Trump is set to hold an exclusive meeting with select global business leaders on the sidelines of the World Economic Forum in Davos on Wednesday, marking a significant moment for international corporate dialogue at the annual Swiss gathering. The reception will highlight United States policy priorities among the assembled political and corporate elites.

Indian Corporate Leadership at Davos

A notable delegation of seven prominent Indian CEOs will participate in the high-profile reception, demonstrating India's expanding influence in global corporate discussions. The confirmed Indian business leaders represent some of the country's largest and most influential companies across diverse sectors.

Global Economic Forum Context

The World Economic Forum in Davos serves as a premier platform for international business and political dialogue, attracting over 3,000 delegates from more than 130 countries. This year's gathering continues to emphasize global economic cooperation and policy coordination among world leaders and corporate executives.

The inclusion of Indian business leaders in Trump's exclusive reception reflects the country's growing economic significance and corporate influence on the global stage. These executives represent key sectors including technology services, telecommunications, automotive, financial services, and diversified industrial operations.

Strategic Corporate Engagement

The participation of these Indian CEOs in the reception underscores the strategic importance of India-US business relationships and highlights the role of Indian corporations in international economic discussions. Their presence at this exclusive gathering positions Indian business interests prominently within global policy conversations taking place at Davos.

The meeting represents an opportunity for direct engagement between Indian corporate leadership and US policy priorities, potentially influencing future bilateral business and economic cooperation frameworks.

Source: https://www.moneycontrol.com/news/india/n-chandrasekaran-salil-parekh-5-other-indian-ceos-to-be-part-of-trump-reception-at-davos-13781390.html

Technology leaders at the World Economic Forum in Davos 2026 have identified power availability, cybersecurity, and enterprise readiness as the three critical factors that will determine artificial intelligence's successful transition from experimental pilots to large-scale deployment. As AI moves from promise to practice, these infrastructure and operational challenges are becoming the primary bottlenecks for widespread adoption.

AI Enters Production Phase After Years of Experimentation

Jeetu Patel, President and Chief Product Officer at Cisco, emphasized that the AI industry is entering a new maturity phase after extensive experimentation. While companies moved from simple chatbots to more advanced agentic AI systems, the focus is now shifting to production deployment.

"2026 will be the production of agentic AI," Patel stated, noting that early forms of physical AI and large world models will also begin to surface. This transition is driving investment into areas where constraints are becoming increasingly visible.

Power Constraints Emerge as Major Bottleneck

Varun Sivaram, Founder and CEO of Emerald AI, warned that energy shortages will seriously limit AI ambitions in major markets during 2026. The power bottleneck represents a critical infrastructure challenge that could determine competitive positioning in AI innovation.

"They have chips; they need power in 2026," Sivaram explained, highlighting the disconnect between available computing hardware and energy infrastructure. The scale of the challenge is significant, with plans for tens of gigawatts of data center capacity but limited grid connectivity.

Emerald AI, backed by Nvidia, has developed power-flexible AI systems that can adjust electricity consumption in real-time. The company launched what it describes as the world's first power-flexible AI factory in Virginia, allowing data centers to connect to grids faster without increasing electricity costs for surrounding communities.

Enterprise Decision-Making Drives AI Evolution

Chakri Gottemukkala, Co-Founder and CEO of o9 Solutions, highlighted the growing complexity facing enterprises in an increasingly volatile global environment. Companies are seeking AI solutions that can support sophisticated decision-making processes beyond the capabilities of current large language models.

The next evolution involves combining the accessibility of LLMs with structured enterprise knowledge through neuro-symbolic AI. This approach aims to democratize insights by making them available to frontline teams rather than limiting access to specialists and analysts.

Security Concerns Rise with AI Integration

Jonathan Zanger, Chief Technology Officer at Check Point Software, emphasized that many AI solutions were not originally designed with security considerations. As AI systems become more deeply embedded in business operations, security gaps are creating vulnerabilities that attackers can exploit.

"In 2026, we definitely need to double down on investment to ensure AI is adopted securely," Zanger stated. Companies are responding with significantly increased budgets for AI security, with boards and CEOs making it a top priority.

Patel noted that while AI was initially used to strengthen cyber defenses, companies must now focus on securing AI systems themselves. Enterprise applications require predictable and reliable performance, even as AI models grow more complex and unpredictable.

Infrastructure Architecture Adapts to Scale Requirements

The evolution toward large-scale AI deployment is driving significant changes in infrastructure architecture. Power constraints are pushing the development of distributed systems where multiple data centers function as single virtual clusters.

Hyper-scalers are building clusters of hundreds of thousands of GPUs across different locations, connecting data centers where power is available to operate as unified virtual systems. This distributed approach addresses both power availability and scaling requirements as AI models continue to grow in size and complexity.

The convergence of these challenges—power, security, and enterprise readiness—represents a critical juncture for AI adoption. Success in 2026 will depend less on technological experimentation and more on solving practical infrastructure and operational challenges that enable safe, reliable, and scalable AI deployment.