AI Insights

The software development process is rapidly changing. Very soon, it will look completely different from what we are used to. Today, coding often takes the most time in software creation, but tomorrow it might become the fastest part of the process, as the code will be written by an AI assistant rather than a human.

The emergence of AI assistants such as GitHub Copilot and OpenAI Codex unlocks new opportunities to speed up development and reduce routine tasks. Thanks to natural language interfaces, these tools allow developers to focus on complex aspects of architecture and business logic, spending less time on basic coding. At the same time, this transformation demands team adaptation and the introduction of new quality control practices.

This article presents our vision of how the software development process will change in the near future as a result of integrating AI assistants.

The Role of AI Assistants

Modern AI assistants can generate boilerplate code, suggest solutions to common problems, and assist in debugging.

For example, GitHub Copilot, integrated into popular IDEs, interprets the context of open files and offers real-time suggestions. Unlike earlier popular development tools like JetBrains ReSharper, its suggestions often consist of large, meaningful code blocks that a developer would typically write themselves. These suggestions can dramatically accelerate the coding process.

OpenAI Codex, in turn, is capable of iterative code refinement, including running tests and fixing errors without constant developer intervention. This tool performs actions on the codebase, checks the result, and compares it to the original task. If the task is not completed, it creates a new action plan and repeats the cycle. This continues until the desired result is achieved or the maximum number of iterations is reached.

Upon closer inspection of such an assistant’s workflow, it’s clear that its logic closely resembles that of a human programmer. However, its speed is significantly higher, and the cost may be considerably lower.

Currently, the quality of such tools is not always stable, but over time these issues will be resolved, and effective usage strategies will be developed. Soon, there will be no practical difference between delegating and controlling a task performed by a human or a virtual assistant.

Automating routine development steps leads to a noticeable redistribution of time: developers now dedicate more of their workday to task planning, architecture design, and stakeholder communication. This shifts team workflows and raises the bar for project management skills.

The following skills become increasingly important:

• Effective communication
• Ability to structure and articulate tasks
• Understanding of AI systems
• Knowledge of software design architectures and patterns
• Awareness of how technologies apply to business problems

In other words, the competence level of such specialists will be high. On the one hand, this may lead to higher compensation. On the other hand, one such specialist, aided by AI assistants, could potentially replace a team of developers. This is the core of the economic value that AI tools promise.

Risks and Challenges of AI Assistant Adoption

Despite the clear advantages, integrating AI assistants comes with a number of challenges that must be addressed:

Security

Generated code may not comply with corporate security and licensing standards. Many models are trained on publicly available data, and not all original authors approved this usage. Moreover, the generated code may contain various vulnerabilities. Therefore, it’s crucial to thoroughly inspect software and components added by AI.

Legal Issues

Internal policies for working with AI suggestions must be developed to avoid copyright violations and regulatory non-compliance. Some AI assistants allow filtering suggestions that originate from legally restricted code.

Higher entry threshold for the profession

Reducing routine work increases the proportion of tasks requiring high qualifications. As a result, junior developers will need significantly more knowledge and skills to justify their hiring over AI assistants.

Dependence on major LLM providers

There is an ongoing race among LLM providers to capture market share. Many customers rely on their services and adapt their business processes accordingly. Given global economic and political instability, sudden service cutoffs are possible. Therefore, businesses should always consider alternative providers and have contingency plans in place.

Ethical Dilemmas

AI assistants may produce decisions or suggestions that are ethically questionable. This can involve both code content and model behaviour based on input. It’s vital to ensure AI usage aligns with corporate values and principles of fairness, inclusivity, and transparency. Acceptable boundaries should be defined in advance, with mechanisms for evaluating and correcting AI behaviour.

New Challenges to Consider

The introduction of AI assistants also brings organisational and cultural challenges:

Resistance to change

No matter how advanced AI becomes, humans remain the source of creative ideas. AI assistants should be seen as productivity multipliers. Achieving this requires changing work habits and approaches. Currently, most people resist these changes; a few may even openly sabotage them. It is essential to plan AI adoption thoughtfully, conduct explanatory and training sessions, and allocate time for learning. A common mistake is implementing new technology without adjusting release plans. As a result, employees often lack time to understand and use new tools. In addition to licensing costs, time and resources must be invested in training.

Rethinking business models

There is a growing belief that in the near future, a single qualified specialist with the right AI tools will be able to replace a small team. This vision requires rethinking specialist compensation and business models—especially for service companies that sell labor by volume. Alternatives might include new pricing models or charging based on output. Each business must choose what works best for them.

Future Outlook

Over the next 2–3 years, AI assistants will become an integral part of development workflows, enabling companies to respond more quickly to market needs, optimise costs, and focus on strategic initiatives. However, only those organisations that thoughtfully combine technology with refined methodologies, invest in developing new competencies, and prepare their infrastructure to work closely with AI will achieve sustainable benefits. Only such a systematic approach will enable high levels of automation without sacrificing flexibility, quality, or security.