Global AI spending is poised for remarkable growth as businesses shift towards large-scale enterprise adoption of artificial intelligence technologies. This shift was underscored during the IDC Directions 2026 conference in Beijing, where over 400 industry leaders, investors, and experts gathered to discuss the evolving landscape of AI.
Shifting Landscape of AI Adoption
The conference highlighted a transformative period for AI, moving away from a focus on infrastructure development to a pronounced emphasis on application and enterprise integration. Lorenzo Larini, CEO of IDC, remarked on the unprecedented pace of change: “IDC has been on the ground here since 1986, and we’ve never seen the pace of change move faster than it is right now.”
Entering the AI Supercycle
Discussions at the event centred around what experts are calling the AI supercycle. Projections indicate that global enterprise spending on AI is expected to reach $940 billion by 2026 and soar to $2.1 trillion by 2029. Kitty Fok, an industry analyst, stated, “The global AI industry has entered a super cycle, and the market is now moving from infrastructure build-out to enterprise application explosion.”
Robotics and Automation Growth
This transition is reflected in the fast-growing robotics and automation sectors. Spending on embodied intelligence, for instance, is set to leap from $1.4 billion today to an astonishing $77 billion within five years, translating to an annual growth rate of 94%. As industries embrace automation, the landscape of work and productivity is evolving rapidly.
Transforming Enterprise AI Costs
The cost structure of enterprise AI is also undergoing a significant transformation. The emergence of the “token” as a critical metric for cost and value is reshaping how businesses assess AI investments. Zhenshan Zhong explained, “Tokens are the core of cost, and agents are the core of value,” highlighting a shift from merely generating content to creating execution-based systems that drive operational results.
Model-as-a-Service and Generative AI Integration
The Model-as-a-Service market is expected to grow dramatically, with forecasts predicting it will reach 40,000 trillion token calls by 2026, generating around 18.6 billion renminbi in revenue. Notably, over 60% of leading enterprises have already integrated generative AI into their core business processes, signifying a broad shift towards AI-native operations.
Efficiency as a Competitive Edge
Experts emphasise that efficiency is becoming a crucial differentiator in AI. Traditional computing benchmarks are being replaced by more relevant measures, such as “tokens per watt.” Thomas Zhou noted that by 2027, inference is anticipated to account for over 70% of intelligent computing demand, while edge infrastructure is set to grow faster than centralised data centres.
Strategic Evolution in the Digital Economy
The strategic landscape is also evolving, with digital economy policies focusing on innovation, ecosystem development, and long-term capability building. Lianfeng Wu highlighted that businesses are increasingly turning to platform-based growth and developer ecosystems to stay competitive in this new phase of expansion.
Industrial AI and Full-Scale Deployment
Industrial AI is emerging as a cornerstone of this transition, moving from pilot projects to full-scale deployment across production, supply chains, and decision-making systems. Kai Cui explained how modern industrial software is now integrating perception, prediction, and collaborative execution, enabling comprehensive transformation across the value chain.
Consumer-Level Changes and Market Dynamics
At the consumer level, the evolution of smart devices is reshaping market dynamics, with buyers increasingly prioritising intelligent experiences over mere hardware specifications. Shipments of smart devices are projected to reach approximately 900 million units by 2026, although challenges related to component supply constraints remain a concern.
The Road Ahead for Global AI
Antonio Wang noted that AI-native devices signify a shift in how value is distributed across ecosystems, moving beyond a simple upgrade cycle. The discussions at the IDC conference indicate a structural transformation of the global technology landscape, where adaptability, efficiency, and ecosystem integration will be pivotal for long-term leadership.
