Bairong YunChuang Promotes the Integration of Decision AI and Generative AI
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Since 2006, when scientists proposed deep learning algorithms for neural networks by simulating human neurons, decision AI has rapidly expanded across various fields. As a leader in AI cloud services, Bairong YunChuang has been introducing decision AI technology into vertical industries since its inception.
The deeper Bairong YunChuang delves into industries, the more it realizes that when discussing AI, the focus should actually be on economics. The high technical barriers and substantial economic costs have made the industrialization of AI a bumpy road, leading to a lukewarm response from the industry at times.
Facing this situation, Zhang Shaofeng, CEO of Bairong YunChuang, believes that decision AI demonstrates powerful capabilities in perception, reasoning, and decision-making, which will undoubtedly bring about a significant technological revolution. However, a new business philosophy and methodology are needed to unlock this technological potential.
He argues that decision AI is no longer just a technology or a one-time transaction; it cannot be treated as a typical engineering product. For example, Microsoft initially sold Office software by charging for each product sold, representing the second industrialization of technology products. However, this approach is unsuitable for decision AI, which has high technical barriers and costs. To widely embed decision AI into industries, the entire business model must be transformed, moving away from selling one model per scenario and adopting a service-oriented approach to advance decision AI into the tertiary industry.
Driven by this philosophy, Bairong YunChuang pioneered the MaaS (Model as a Service) model in the financial sector, building on decision AI. By leveraging the paradigm of 'large-scale pre-training + fine-tuning,' Bairong YunChuang effectively integrates technologies such as large models, natural language processing, intelligent voice, and knowledge graphs. Through upstream training, it develops highly generalized models, providing industrial clients with services like model training, model invocation, and model deployment.
Bairong YunChuang is like training a skilled musician for its clients—someone who can play beautiful music with minimal practice on different scores, without needing to learn the basics from scratch. This significantly transforms the business model of the AI industry. Traditionally, building a complex AI model required companies to follow a lengthy process: 'data processing - environment setup - model training - model optimization - model deployment - production application.' Under the MaaS model, this process is simplified into three steps: 'large model - digital intelligence tools - application scenarios,' leading to a qualitative improvement in modeling efficiency.
Industrial clients no longer need to worry about hardware or underlying technical details. Instead, they can focus on business logic, using APIs to call ready-made model products for direct industrial applications or fine-tuning Bairong YunChuang's large models to create their own products. In the financial sector, both large and small banks can easily adopt this approach without additional learning costs.
The results speak for themselves. In less than a decade, Bairong YunChuang has delivered AI capabilities to over 7,000 clients, with invocations reaching tens of billions.
However, Zhang Shaofeng clearly recognizes that from a macro perspective, decision AI has only kicked off the first half of the AI era, completing partial tasks. For industries, decision AI primarily operates in intermediate workflow stages, serving industrial clients rather than end-users. Taking the financial sector as an example, BaiRong YunChuang uses decision AI to help financial institutions analyze hundreds of thousands or even more clients in real-time, aiding their decision-making. In one bank collaboration, it reduced approval times from several hours to just a few minutes.
In marketing scenarios, BaiRong YunChuang provides financial institutions with scientific recommendations through decision AI, such as whether to reach users via app notifications, SMS, or in-person services. However, whether it's user analysis or marketing strategy formulation, the final actions depend entirely on the financial institutions' preferences. Essentially, BaiRong YunChuang's decision AI offers only decision references, empowering intermediate processes with indirect impacts on business outcomes.
The rise of generative AI technologies, represented by large models and AIGC, has filled the final piece of the puzzle for decision AI. The greatest advantage of generative AI lies in its powerful interaction capabilities—whether through voice, text, or video—enabling AI to directly engage with a vast number of individual users.
BaiRong YunChuang combines decision AI and generative AI, extending AI capabilities from intermediate workflows to final outcomes, making AI usable, visible, and tangible in vertical industries. For instance, in wealth management, an offline account manager can only handle a limited number of clients, leaving over 90% of clients unreached. BaiRong YunChuang uses decision AI for data-driven insights to develop comprehensive marketing and operational strategies, then leverages generative AI to create text and voice interactions for deeper engagement. In collaboration with a major state-owned bank, under the synergy of decision AI and generative AI, an account manager can now send over 3,000 personalized messages and conduct more than 10,000 in-depth conversations daily in under 60 minutes, providing 'tailored' services to previously overlooked long-tail customer segments.
The perfect pairing of decision AI and generative AI means vertical industries can now harness both powerful analytical capabilities and content generation.