How Far Should We Go Away from Smart Contract to Smarter Contractor? A Systematic Review

Abstract

Blockchain technology has emerged as a transformative force across various industries by providing a decentralized, transparent, and secure digital infrastructure. Central to this transformation are smart contracts, which are self-executing agreements that autonomously enforce and execute contractual terms without the need for intermediaries. While smart contracts offer significant advantages in terms of efficiency and security, their inherent rigidity and limited adaptability pose challenges in dynamic and complex environments. This situation prompts the critical question: How Far Should We Go from Smart Contracts to Smarter Contractors? Driven by the necessity to overcome these limitations, this systematic review investigates the evolution of smart contracts into smarter contractors through the integration of artificial intelligence (AI) and machine learning (ML) technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an extensive literature search was performed across multiple academic databases, identifying and analyzing 276 relevant studies published between 2015 and 2024. The analysis, structured around six key research questions, reveals that the incorporation of AI and ML has significantly enhanced the functionality, security, and adaptability of smart contracts throughout their lifecycle. These enhancements include automated code generation, formal verification, real-time monitoring, and adaptive management. Despite these advancements, persistent challenges such as scalability, interoperability, data privacy, and computational overhead continue to hinder the full realization of smarter contractors. Additionally, the advent of Large Language Models (LLMs) has further expanded the capabilities of smart contracts, enabling more sophisticated code generation, vulnerability detection, and intelligent auditing. This review underscores the pivotal role of AI and ML in addressing the limitations of traditional smart contracts, highlighting their transformative impact on the broader blockchain ecosystem and facilitating the development of more advanced and intelligent decentralized applications. Finally, we propose future research directions that emphasize the necessity for standardized frameworks, enhanced interoperability protocols, and robust security measures to support the ongoing advancement of intelligent smart contracts.