Generative artificial intelligence has expanded the boundaries of what is possible in the business realm. However, for industry leaders, the real challenge is no longer simply adopting AI, but deeply integrating it into the strategic core of the organization. The big question resonating in boardrooms is: how do we make AI part of our business DNA?
Leading companies in data utilization like OpenAI, MongoDB, Pinterest, Netflix, and Adidas have paved the way, establishing the principles that differentiate theory from real application. According to Matías Cascallares, Director of Customer Success Engineering for EMEA at Confluent, the leap from experimentation to effective implementation requires more than good intentions: it demands a radically different architecture.
“The key is to build a real-time, event-driven architecture equipped for continuous intelligence,” Cascallares emphasizes. On this premise, Confluent identifies four fundamental lessons for shaping a truly AI-native company.
Reimagining Developer Productivity in the Age of AI
The modern development environment is becoming increasingly intelligent. AI tools generate code, detect errors, and suggest improvements in the blink of an eye, but faster doesn’t always mean better.
Working sessions focused on the emerging truth that real productivity isn’t about generating code faster. It’s about building with confidence. And that means prioritizing reliability, version control, and auditability.
In the world of Kafka-based systems, a recurring theme in discussions is that AI should enhance, not automate, the rigor of engineering. The smarter approach? Treat AI as a powerful pair programmer, not a lone executor. It’s essential for AI to propose solutions while allowing developers to review, refine, and publish. “This mindset preserves what matters most in enterprise software without sacrificing speed,” Cascallares notes.
AI Agents Are Here and Need a Real-Time Backbone
AI copilots are changing the way people work, but the next leap is systemic: autonomous agents that pursue goals, explore real-world context, make decisions, and act, often without waiting for a human push.
Today, companies are increasingly realizing that agents do not thrive on outdated, batch-processed data. They need fresh, fluid, and ongoing context. That’s where streaming infrastructure comes into play.
Many of the pioneering companies mentioned earlier highlight how Flink, Kafka, and other technologies combine to create real-time agents that detect spending anomalies, adjust supply chains on the go, and make sense of complex environments in real-time.
This shift from passive data collection to active intelligence in the moment transforms companies from reactive machines into proactive engines. The advantages are far-reaching, from faster insights and fewer bottlenecks to operations that adjust instantly rather than days or hours later.
Real-Time AI Channels: The Foundation of the Future
An AI model is only as good as its input data and synchronization. In the age of immediacy, feeding models with real-time data is fundamental.
In sessions about open-source integration, we’ve demonstrated how end-to-end pipelines are evolving. Apache Flink, TensorFlow, and PyTorch are integrating into seamless systems that continuously ingest, clean, infer, and even retrain models.
“It’s AI that moves with the world. AI that can respond to a fraudulent transaction, a supply chain failure, or a market change as it’s happening, not in tomorrow’s report,” recalls Matías Cascallares, Director of Customer Success Engineering EMEA at Confluent.
With scalable, observable, and resilient infrastructure under the hood, companies can deploy AI that not only scales but also remains relevant. This changes the game across the board, from fraud detection to personalized experiences and real-time alerts.
And it all starts with streaming. In fact, 89% of global IT leaders say that data streaming platforms (DSPs) facilitate AI adoption by addressing their pain points. The 2025 Data Streaming Report from Confluent also revealed that 87% claim that DSPs will increasingly be used to feed AI systems with real-time, contextual, and reliable data, while 73% of leaders cited DSPs as enablers of using business data to drive AI-based systems.
Becoming Truly AI Native Starts with Infrastructure
Finally, too many teams chase new and shiny AI tools without fixing the foundations. However, companies that are leading the way are not focusing on features but on reconsidering how their systems process data, act on it, and learn from it.
From developers utilizing smart assistants to autonomous agents orchestrating complete workflows, it all points to one thing: real-time event-based infrastructure. “The AI revolution isn’t just about smarter software but about a smarter backbone. And for leaders ready to move from prototype to production, that’s where true transformation begins,” concludes the expert.