From your perspective, what is the biggest trend that will shape the future of artificial intelligence, and more specifically, AI modeling?
There is a clear shift from scale to accuracy. The biggest trend is the increased demand for high quality, domain-specific data. An early model learned from a messy, general dataset. Currently, performance improvements rely on curated, accurate, subtle data that can push models past the current plateau.
My training has also become faster and more repetitive. Instead of months of sprints, the team is running intensive experiments to solve problems more efficiently.
A series of thought reasoning is another big leap. You can observe not only what the model says, but how the model thinks. Optimize your logic, build trust, and unlock new ways to handle complex tasks.
Finally, Agent AI is on the rise. These systems run rather than simply responding. Whether it’s a workflow handling or a tuning tool, AI is beginning to function like a true digital assistant, and that’s changing everything.
Data is at the heart of AI, but having the right data for your AI model is essential. How can businesses ensure the quality of their data entry?
Data quality bars continue to rise. A few years ago, a wide, incomplete dataset filled with typos and general chats was enough to get the model off the ground. Today, all incremental performance gains rely on high fidelity, highly refined data. The accuracy, integrity and nuance of each response are more important than ever. For businesses, the challenge is not to gather more data anymore, but to curate the appropriate data to meaningfully inform the next round of fine-tuning. A recent survey from Dun & Bradstreet shows that only about half of executives think the data is ready to meet AI demands.
Can you talk about the importance of finding a balance between AI and human touch?
Invisible was founded on the belief that technology and business always requires humanity. AI is not about replacing humans – it’s about rethinking how work is done. A good example is the production line. Just swap AI quickly for a 1:1 human. You still need at least one person on the line. Reevaluating the entire workflow, removing unnecessary steps, and designing around new features will bring real benefits. True efficiency is when the machine’s accuracy and human monitoring and design systems are paired to increase both, and true efficiency arises.
