Operationalizing AI: Scalable Strategies for SME Operations Management in the Philippines

Artificial Intelligence (AI) is transforming how businesses streamline operations, reduce costs, and deliver value—but in the Philippines, many small and medium enterprises (SMEs) struggle to translate these possibilities into real operational outcomes. While AI tools can optimize inventory, enhance logistics, and automate repetitive tasks, many SMEs still face barriers in accessing, integrating, and sustaining these technologies. This gap is especially concerning for operations managers, who are at the frontline of managing workflow, performance, and continuous improvement. As AI becomes a defining feature of operational excellence globally, it is crucial to develop context-specific strategies that enable Philippine SMEs to adopt AI in ways that are scalable, inclusive, and grounded in operational realities.

Who Can Use These Topics

This research is ideal for students and professionals pursuing the following courses or strands:

College Programs:

• BS in Business Administration major in Operations Management

• BS in Industrial Engineering

• BS in Management Accounting

• BS in Innovation and Technology Management

• BS in Entrepreneurship

Senior High School Strands:

• Accountancy, Business, and Management (ABM)

• General Academic Strand (GAS)

Why This Topic Needs Research

This research is necessary because existing studies have not adequately addressed how AI tools can be integrated into the day-to-day operational management of Philippine SMEs. Key gaps include:

• No comprehensive models for AI in SME operations: Although Hernandez et al. (2023) showed how select SMEs use AI for sustainability, they emphasized the need for larger-scale studies that analyze how AI addresses persistent operational issues such as infrastructure limitations and workflow efficiency.

• Unclear strategies for cross-sector AI alignment in operations: Rosales et al. (2020) discussed AI’s socioeconomic impact but did not provide models that connect AI readiness with operational practices across sectors. There’s a lack of guidance on how AI can improve resource planning, service delivery, and production efficiency across varied operational environments.

• Lack of frameworks for rural and underserved SMEs: Quimba et al. (2024) found that AI diffusion remains weakest in rural areas due to poor infrastructure and training. However, no research has yet proposed specific operational strategies that SMEs in these regions can realistically implement to leverage AI tools in logistics, procurement, or inventory management.

• Insufficient study of AI’s role in SME process innovation: Hernandez et al. (2024) studied AI and innovation among startups, but did not explore how AI enhances operations such as quality control, lead time reduction, or order processing—especially in traditional industries like agriculture and retail.

• Limited data on AI tool integration into SME workflows: Sarker et al. (2024) noted the absence of empirical studies showing how AI tools like chatbots, predictive analytics, and intelligent scheduling systems are integrated into SME operations. Without this, there is no baseline for assessing operational impact or return on investment.

• Need for adoption strategies tailored to SME operations teams: Hai and Tien (2025) explained the benefits of AI in business processes but pointed out that operational teams in SMEs often lack the resources and training to manage integration. Research must explore scalable adoption models that consider limited budgets, workforce readiness, and system compatibility.

• No operations-specific frameworks for SMEs in retail and services: Sagio et al. (2025) explored AI in retail but didn't provide frameworks showing how SMEs with minimal capital and digital maturity can embed AI in store management, customer service, or supply chain tracking.

Feasibility & Challenges by Target Group

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References

Hai, D. M., & Tien, N. H. (2025). Improved Business Operations due to Artificial Intelligence. International Journal of Advanced Multidisciplinary Research and Studies. 2025b, 5(1), 56-63.

Hernandez, A. A., Caballero, A. R., Albina, E. M., Balmes, I. L., & Niguidula, J. D. (2023, May). Artificial Intelligence for Sustainability: Evidence from select Small and Medium Enterprises in the Philippines. In 2023 8th International Conference on Business and Industrial Research (ICBIR) (pp. 818-823). IEEE.

Hernandez, A. A., Sacdalan, R. V., Sopoco, C. T., Villapando, J. E., & Garcia, P. M. (2024, February). Predicting the Innovation Capability for Firm Performance: the role of Artificial Intelligence in the Philippines Startups Perspective. In 2024 16th International Conference on Knowledge and Smart Technology (KST) (pp. 195-200). IEEE.

Quimba, F. M. A., Moreno, N. I. S., & Salazar, A. M. C. (2024). Readiness for AI adoption of Philippine business and industry: The government's role in fostering innovation-and AI-driven industrial development (No. 2024-35). PIDS Discussion Paper Series.

Rosales, M. A., Jo-ann, V. M., Palconit, M. G. B., Culaba, A. B., & Dadios, E. P. (2020, December). Artificial intelligence: the technology adoption and impact in the Philippines. In 2020 IEEE 12th international conference on humanoid, nanotechnology, information technology, communication and control, environment, and management (HNICEM) (pp. 1-6). IEEE.

Sagio, I., Pramesworo, I. S., & Ekasari, S. (2025). ARTIFICIAL INTELLIGENCE IN THE RETAIL SECTOR: MARKET AND BUSINESS MODEL TRANSFORMATION. INTERNATIONAL JOURNAL OF ECONOMIC LITERATURE, 3(4), 133-146.

Sarker, M. S., Khan, F. S., & Roon, S. L. (2024). The Impact of Artificial Intelligence (AI) on Business Operations in Bangladesh.

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