Convergence transformation The synergy of AI and omni compute enables firms to innovate beyond their conventional boundaries, pursuing higher returns by tapping into emerging markets while addressing the increasing needs of computational power on devices. Companies that primarily operated within a software-focused value chain are expanding into hardware solutions capable of maintenance, predictive analytics and healthcare monitoring. Conversely, the integration of software into advanced systems is creating a new class of products. For example, Waymo is disrupting the automotive industry with its custom foundation model that incorporates multimodal sensor data (LiDAR, radar and camera) into its vehicles. A clear shift is under way in investment patterns: from hype-cycle-driven funding to strategic investments in AI convergence platforms that integrate multiple technologies. In 2024, AI dominates venture capital flows, and while investor uncertainty remains, the convergence of AI with automation, robotics and edge computing is a recurring theme in the fastest- growing technology sectors. This trend is reflected in strategic investments by major players such as Intel, NVIDIA and SoftBank, as well as public initiatives like the European Union’s funding for edge-AI tech projects.11 As AI components become increasingly sophisticated, investment is flowing towards maturity-driven technologies that enable the development of integrated, real-world solutions and accelerating the next wave of intelligent systems. The rapid evolution and widespread adoption of AI have further driven global governments to accelerate the development of regulatory frameworks12 to manage its impacts effectively. The European Union’s AI Act, now in effect, serves as a significant precedent for comprehensive AI regulation, laying down harmonized rules to govern the development and deployment of AI systems. The US, Canada, Brazil, the Association of Southeast Asian Nations (ASEAN), Japan and China are also developing their own regulatory approaches. Governments are increasingly focused on establishing national AI champions and AI skills hubs, and building national AI strategies to strengthen their competitive positions in the global AI landscape. CASE STUDY 4 Anthropic – Model Context Protocol Anthropic has developed an open-source Model Context Protocol (MCP) to transform how AI models interact with external data and tools. As LLMs scale, traditional deployments face growing complexity in context management, such as information silos, limited data accessibility and poor interoperability. MCP addresses these challenges by structuring how information flows into and between models, especially in multi-agent or tool- rich environments. MCP improves efficiency by enabling one-time integrations that work across multiple platforms, saving time and resources. It also enhances context awareness for smarter assistants, real-time processing and coordinated tool use.Open standard for complex agentic workflow Because an MCP client can connect to multiple servers at once, an AI agent can combine tools. For instance, an AI operation agent might use one MCP to monitor equipment status, another to access data from ERP system and a third to schedule maintenance, all within a single conversation. This enables improved decision-making and execution. Shared workspace for collaborating agents Specialized AI agents focused on research, planning or execution can use MCP to dynamically exchange information and coordinate tasks in real time. By accessing a shared toolset through MCP , agents eliminate the need for direct system integrations, enabling faster collaboration, greater modularity and scalable orchestration across complex workflows. Technology Convergence Report 20

Technology Convergence Report 2025