For decades, the decision between building software from scratch or acquiring a ready-made solution guided technology strategies in companies from the most diverse sectors. The equation seemed simple, buying accelerated adoption and reduced costs, building offered customization and control. But the arrival of the generative artificial intelligence, and, in particular, of the development assisted by AI (AIAD), modified all the variables of this account. It is no longer a question of deciding between two classic approaches, and perhaps the traditional dilemma no longer exists.
With generative AI optimizing crucial development cycle steps such as code writing, automated testing, bug detection and even architectural suggestions, building tailored software is no longer an exclusive effort by large corporations with robust budgets. Pre-trained models, specialized libraries and low-code or non-code platforms powered by AI drastically reduced costs and development time.
Instead of months, many solutions today are delivered in weeks, and instead of numerous internal teams, lean and highly specialized teams can deliver customized and scalable applications with impressive efficiency. GitHub Copilot, launched in 2021, is a practical example of generative AI that assists developers by suggesting codes and completing snippets automatically. A GitHub study indicated that developers using Copilot completed faster 55% tasks on average, while those who used it took an average of 1 hour and 11 minutes to complete the task, those who did not use GitHub Copilot took an average of 2 hours and 41 minutes.
Faced with this reality, the old argument that buying ready-made software was synonymous with economics loses strength. Generic solutions, although tempting, often do not shape the particularities of internal processes, do not scale with the same agility and create a limiting dependence. In the short term, they may seem sufficient, but in the medium and long term, they become obstacles to innovation.
More than that, the very notion that the competitive advantage lies in the code itself begins to crumble. In a scenario where rewriting an entire application has become cheap and viable, the idea of “protecting the code” as a strategic asset makes less and less sense. The real value lies in the architecture of the solution, the fluidity of the integration with the business systems, in the governance of the data and, mainly, in the ability to quickly adapt the software as the market, or the company, changes.
The use of artificial intelligence (AI) and automation reduces up to 50% at development time, as pointed out by 75% of executives interviewed in a report conducted by OutSystems and KPMG. But if “build” is the new normal, a second dilemma arises: build internally or with specialized external partners? Here, pragmatism speaks louder. Creating a technology team of its own requires continuous investment, talent management, infrastructure and, above all, time, the scarce asset in the race for innovation. for companies whose core business it is not software, this choice can be counterproductive.
On the other hand, strategic partnerships with development companies bring advantages, such as immediate access to advanced technical know-how, accelerated delivery, hiring flexibility and operational overhead reduction. Experienced outsourced teams act as an extension of the company, focusing on results, and often come with ready-made models of scalable architecture, integrated CI/CD pipelines and tested frameworks, everything that would be costly and time-consuming to build from scratch. It is also worth mentioning a third element in this equation: the network effect of accumulated expertise.
While internal teams face a continuous learning curve, external experts who work on multiple projects accumulate technical and business repertoire at a much faster pace. This collective intelligence, applied in a directed way, often generates more effective and innovative solutions. The decision, therefore, is no longer between buying or building, but between sticking to plastered solutions or building something that really meets what the business needs. Personalization, rather a luxury, became an expectation, scalability, a requirement, and AI, a watershed.
In the end, the real competitive differential is not in the ready-made software, nor in the lines of code written to measure, but in the strategic agility with which companies integrate technological solutions to their growth. The Aiad era invites us to abandon binary dilemmas and think of software as a continuous, lively and strategic process. And for that, it is not enough to build, it is necessary to build with intelligence, right partners and vision of the future.
