Further efforts are needed to build responsible artificial intelligence by promoting technological openness, fostering collaboration and establishing consensus-driven governance to fully unleash AI's potential to boost productivity across various industries, an executive said.

The remarks were made by Wang Lei, chairman of Wenge Tech. Corporation, a Beijing-based AI company recognized by the Ministry of Industry and Information Technology as a "little giant" firm—novel and elite small and medium-sized enterprises that specialize in niche markets. Wang delivered his speech at the recently concluded World Summit on the Information Society.

"AI has made extraordinary progress in recent years. Innovations like ChatGPT and hundreds of other large language models (LLMs) have captured global attention, profoundly transforming how we work and live," said Wang.

"Now we are entering a new era of Artificial General Intelligence (AGI). Enterprise AI has proven to create significant value for customers in fields such as government operations, ESGs, supply chain management, and defense intelligence, excelling in analysis, forecasting, decision-making, optimization, and risk monitoring," he added.

A recent report from the think-tank a16z and IDC reveals that global enterprise investments in AI have surged from an average of $7 million to $18 million, a 2.5-fold increase. In China, the number of LLMs grew from 16 to 318 last year, with over 80 percent focusing on industry-specific applications, Wang noted.

He predicted a promising future for Enterprise AI, with decision intelligence being the ultimate goal. "Complex problems will be broken down into smaller tasks, each resolved by different AI models. AI agents and multi-agent collaboration frameworks will optimize decision-making strategies and action planning, integrating AI into workflows, data streams, and decision-making processes within industry-specific scenarios."

Wang proposed a three-step methodology for successful Enterprise AI transformation: data engineering, model engineering, and domain engineering.

"To build responsible AI, we must address several challenges head-on," he emphasized. "Promoting technological openness can reduce regional and industrial imbalances, fostering collaboration can mitigate unfair usage restrictions, and establishing consensus-driven governance can significantly enhance AI safety."