How Will AI Drive Business in the Future? These Three Key Predictions Are Worth Watching

"Today, when we talk about generative AI, it is no longer a distant technological concept—it has become a driving force that is profoundly shaping the future of business logic." Regarding the prospects of AI-driven business, Jiang Liqin shared his three core predictions.

1. AI will evolve from an efficiency tool to the core of enterprise mechanisms.
Currently, generative AI has begun penetrating multiple scenarios across industries as an enabler, but most companies still position AI as a tool for improving efficiency—this is only the prelude to AI commercialization. In the next three to five years, leading enterprises will undergo a fundamental shift from "AI-assisted" to "AI-native," deeply embedding AI into corporate strategy, organizational processes, product development, and even corporate culture, making it the core of their operational systems. Future business decisions will no longer rely solely on executives' experience and intuition but will be based on real-time insights and predictions provided by AI across the entire value chain. Business operations will transition from "process-driven" to "data-and-intelligence-driven."

2. "Human-machine symbiosis" will become the mainstream of future work, redefining talent.
The winners in the future market will be enterprises that achieve the most efficient "human-machine symbiosis." Such companies can perfectly integrate human strategic thinking, creativity, and empathy with AI's computational power, predictive analytics, and knowledge integration capabilities. Multidisciplinary talents with "AI literacy" will become the most sought-after in the job market. These talents must understand AI principles, collaborate with AI, and interpret AI outputs—such as Agent orchestration engineers, data ethics translators, and AI behavior analysts—while also possessing industry-specific expertise and data-driven thinking. Companies must redefine talent profiles, reshape job responsibilities, and accelerate employees' transition from AI awareness to proficiency through systematic training and hands-on projects.

3. As AI technology iterates, earning multi-stakeholder trust will become a company’s most valuable asset.
This trust is multidimensional:

• Customer trust—Will businesses use their data responsibly?

• Employee trust—Is large-scale AI adoption aimed at empowering employees rather than replacing jobs?

• Regulatory trust—Are deployed AI systems and data collection practices fair, transparent, explainable, and risk-controlled?

• Public trust—Do companies adhere to ethical and moral standards when using AI?

Thus, investments in data governance, privacy protection, algorithmic fairness, explainability, and responsible AI frameworks—currently seen as cost centers—will become critical to corporate branding and sustainable growth.