are sometimes unclear.

How to ensure the security of code generated by artificial intelligence?

The security of code generated by AI is an absolutely key issue. Since models learn from publicly available data, including code that contains vulnerabilities, there is a real risk that the generated fragments will be unsafe. Ensuring security requires a multi-level approach.

First, the code generated by AI caot be trusted unreservedly. Every piece of it, especially those dealing with input handling, authentication, authorization or cryptographic operations, must undergo rigorous review (code review) by experienced developers.

Second, deploy automated code security analysis tools (SAST - Static Application Security Testing) in CI/CD pipelines. These tools can scan both human-written and AI-generated code for known vulnerability patterns.

Third, it is important to regularly conduct penetration tests and security audits of applications that contain AI-assisted code.

Fourth, developers should be trained in secure coding and informed use of AI tools, sensitizing them to potential risks.

Finally, consider using AI models specialized in security or fine-tuning generic models on your own secure code (if possible and economically justified) to minimize the risk of generating vulnerabilities. Security must be a priority at every stage of using AI in the development process.

To what extent can generative AI speed up the software development process?

The potential to speed up the software development process with generative AI is significant, but varied. The greatest benefits are observed in automating repetitive and standard tasks, such as writing boilerplate code, generating unit tests or creating basic documentation. Here, acceleration can reach tens of percent.

Coding assistants such as GitHub Copilot can increase programmer productivity by an average of 20-50% in everyday coding tasks. However, for more complex tasks that require creativity or architectural decisions, the acceleration is much less. Moreover, the time saved on writing code must be partially reinvested in verifying and testing AI-generated code. The overall software development life cycle (SDLC) acceleration is therefore typically smaller than the acceleration of the coding stage alone, but even a moderate reduction in software delivery time can translate into a significant competitive advantage.

How does generative AI affect code quality and reliability?

The impact of generative AI on code quality and reliability is complex. On the one hand, AI can positively affect quality by suggesting good practices, helping with refactoring, generating tests or detecting potential bugs. It can also contribute to greater code consistency.

On the other hand, there are serious risks. AI can generate code that is erroneous, inefficient or contains security vulnerabilities. It can also replicate bad practices. Over-reliance on AI without verification can lead to the introduction of hard-to-detect bugs or reduce code readability.

The ultimate impact of AI on quality depends on how it is used and the maturity of verification processes. It is crucial to treat AI as an enabler rather than a replacement for human responsibility for quality. Rigorous code reviews, testing and monitoring become even more important.

What skills should developers develop in the era of generative AI?

The era of generative AI requires programmers to evolve and expand their skills. While the basics of programming remain crucial, the competencies to work effectively with AI are becoming increasingly important:

  • Prompt engineering: precise formulation of queries to AI.

  • Critical thinking and verification: skeptical evaluation of AI suggestions.

  • Systems architecture and design: making strategic technology decisions.

  • Solving complex problems: dealing with ambiguity and creative challenges.

  • Domain knowledge: deep understanding of the business context.

  • Software security: threat awareness and AI code verification.

  • Communication and collaboration skills: working effectively in a team and with stakeholders.

  • Continuous learning and adaptability: willingness to learn new tools quickly.

Programmers of the future will be less “coders” and more architects, designers and critical thinkers, using AI as a powerful tool.

What does human-AI collaboration look like during software development?

Human collaboration with generative AI is akin to working with an extremely fast but sometimes unpredictable assistant. The key is synergy. The programmer sets the direction, defines the problem and makes strategic decisions. He then directs the AI’s work with precise prompts. The next extremely important stage is to verify and adapt the results of the AI’s work. The programmer must critically evaluate the generated code and modify or integrate it. In this collaboration, the human brings creativity, experience and responsibility, while AI provides speed and extensive knowledge. This is a “pair programming” model ** at a new level**, where AI is a partner, but control remains with the human.

How does generative AI support code debugging and testing?

Generative AI also offers significant support in the debugging and testing stages. In the debugging area, AI can analyze error messages, suggesting potential causes and suggesting fixes. It can also help understand complex code by generating explanations. In the testing context, AI can generate test cases, create test data (especially edge data ), help write automation script frameworks, and analyze test execution results, identifying error patterns and speeding up reporting. This is an area with great potential for improvement.

What are the business benefits of implementing generative AI in a software company?

Implementing generative AI in software development processes can translate into a number of significant business benefits. First and foremost is the increased productivity of development teams resulting from the automation of routine tasks. This, in turn, can lead to reduced time-to-market, which is crucial in today’s competitive environment. While investment is required, long-term there is the potential to reduce development costs through increased efficiency. With proper implementation and verification processes, AI can also help improve code quality and reliability. Relieving developers of monotonous tasks can **increase innovatio ** and **improve talent satisfaction and retentio **. Realizing these benefits, however, requires a strategic approach and conscious management.

