## 4 Future Directions
### Enhanced AI/ML Integration for Autonomous Decision-Making  
Future low-code platforms will increasingly embed artificial intelligence (AI) and machine learning (ML) modules to automate complex workflows [69; 70; 71]. For instance, AI-driven code generation tools, such as SAP Build Code's Joule, enable developers to auto-generate data models and logic flows, reducing manual coding efforts in ERP customization  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Company** & **Application Scenario** & **Manpower Reduction** & **Time Savings** & **Cost Savings** \\ \hline New Oriental (Beijing, China) & WeChat-based study room booking system & 50\% reduction, 80\% backend labor saved via component reuse & 50\% faster delivery, 30–50\% maintenance time reduction & \$72,000 development cost savings, \$50,000+ annual IT budget reduction \\ \hline Mercedes-Benz (Lisbon, Portugal) & Customer complaint management system & 66\% reduction, 50\% development team downsizing & 60\% faster deployment, 50\% third-party collaboration time reduction & \$116,000 annual labor cost savings, \$290,000+ annual loss prevention reduction \\ \hline MDEC (Cyberjaya, Malaysia) & Integration of 50+ legacy government systems & 70\% reduction, 60\% non-technical staff participation & 93\% faster per-application delivery, 10\(\times\) overall efficiency improvement & \$1.6M annual maintenance cost reduction, \$1.4M+ outsourcing cost savings \\ \hline MassHousing (Boston, MA, USA) & Affordable housing loan approval platform & 70\% reduction, 80\% business user involvement & 83\% faster development, 50\% loan processing time reduction & \$375,000 development cost savings, \$3M+ annual revenue growth \\ \hline \hline \end{tabular}
\end{table}
Table 2: Low-code development impact on cost and efficiency [68].  
projects. Academic studies highlight the potential of generative AI to dynamically adapt low-code templates based on real-time user behavior. For example, [PERSON] et al. [55] propose a framework where AI agents analyze historical application usage patterns to recommend optimal component configurations, improving user productivity in IoT device management scenarios. Figure 2 illustrates the temporal milestones of AI and ML adoption across diverse industries. Early pilot projects for AI/ML in quality control and predictive maintenance emerged as early as 2020. These sectors prioritized initial exploration of AI-driven supply chain optimization and ML-based fault detection in 2021, followed by small-scale pilot implementations of intelligent diagnostic systems in 2024, with plans for broader industry-wide adoption by 2025. It demonstrates that while manufacturing and energy sectors have achieved relatively advanced deployment, high-tech industries like aerospace are prioritizing incremental pilot expansions, reflecting diverse strategic approaches to technological innovation.  
However, challenges persist in balancing automation with transparency. Research [1] underscores the need for explainable AI (XAI) in low-code platforms to ensure compliance with regulatory standards, particularly in aerospace and healthcare sectors where decision-making processes require auditability.