From your perspective, the biggest trends that shape the future of artificial intelligence and, in particular, AI modeling?
We are seeing a clear change from the scale accurately. The biggest trend is high quality, increasing demand for domain-specific data. The early models were learned from messy, general dataset. Now, performance benefits depend on curate, accurate and fine data that can push the previous model of current plateaus.
Training has also become faster and more repeated. Instead of months-long sprints, the team is running a focused experiment to solve problems more efficiently.
The series of ideas arguments is another major leap. Now we can see how models think, not only what they say – to customize logic, create confidence and unlock new methods to handle complex tasks.
Finally, the agent AI is increasing. These systems do not just respond, they execute. Whether it is handling the workflows or coordinating the tool, AI is starting working like a true digital assistant, and it is changing everything.
The data is at the origin of AI, but the AI model requires the right data. How can companies ensure their data input quality?
Data keeps growing for quality. A few years ago, broad, incomplete data sets – filled with typo or general chat – were enough to get models from the ground. Today, every incremental performance benefits depend on high-loyal, high refined data. Each reaction in accuracy, perfection and nuances more than before. For companies, the challenge is no longer about collecting more data, but cure the correct data to meaningfully inform the next round of fine tuning. A recent survey by Dun & Bradustratit shows that only half of the officials believe their data is ready to meet AI’s demands.
Can you talk about the importance of finding a balance between AI and human touch?
The invisible was established on the belief that technology and business would always need humanity. AI is not about changing humans – it is about rethinking how work is done. A good example is a manufacturing line: just swapping in AI 1: 1 maximum quickly out for a human. You still need at least one person on the line. The real advantage comes when you assure the entire workflow, remove unnecessary steps and designs around new abilities. True efficiency comes when you combine the accuracy of the machine with human inspection and design systems to elevate both.
