As digital transformation deepens, artificial intelligence (AI), especially generative AI (Gen-AI), has become a key tool for enterprises to enhance efficiency, optimize decision-making, and improve customer experiences. However, despite the immense potential of AI technology, enterprises face numerous challenges in its practical application, which may hinder the progress of digital transformation.
Challenges in Gen-AI Application
Limitations of Large Language Models (LLM) Large language models (LLMs) are at the core of generative AI. However, their construction method and the limitations of their training data often result in the generation of erroneous information, undermining their reliability. For example, since LLMs are trained on vast amounts of text data that may not fully align with the specific needs or industry context of enterprises, the output may be inaccurate or lacking in practical value.
Lack of Industry-Specific Expertise While general-purpose LLMs can handle a variety of tasks, they typically lack deep knowledge of specific industries and fail to meet the unique needs of businesses. Especially for companies dealing with complex industry rules or highly customized requirements, general models are often inadequate. While customized models can address this gap, the cost of developing and maintaining them can be prohibitively high, imposing a significant economic burden on enterprises.
Weak Performance in Complex Reasoning and Multi-Tasking In real-world business operations, tasks are often complex and have multiple objectives. Current LLMs often struggle with complex reasoning and multi-tasking, making them less effective in meeting the actual needs of enterprises. For scenarios requiring in-depth analysis and multi-dimensional decision-making, LLMs often fail to deliver the desired results.
Challenges in Implementation Successfully implementing LLMs requires not only powerful computing resources and intelligent algorithms but also strict data management and rule adherence. Any mistake in one of these areas could result in system failure, disrupting normal business operations. For most enterprises, the lack of sufficient technical resources and capabilities to address these challenges results in slow progress and escalating costs during AI system implementation.
Shortage of Cross-Disciplinary AI Talent During digital transformation, enterprises commonly face a widespread problem: the lack of AI experts who possess expertise in data, algorithms, engineering, and business. This talent gap leads to slow progress and high costs in AI adoption, making it difficult for companies to fully leverage the potential of AI technology.
LBAI’s Innovative Solutions
To address these challenges, LBAI has proposed innovative solutions that help enterprises overcome the difficulties they encounter in applying Gen-AI, thereby accelerating their digital transformation process.
Multi-Agent System LBAI solves the limitations of LLMs by adopting a multi-agent system, where each agent is responsible for a specific task. These agents collaborate and cross-verify each other, reducing errors and enhancing system reliability. The collaboration of multiple agents not only improves overall efficiency but also ensures the accuracy and consistency of the information, avoiding the biases that a single model might introduce.
Personalized Token Generator LBAI's personalized token generator deeply understands enterprise data and business needs. By generating customized tokens, it ensures the consistency of context and information. This approach significantly enhances the model’s ability to comprehend enterprise data and prevents misinterpretation or loss of context.
Industry Knowledge Base and Deep Customization To meet the unique needs of businesses, LBAI has built an industry-specific knowledge base and provides deep customization services. By embedding industry-specific knowledge into the model, LBAI can provide more accurate and relevant AI services tailored to each industry, enhancing the effectiveness of digital transformation efforts.
Scheduler and Semantic Parsing LBAI’s scheduler performs semantic parsing of complex tasks and assigns sub-tasks based on their characteristics. Through multi-agent collaboration, multiple sub-tasks are processed efficiently, ensuring that complex business tasks are completed successfully. This allows enterprises to effectively tackle multi-faceted challenges and streamline their operations.
One-Stop Solution LBAI offers a one-stop solution that greatly simplifies the implementation of AI applications and reduces the need for extensive hardware resources. By integrating various functional modules, enterprises can rapidly deploy AI systems with lower IT infrastructure demands, significantly reducing the cost of digital transformation.
User-Friendly Interface and Automation Tools LBAI aims to lower the technical threshold by providing user-friendly interfaces and automation tools that make it easy for non-technical personnel to use AI systems. This not only reduces dependency on AI experts but also enables more enterprises to quickly adopt AI technology and improve operational efficiency.
Intelligent Tools and Professional Support LBAI offers intelligent tools and platforms that lower the professional barrier to AI adoption while also providing professional support services to assist businesses from needs analysis to solution implementation. Automated model training and deployment processes reduce the reliance on top-tier AI experts, speeding up the digital transformation process.
LBAI's innovative methods successfully address the challenges enterprises face when applying Gen-AI, providing strong support for their digital transformation efforts. Through the use of a multi-agent system, personalized services, industry knowledge base, semantic parsing, and automation tools, LBAI helps businesses overcome technical hurdles, enhance operational efficiency, and lower implementation costs. As these innovative solutions are more widely adopted, enterprises will be able to move toward a smarter, more efficient, and safer future, embracing new opportunities in digital transformation.