A Unified AI-Agent Framework for Cross-Industry Optimization in Small and Medium Enterprises (SMEs)

Small and medium-sized businesses (SMEs) are the backbone of the majority of economies, playing an important role in employment, innovation, and regional development. Their capacity to stay competitive is always threatened by operational inefficiencies like frequent machine breakdowns, error-prone and manual scheduling, inconsistent quality control procedures, and demand-supply mismatches. Although AI-based approaches have shown outstanding performance in the large-scale sectors, the majority of systems are specialized in their focus areas of application and require high technical skill or infrastructure costs, conditions that are not commonly available to SMEs. Therefore, it is an urgent necessity to bridge the gap of creating cross-industry, resource-consumer-friendly AI frameworks that can be rescaled and reused by SMEs irrespective of their industry vertical. This research envisions an integrated AI-agent platform that encompasses four fundamental intelligent modules, including predictive maintenance, demand forecasting, computer vision based quality control, and intelligent scheduling. Each agent has to be able to function autonomously but synergistically in an orchestrated architecture that facilitates interoperability, scalability, and real-time adaptability. The framework is tested with a combination of actual pilot data from SMEs in electronics, furniture, and textiles, and open-source and synthetic datasets to mimic varied industry environments. Outcomes show quantifiable improvements on several fronts: minimizing downtime by nearly 20%, accuracy of defect detection being over 90%, forecasting accuracy of demand with MAPE ≤ 12%, and efficiency gains in scheduling of about 18%. Aside from technical verification, this paper highlights the scalability and universality of AI adoption among digitally under-resourced SMEs. Through its provision of a modular, general-purpose solution, the framework fills the AI research-to-industry takeup gap, allowing SMEs to undertake digital transformation at an affordable and sustainable cost. The results position this method as a policy blueprint for policymakers, tech providers, and SME clusters to support inclusive Industry 4.0 uptake on a global level.