The use of generative AI raises significant legal and ethical issues. Companies face uncertainty regarding copyrights and licenses for AI-generated code, which can lead to disputes. Protecting the confidentiality of data and intellectual property put into AI tools, especially cloud-based ones, becomes crucial. The question of liability (accountability) for errors or damage caused by software containing AI code arises - the lack of clear regulation is a risk here. The risk of AI replicating biases (bias) that could lead to discriminatory application results should also be actively addressed. The issue of transparency to users about the use of AI in the development process is also gaining attention. These challenges require careful legal analysis, implementation of appropriate policies and informed risk management.

Fiche 2: AI, law and ethics - be careful!

  • Licenses and copyrights: check the origin and licenses of AI code.

  • Data Confidentiality: Be careful what you send to external AI tools.

  • Accountability: who is responsible for AI errors? It’s still a gray area.

  • Bias and fairness: test applications for potential discrimination.

  • Transparency: Consider how to communicate the use of AI.

How does generative AI affect technical documentation and code commenting?

Generative AI offers significant support for the often underestimated but essential tasks of documentation and code commenting. It can automatically generate descriptions of functions, classes or modules based on code analysis. It is capable of creating API documentation templates or generating README files. What’s more, AI can help keep documentation consistent and up-to-date by suggesting changes to descriptions as code evolves. It can also analyze existing code and add comments to explain how it works, which is invaluable when working with legacy code. However, as with code, the results of AI’s work in the documentation area need to be reviewed by programmers to ensure their factual accuracy. Nonetheless, AI can significantly reduce the time and effort required to produce good documentation.

How can AI help in requirements analysis and software architecture design?

AI’s potential extends beyond just coding to include earlier stages of the software development lifecycle. At the requirements analysis stage, AI can support the processing of large volumes of documents, identifying key functionality, conflicts or ambiguities. It can generate initial user stories or acceptance criteria. In the area of **architecture desig **, AI can act as **a source of inspiratio **, suggesting appropriate architectural patterns and helping analyze trade-offs between different approaches. It can even generate simple diagrams. However, it should be emphasized that at these strategic stages, AI’s role is only supportive. It will not replace the architect’s experience, his ability to understand the business context and make complex design decisions.

What new professional roles are emerging due to the development of generative AI?

The development of generative AI is leading to the evolution of roles and the emergence of new specializations. We are seeing the formation of roles such as Prompt Engineer, a specialist in formulating queries for AI. AI Interaction Designers, designing interactions with AI systems, are growing in importance. AI Ethicist / Auditors, responsible for the ethics and auditing of AI systems, are emerging. **AI Trai **ers / Fine-Tuners are needed, tuning models for specific tasks. The role of MLOps Engineer with a focus on managing generative models is also evolving. At the same time, traditional roles are evolving, with developers becoming more architects and testers becoming AI-assisted quality curators. The key is to adapt and develop new competencies.

What will the future of software development with generative AI look like in 5-10 years?

Looking ahead, we can expect generative AI to become an integral part of the software development ecosystem, deeply integrated into tools and processes. Ubiquitous AI assistants will offer even more advanced support. Automation will extend to increasingly complex tasks, potentially generating entire application components. AI support will extend throughout the software lifecycle, from requirements to monitoring. AI models specializing in specific domains or quality aspects will emerge. The role of the software developer will evolve into strategic management of AI collaboration. Perhaps new software development paradigms will emerge, based more on intent than code. At the same time , challenges related to quality, security and ethics will remain key. The future is not “the end of coding,” but its **transformation toward more intelligent human-machine collaboration **.

What generative AI solutions are worth investing in now?

For companies looking to strategically leverage the potential of generative AI, it’s worth considering investments in a few key areas now. A good place to start is with coding assistants (e.g., GitHub Copilot), which realistically increase developer productivity. It’s worth providing teams with access to language model platforms and APIs (OpenAI, Google AI, etc.) to allow experimentation and creation of their own assistive tools. It is absolutely crucial to invest in training and competence development of the team in Prompt Engineering and critical evaluation of AI results. In the context of security, tools for static code security analysis (SAST) and license analysis are becoming essential. For larger organizations planning to train their own models, an investment in MLOps platforms will be necessary. It is important to start with the areas with the highest return potential and lowest risk.

How to prepare the development team to effectively use AI tools?

Preparing the development team is a key factor for success. This requires first and foremost educating and demystifying AI, explaining its capabilities and limitations. Training in Prompt Engineering and providing access to tools in a secure environment for experimentation is essential. Together, good practices and guidelines for using AI, code verification and security should be developed. It is worth starting with small experiments and promoting a culture of critical thinking, emphasizing that AI is an assistant, not a substitute. It is important to create a space for sharing knowledge and experience within the team. The whole process requires leadership support and an evolutionary approach focused on real benefits.