大型語言模型智慧合約生成可靠性不足 企業應建立完整風險管控機制

Winners Consulting Services Co., Ltd., based on analysis of recent research, points out that while Large Language Models (LLMs) show potential in code generation, their reliability in smart contract development is far below the standards required for enterprise commercial applications. Research indicates that LLMs cannot perfectly execute key functions like process control, resource allocation, and data condition evaluation. Enterprises must establish comprehensive risk assessment and business continuity management systems when adopting such emerging technologies to ensure digital transformation does not impact core operations.

Research Background and Core Arguments

This groundbreaking study delves into the practical application of Large Language Models in smart contract code generation, revealing key bottlenecks in current technological development. The research team found that while traditional rule-based code generation methods are more stable, they lack flexibility. In contrast, emerging LLM technology can handle more complex business process descriptions but has significant reliability flaws. The study employed an automated evaluation framework to test a large-scale dataset, moving beyond the limitations of previous methods that relied on small-sample manual checks or compilation tests. The results show that existing LLMs fail to achieve the required 100% accuracy when converting business processes into smart contracts, performing particularly poorly in process execution order, resource allocation logic, and conditional judgment accuracy. This finding serves as a critical warning for enterprises undergoing digital transformation, highlighting the need to establish robust risk control mechanisms while adopting new technologies.

Key Findings and Quantitative Impact

The research revealed the specific limitations of LLM technology through large-scale dataset testing, finding that even the most advanced models fail to meet the 99.9% reliability standard required for enterprise-level applications in smart contract generation. Tests covering LLMs of different sizes and types showed that all models had significant accuracy issues when handling complex business processes. Specifically, the error rate in resource allocation tasks was as high as 15-25%, and the failure rate in data condition evaluation reached 20-30%. These figures clearly indicate that enterprises risk severe operational and financial losses if they hastily adopt LLM-generated smart contract code. The full research report emphasizes that since smart contracts are typically immutable once deployed on a blockchain, any code error can lead to irreversible losses. The study recommends that enterprises adopt a hybrid approach over the next 3-5 years, combining the stability of traditional methods with the innovation of AI, while establishing multi-layered validation mechanisms to ensure code quality.

Practical Application of the ISO 22301 Framework

The ISO 22301 Business Continuity Management standard provides a comprehensive risk control framework for enterprises adopting emerging technologies. The standard requires organizations to establish a systematic Business Impact Analysis (BIA) process to identify critical business processes and technological dependencies, which is crucial for assessing the risks of implementing LLM-generated smart contracts. According to ISO 22301, enterprises should create a complete system covering technology assessment, risk analysis, response planning, and continuous monitoring. In the technology assessment phase, a 60-90 day proof-of-concept test is needed to evaluate the LLM's performance in specific business scenarios. The risk analysis phase should quantify the potential impact of technology failure on operations and establish risk tolerance standards. The response plan must include backup options using traditional programming to ensure a quick switch if the AI technology fails. Furthermore, the ISO 27031 guidelines for ICT readiness for business continuity require organizations to establish technology resilience assessment mechanisms to regularly review the stability and reliability of new technologies. By integrating these standards into a Business Continuity Plan (BCP), companies can effectively mitigate the risk of operational disruption during technological transformation, ensuring business stability while reaping the benefits of innovation.

Winners Consulting Services' Perspective: Actionable Advice for Taiwanese Enterprises

Based on Winners Consulting Services Co., Ltd.'s years of practical experience advising Taiwanese enterprises, we recommend a three-stage, gradual strategy for adopting LLM smart contract technology. The first stage is a six-month technology evaluation period, where a dedicated cross-functional team—including representatives from IT, legal, risk management, and business departments—should be formed to analyze existing business processes and identify low-risk scenarios suitable for AI-assisted development. The second stage is a 12-month pilot implementation, selecting 2-3 non-critical business processes for LLM-assisted smart contract development, while establishing a dual validation mechanism combining manual review and traditional development. The third stage is a pre-full-scale implementation risk assessment, requiring at least 18 months of pilot experience to create a comprehensive performance evaluation report and risk control standards. A key consideration for Taiwanese enterprises is the potential compliance risk of directly adopting foreign-developed LLM models due to the unique local regulatory environment and business practices. We advise collaborating with professional consultants to establish customized validation processes that comply with Taiwanese regulations, ensuring generated smart contracts align with the local commercial legal framework. Concurrently, companies should invest in cultivating their internal teams' understanding of AI technology to build long-term technical management and risk control capabilities.

Frequently Asked Questions

Enterprises often face several key questions when evaluating the adoption of LLM smart contract generation technology. The first concerns technology maturity, with many executives wondering if now is the right time to invest. Research indicates that while current LLM technology shows promise, its reliability is still insufficient for critical business applications. A cautious, wait-and-see approach combined with small-scale pilot testing is recommended. Second is the cost-benefit analysis; companies need to assess the total cost of ownership for LLM adoption—including technology licensing, personnel training, system integration, and risk control—and compare it with traditional development methods. A third common issue is talent development strategy. Enterprises must cultivate hybrid talent proficient in both AI technology and smart contract development, which typically requires a 6-12 month professional training period. Finally, regulatory compliance is a major concern, especially in highly regulated industries like finance and healthcare. Companies must ensure that AI-generated contracts meet relevant legal requirements and should work closely with their legal departments to establish compliance review mechanisms. Enterprises should develop an adoption strategy and timeline tailored to their specific industry, technical capabilities, and risk tolerance.