Why Identity Is the True Perimeter of Enterprise Security

From your perspective, what are the biggest trends shaping the future of artificial intelligence and, more specifically, AI modeling?

We’re seeing a clear shift from scale to precision. The biggest trend is the rising demand for high-quality, domain-specific data. Early models learned from messy, general datasets. Now, performance gains depend on curated, accurate and nuanced data that can push models past current plateaus.

Training has also become faster and more iterative. Instead of months-long sprints, teams are running focused experiments to solve problems more efficiently.

Chain of thought reasoning is another major leap. We can now observe how models think, not just what they say – unlocking new ways to optimize logic, build trust and handle complex tasks.

Finally, agentic AI is on the rise. These systems don’t just respond, they execute. Whether it’s handling workflows or coordinating tools, AI is starting to act more like a true digital assistant, and that’s changing everything.

Data is at the core of AI, but having the right data for AI models is essential. How can companies ensure the quality of their data inputs?

The bar for data quality keeps rising. A few years ago, broad, imperfect data sets – full of typos or general chat – were good enough to get models off the ground. Today, every incremental performance gain depends on high-fidelity, highly refined data. Accuracy, completeness and nuance in each response matter more than ever. For companies, the challenge is no longer about collecting more data, but curating the right data to meaningfully inform the next round of fine tuning. A recent survey from Dun & Bradstreet shows that only about half of executives believe their data is ready to meet the demands of AI.

Can you speak to the importance of finding a balance between AI and human touch?

Invisible was founded on the belief that technology and business will always need humanity. AI isn’t about replacing humans – it’s about rethinking how work gets done. A good example is a manufacturing line: simply swapping in AI for a human 1:1 maxes out quickly. You still need at least one person on the line. The real gains come when you reassess the entire workflow, removing unnecessary steps and designing around new capabilities. True efficiency comes when you pair machine precision with human oversight and design systems to elevate both.