Kirill Sorokin, Consulting Partner, EY Azerbaijan
“When it comes to competitiveness in AI today, we’re seeing a global shift. Until recently, success was often measured by a company’s size, access to capital, or government ties. But those indicators matter far less now. The real measure of competitiveness in AI is how fast an organization can learn — especially from its own mistakes. The failures of the 90% that fall behind offer valuable lessons about what not to do and where new opportunities might lie. Right now, the most competitive players in AI are those who learn the fastest and operate the most efficiently. And efficiency is not only about sustainability — it’s about the very real cost of running AI models, which consume enormous amounts of electricity. In many ways, success comes down to innovation speed and the lowest power bill.”
Amy Peck, Founder&CEO, EndeavorXR
“When we talk about responsible AI, the first question on everyone’s mind is how to deploy this technology ethically and safely. Beyond the current buzzwords — agentic AI, AI at the edge, and many other — the most meaningful applications will vary from company to company. The essential starting point is to establish strong AI governance structures within the organization. The next critical element is data. Companies sit on vast archives of historical data while also generating large volumes of dynamic, real-time data. It is crucial to ensure that these data assets are properly structured, cleaned, and ready to be integrated into future business workflows. And when we talk about the future of business, it’s not only about immediate AI implementation for productivity gains — although we know that many AI tools, especially LLMs, can be deployed right away to boost efficiency. The larger objective is to build the long-term foundations that enable companies to leverage AI strategically, sustainably, and responsibly.”
Sabir Mardanov, Head of Data & AI, Azercell Telecom
“Looking from the perspective of a practitioner who has been implementing AI solutions for many years — long before GenAI, going back to early deep learning and even traditional machine learning — I’ve found that the universal formula for success remains remarkably consistent. The key strategic enablers are always the same: strong executive leadership, a clear product-driven mindset, repeatable and reusable data foundations, and production-grade, robust platforms. To me, this is the essential bundle required for meaningful progress. Once these elements are in place, everything else — talent, change management, compliance, measurement — can align behind them. These are the factors that truly help organizations move beyond pilot projects and scale AI use cases effectively across the enterprise.”
Ryan Wang, Co-founder and General Partner of Outpost Capital, Managing Partner at Solaris Venture Partners
“In the short term, we’re seeing billions of dollars flow into AI — from both venture capital and infrastructure investment. Whether these numbers are ultimately justified is still a question, especially since so much capital is chasing a limited number of high-quality companies. Valuations reflect this: firms like Figure AI, still pre-product and pre-revenue, are already valued at around $40 billion. We’re also seeing the rise of “day-one unicorns. However, when we look at a slightly longer horizon—two to three years—the picture changes. The explosive adoption of technologies like ChatGPT, which reached 100 million daily active users within six months, shows how fast AI startups can scale. This acceleration in user growth and product development suggests that today’s massive investments may indeed be justified. Given the current momentum and the hyper-growth potential of AI companies, it’s reasonable to expect many of these investments to pay off — provided investors remain patient.”
