Python has become the go-to language for AI not because it’s the fastest or most feature-rich, but because it makes the path from idea to working code exceptionally short. Its accessibility and general-purpose nature make it familiar to many developers, often as a second language, which lowers the barrier to experimentation. In a rapidly evolving AI landscape, this ease of use is invaluable, allowing teams to test concepts and iterate quickly. Python’s popularity has surged alongside AI’s growth for precisely this reason.
However, Python is not the only viable option for AI development. Rod Johnson, creator of the Spring framework, notes that if your organization is already invested in Java, leveraging Java-based AI frameworks like Embabel can be just as effective. The key isn’t chasing the “perfect” language but enabling your teams to use the tools they already know. Switching languages solely for perceived technical advantages often introduces unnecessary friction and slows down progress.
At the heart of AI success is people, not just technology. Domain expertise, skills, and adoption are far more critical than picking the latest programming language. Organizations often fall into the trap of introducing shiny new tools without considering how well their teams can actually use them. Empowering staff with familiar tools—be it Excel for analytics, SQL for AI queries, or the programming language your developers already know—ensures higher adoption and better outcomes.
This principle applies to the entire tech stack, not just programming languages. According to Gartner, by 2028, 80% of generative AI business applications will be built on existing data platforms rather than on entirely new AI-first architectures. In other words, integrating AI into your current systems and workflows—rather than chasing trendy new stacks—delivers the clearest path to real value. Using what your teams already know and the systems they already trust is often the most efficient and impactful strategy.