Articles and webinars

As AI becomes more integrated into software development, it inevitably changes quality assurance as well. Implementation speed increases, releases happen faster, and suddenly testing and validation feel like they are running on a treadmill set by someone else. If we do not evolve QA alongside development, we are just creating a brand new bottleneck.

AI tools are quietly reshaping what it means to be a Product Owner. Traditionally, many POs have focused on gathering business requirements and prioritizing them for technical teams. They have acted as translators between stakeholders and developers, which has often meant long feedback loops and plenty of ambiguity at the start. AI changes this.

Most software architects are generalists, especially in consulting. Industries and technology stacks change constantly, and architects rely heavily on accumulated experience to design workable solutions in unfamiliar contexts. One long-standing challenge has been domain knowledge. When every project comes from a different industry, there is always a learning curve at the start. AI has made this much easier.

Artificial intelligence is widely used in software development today. In practice, however, "AI-powered development" means very different things to different companies and service providers – for some, it means a range of tools for developers' everyday work, while for others, it is a series of separate experiments. In this blog, we will explain what AI-based development is like when it is a strategic part of the entire operating model.

It is common for companies to have some of their software lag behind the development of the technologies used in them. For example, applications based on React or Django may be functionally sound but use outdated versions of these technologies. This type of technical debt does not usually require the construction of a completely new system, but it increases risks and complicates development work. At this point, the principles of legacy system modernization can also be effectively applied to simple version updates, bringing significant value to organizations. Fluentia utilizes an AI-based modernization model for this purpose.

Outdated systems slow down organizations' ability to improve their business and take advantage of automation. Modernizing systems to meet today's requirements is not just a technical upgrade, but an opportunity to grow and improve business efficiency. Typical technologies to be modernized include COBOL, RPG, PHP, Visual Basic and other legacy languages that no longer meet current development and integration requirements.