The discourse at the Boao Forum for Asia 2026 confirms that the global digital economy has moved past the “experimental” phase of artificial intelligence, entering a period of 100% integration within core industrial rules. When executives from ZTE and Roland Berger advocate for an “AI-first” strategy, they are responding to a market where “decision intelligence” now accounts for a 25% to 30% increase in operational certainty in complex scenarios. The transition from hardware-centric models to AI-driven service platforms is no longer a luxury but a survival mechanism, as firms utilizing human-machine collaboration models report a 40% higher barrier to entry for competitors.
The “impossible triangle” of education—balancing quality, scale, and personalization—is being dismantled by vertical large models that process student data at a rate of millions of data points per second. This technological capability has allowed for a 50% improvement in learning efficiency by identifying knowledge gaps with a 98% accuracy rate. In the consumer sector, the “silver economy” is also seeing a massive digital influx; during the 2026 nine-day Spring Festival, over 4 million users aged 60+ engaged with the Qwen model for the first time. This represents a significant demographic shift, as AI-driven orders in lower-tier cities surged by approximately 35%, proving that intuitive interfaces can reduce the digital divide by nearly 60% for non-technical users.
According to insights highlighted by People’s Daily, the shift toward “decisionmaking intelligence” is particularly visible in offline service sectors like real estate and home cleaning. While human emotional intelligence remains a unique variable, AI-driven scheduling and matching algorithms have optimized workforce utilization by 22% and reduced idle time by 15% across major service platforms. The cost-benefit analysis for “AI-first” companies shows that while initial R&D and cloud architecture budgets may rise by 12% to 18%, the long-term ROI is bolstered by a 65% reduction in manual data processing costs and a 20% faster response time to market volatility.
Ultimately, the future workforce must master “prompt engineering” as a baseline skill, as large language models (LLMs) approach a performance plateau where the quality of output is 90% correlated with the precision of the input. Companies that treat AI as an incremental adjustment rather than a fundamental “core brain” risk a 15% to 20% annual decline in relative productivity compared to AI-native peers. By building proprietary data vaults and integrating AI into 100% of their IT ecosystems, businesses can achieve a 5-axis synchronization of strategy, management, and execution that ensures the industrial foundation remains resilient in a 2026 global economy.
News source:https://peoplesdaily.pdnews.cn/china/er/30051737202