AI Governance

Nearly 80% of companies report no significant bottom-line impact from their investments in generative AI. This is a striking figure given the billions spent and the countless pilots launched. Despite the excitement around AI, four out of five organizations struggle to turn their AI efforts into real business value. So, what sets the successful 20% apart from the rest? After working with enterprises across various industries on AI strategy and implementation, clear patterns em

A recent survey revealed a striking statistic: 76% of executives now see agentic AI as a coworker rather than just a tool. This shift is more than a change in wording. It signals a new way organizations think about AI’s role in the workplace. This change affects how managers lead their teams and interact with AI systems. AI system collaborating with a human worker The Difference Between Tools and Coworkers Understanding the difference between tools and coworkers is essential

The gap between AI experimentation and real business value has never been clearer. Recent research shows that 65% of organizations are actively experimenting with AI agents, yet fewer than 25% have successfully scaled them into production. This gap highlights a major challenge in enterprise technology today. If 2025 was the year when everyone talked about AI agents, 2026 is the year businesses began asking a tougher question: Is any of this actually working? The Shift from Pr

Network Protocols The rise of AI agents is reshaping how software systems interact, but it also creates a familiar challenge: how do we enable different AI systems to communicate effectively? In the early internet days, HTTP became the standard that allowed web servers and browsers to exchange information smoothly. Today, as AI agents multiply across industries, similar standards are essential to avoid chaos and fragmentation. Two protocols are emerging as key players in this