Using AI effectively isn’t just about adopting the newest tools or programming languages. The real key is leveraging your team’s skills and what your current systems can do. The best AI solutions build on what you already have, making your work smoother and faster.

Python’s Rise and Its Limitations

Python became the go-to language for AI because it makes turning ideas into working programs quick and easy. It’s simple to learn, versatile, and many AI libraries are built in Python. That’s why so many tutorials and open-source projects are Python-based. It lowers the barrier for experimentation, which is crucial in the fast-moving AI world.

But Python isn’t the only way to build AI. Rod Johnson, the creator of the Spring framework, argues that other languages like Java can be just as effective—sometimes even better—especially in enterprise settings. For example, Johnson reimplemented an AI workflow from Python in Java using his Embabel framework. His tests showed Java could create more reliable, maintainable AI agents, thanks to its strong typing and enterprise features.

People First, Technology Second

Many get caught up in the tech debate—Python versus Java or other languages—but the real challenge with AI isn’t the tools. It’s the people. Skills, domain knowledge, and how easily staff adopt new tech matter far more than the language choice. If your team already knows Java, forcing them into Python for AI doesn’t make sense. Instead, using what they know will save time and reduce mistakes.

This idea applies beyond just programming languages. Most companies will integrate AI into their existing platforms rather than switch to new, AI-specific stacks. By 2028, Gartner expects 80% of AI applications will run on current data management systems. This means building AI solutions with your current tools and skills is usually the smartest move.

Choosing the Right Language for Your AI Projects

There’s a common belief that Python is necessary for generative AI projects, like those using large language models. It’s true that many tutorials and libraries are Python-based, making it seem like the only option. But that’s not the full story. Java and C# can also be used to build AI agents, especially with frameworks like Embabel that Johnson developed. In his tests, Java offered better type safety and easier integration with enterprise systems, making it a strong choice for real-world applications.

It all comes down to your team’s existing expertise. If your developers are already skilled in Java, switching to Python just for AI might not be worth the hassle. Conversely, Python teams should stick with what they know unless there’s a compelling reason to switch. The goal isn’t to chase the latest trend but to choose tools that help your team work efficiently and effectively.

Of course, Python’s extensive AI library ecosystem is a plus. But many of those capabilities are now accessible from other languages through APIs or new libraries. The gap between languages for AI development is closing fast. The bottom line: pick the tools that fit your team and systems, not what’s fashionable right now.

In a rapidly evolving field, acting quickly is crucial. AI is expected to become deeply embedded in business operations by 2026, so delaying your efforts could mean missing out. Building on your existing skills and systems is the fastest way to start integrating AI and reaping its benefits. Remember, AI is a means to an end—better products, smarter workflows, and improved business outcomes. The smartest move is to use what you already have, adapt your team, and focus on delivering value.

Inspired by

• https://www.infoworld.com/article/4071159/java-or-python-for-building-agents.html

Sources

• gmpg.org
• unsplash.com
• techrepublic.com
• medium.com
• github.com