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Artificial Intelligence in Practice: from the Power of Languages to Custom Software Development

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In the previous posts of the Artificial Intelligence in Practice series, we explored how AI has been transforming different sectors of the economy: from operational efficiency in the consumer electronics sector to new experiences and opportunities in the real estate market.

Now, in the fifth post of the series, we will show how artificial intelligence is shaping the future of custom software development, driven by the power of languages.

Until recently, creating customized systems required long development cycles, high investments, and highly specialized teams. Today, this scenario has radically changed. From automatic code generation tools to platforms capable of understanding natural language commands, AI is democratizing access to customized software, making it faster, more accessible, and scalable.

In this post, discover how natural and programming languages evolve with artificial intelligence, what the main applications are in custom software projects, how B2B and B2C businesses are already benefiting from these solutions, and what challenges come with adopting this technology at scale.

The Power of Languages: from Human Communication to Machine Code

The relationship between language and technology has always been a determining factor in the evolution of software. For machines to understand human instructions, it is necessary to translate ideas into code. In the early days, low-level languages such as Assembly predominated, where each line required detailed knowledge of the hardware. Programming was laborious, costly, and restricted to a few specialists.

The evolution brought high-level languages such as C, Java, and Python, which brought programming closer to business logic and accelerated development. Later came declarative languages and frameworks, such as SQL and R, which increased the abstraction of complex tasks.

Now, we are entering the era of natural languages powered by AI. With advances in Natural Language Processing (NLP), it is already possible to describe in different languages what you want, and the technology translates that intent into functional code. Tools such as GitHub Copilot, ChatGPT, Amazon CodeWhisperer, and Google Gemini allow you to generate code snippets, suggest corrections, and even build complete systems from text instructions.

According to consulting firm Gartner (2024), by 2027, 70% of new enterprise code will be written with the support of generative AI, confirming that we are not facing a passing trend, but a structural shift in the way we develop technology.

The New Paradigm of Custom Software Development

Custom software has always been seen as the solution most aligned with a business’s specific needs. However, the traditional process presented several challenges, including lengthy requirement-gathering cycles, high costs, risks of errors, and timelines incompatible with the speed of the digital market.

Artificial intelligence is changing this scenario in virtually every stage. In requirement gathering, algorithms can already analyze documents and service histories to map business needs, reducing communication gaps between client and developer. In code generation, models such as Codex or LLaMA can write in different programming languages from text descriptions, accelerating prototypes and testing.

In this context, Python stands out as the most widely used language for AI and custom software projects. Its simple syntax, vast community, and wide range of specialized libraries such as TensorFlow, PyTorch, Scikit-learn, and Pandas make development faster and more accessible. In addition, Python allows smooth integration with language model APIs, facilitating the creation of systems that combine machine learning, automation, and data analysis in customized solutions.

The benefits also appear in quality. AI performs automated simulations, identifies vulnerabilities, and suggests performance improvements before launch. In maintenance, intelligent systems monitor real-time performance, predict failures, and proactively suggest updates.

AI does not replace development teams, but it extends their capabilities and, with the support of languages such as Python, drastically reduces the time and cost of creating customized solutions.

Practical Cases: Where AI is Already Transforming Custom Software

  • Consumer Electronics Retail: Large chains are creating their own price management platforms integrated with competitor monitoring and dynamic pricing. Previously, they relied on rigid off-the-shelf solutions; now, they have systems tailored to their strategies.
  • Real Estate: Agencies are already adopting AI for customer service through chatbots and predictive pricing, and they are also developing proprietary software that integrates these solutions with CRMs and management platforms.
  • Healthcare: Hospitals are creating customized software that brings together patient history, AI-driven image diagnostics, and medical report automation, adapting each system to compliance rules.
  • Industry 4.0: Factories connect IoT sensors to failure prediction algorithms and control dashboards, making automation more scalable and adaptable.

These examples show how different sectors are already capturing the strategic value of AI-powered custom software.

Strategic Benefits of AI-Powered Custom Software

Adopting AI in software development is not just a technical decision, but a strategic one. A McKinsey study, indicates that businesses can reduce development cycles by up to 60% with generative AI. In addition:

  • Cost reduction, with less dependence on external teams and rigid software licenses;
  • Flexibility, to create systems that adapt to unique rules and processes;
  • Scalability, to grow alongside the business without constant replacements;
  • Continuous innovation, driven by machine learning, leads to ongoing improvements.

Challenges and Points of Attention

Despite the benefits, adopting Artificial Intelligence in software development requires caution. Not every line of code generated by AI models is optimized or error-free, and there is still dependency on technology providers.

Another challenge is data privacy: customized software handles critical information, demanding robust governance. Cultural issues also arise. Many teams fear replacement, but in practice, AI serves as a support resource.

Finally, there is the ethical debate and the issue of algorithmic bias. Software involving credit, healthcare, or recruitment, for example, requires continuous auditing to avoid reproducing inequalities.

These points reinforce that the adoption of Artificial Intelligence must be aligned with a clear strategy, with governance and specialized oversight.

How Businesses Can Prepare for This Transformation

Businesses that want to explore AI in custom software projects should adopt a structured approach. The first step is to conduct an internal assessment, identifying bottlenecks that could be resolved with intelligent automation. Next, it is essential to invest in team training, preparing developers and managers to work together with AI tools.

Another decisive point is the choice of technological architecture: open-source solutions offer flexibility, while proprietary models may bring cost and usage limitations. Data governance should be a priority, ensuring compliance with regulations and the protection of sensitive information.

Finally, it is recommended to start with pilot projects, validating hypotheses on a smaller scale before expanding to more complex solutions. This progressive model increases the chances of success and reduces the risk of high investments without return.

Conclusion

Artificial intelligence applied to custom software development is not just a trend, but a reality that is redefining how businesses build solutions, optimize processes, and deliver value to the market. From advanced use of programming languages to automated testing and the integration of machine learning algorithms, AI is transforming the very act of developing technology.

More than productivity gains, we are facing a new paradigm in which software not only meets current demands but also anticipates future needs, learns from real-time data, and contributes to building smarter, more connected digital ecosystems.

At ASM, the strategic adoption of artificial intelligence in the development of customized solutions is one of the most critical differentiators for businesses that want to remain competitive in the global market.

In the following article, we will discuss the future of artificial intelligence and its connection with sustainability and ESG criteria, analyzing how this technology can drive more responsible, efficient, and socially aligned businesses.