The advancement of artificial intelligence (AI) in software engineering has entered a new phase, marked by the transition from experimental discourse to structured adoption in corporate processes. After a period of strong initial enthusiasm, companies begin to deal with the practical challenge of integrating AI into the real cycle of development, testing and operation of systems, especially in critical and regulated environments. According to a study by International Data Corporation (IDC), commissioned by Microsoft, 68% of companies in the world already use generative artificial intelligence in their operations, indicating the consolidation of technology in the corporate environment.
In Brazil, this movement is mainly observed in the financial sector. Institutions such as Banco Bradesco have been using integrated AI platforms to automate processes and gain operational efficiency, while fintechs such as Nubank stand out internationally for the strategic application of technology in digital products and operations. This advancement increases the complexity of systems and reinforces the need for technical maturity, governance and integration of AI into existing software engineering flows.
To Marcelo Marchi, CEO of Vericode, a company specialized in software quality, test automation and observability, the adoption of the concept “AI First", the practical challenge of integrating artificial intelligence into real software engineering processes requires more than the incorporation of new tools. "Artificial intelligence only generates value when it is integrated into the engineering flow, with governance and predictability", he states. According to him, isolated initiatives tend to lose effectiveness when they are not connected to consolidated processes, controls and practices.
The global research “Future of Cyber”, by Deloitte, points out that, in Brazil, artificial intelligence/cognitive computing occupies the 6th position in the ranking of investments in cybersecurity, while generative AI appears in 13th place. The data reflects the efforts of organizations to structure controls, model inventories, usage records and access policies, especially given the growth of automation and the expansion of regulatory and operational risks in sectors such as finance.
As a result, platforms that apply AI in an integrated way to software engineering gain relevance. To Marcelo Marchi, solutions aimed at test automation, API validation and generation of technical evidence allow artificial intelligence to be incorporated in a gradual and controlled manner into teams' daily lives. “This path favors scalability, security and predictability, transforming AI into a structural element of software engineering, and not just a technological promise”, he highlights.
