Artificial intelligence (AI) is transforming various sectors by enhancing operational efficiency, offering actionable insights, and fostering data-driven decision-making. In the Philippine economy, where small and medium-sized businesses (SMBs) account for a significant portion, the Ateneo de Manila University’s Business Insights Laboratory for Development (BUILD) is spearheading research to ensure that AI serves to augment human labor rather than replace it. This approach recognizes the pivotal role that small businesses play and seeks to introduce AI tools that can seamlessly integrate with existing practices.
A central focus of BUILD researchers includes the traditional pen-and-paper logbook, a simple yet reliable tool for record-keeping across various businesses. While these physical records are favored for their low cost and reliability, particularly in dynamic environments such as food stalls and convenience stores, they present substantial challenges when it comes to data analysis. Handwritten logs can be tedious to read, analyze, and interpret, creating obstacles for SMB owners who wish to utilize data to optimize operations.
In stark contrast, AI technologies excel in data analysis and offer capabilities that empower business owners to gain valuable insights efficiently. By automatedly identifying sales trends, evaluating product performance, and providing recommendations on inventory management, AI can enable businesses to adapt more quickly to consumer demands. However, there exists a palpable hesitance among SMB owners to embrace digital transformation due to concerns surrounding the complexity of new technology and the potential threat to jobs.
BUILD researchers propose a model that addresses these concerns through a “copilot” framework. In this vision, AI acts as a facilitator, augmenting human efforts rather than taking over. Their research, which was presented at the Artificial Intelligence in Human-Computer Interaction Conference 2025 in Sweden, explores how technologies such as optical character recognition (OCR) and large language models (LLMs) can convert handwritten sales logs into actionable digital data.
The implementation of this system was successfully tested in an actual food stall at the Ateneo’s Student Enterprise Center. By utilizing Python for system development alongside Amazon Web Services for OCR and Anthropic’s Claude 3 Haiku LLM for data interpretation, the solution can democratize access to critical business metrics. Individuals lacking extensive digital training can easily leverage the AI system to understand inventory trends, scan logbook images, and receive sales summaries tailored to their business needs.
When evaluating tools for automation and data integration, platforms like Make and Zapier come to mind as potential options for SMBs. Make, known for its flexibility and powerful workflow automation capabilities, allows users to create complex scenarios without compromising ease of use. In contrast, Zapier excels in its user-friendly interface, making it incredibly accessible for non-technical users. However, as operational requirements grow, the limitations of these platforms emerge. For instance, Make may allow for more intricate connections and integrations but demands a steeper learning curve. Conversely, while Zapier’s straightforward approach rapidly empowers users, it might also lead to higher costs for extensive integrations over time.
The financial implications of adopting AI or advanced automation tools cannot be overstated. Typically, a modest initial investment in technology can yield substantial returns. For instance, automating data entry tasks drastically reduces labor costs and minimizes human error, thereby boosting overall productivity. In the context of AI applications at small businesses like the food stall, the potential return on investment (ROI) can be significant, as analyzing consumer preferences leads to more informed inventory decisions, ultimately translating to increased sales.
Scalability is another critical factor when selecting a digital solution. A tool effective for small businesses should easily adapt to an expanding operational scale. The AI model developed by BUILD is designed to evolve as technology improves, thus ensuring that SMBs can rely on increasingly accurate insights while maintaining a cost-effective structure. This adaptability contrasts with many traditional automation platforms that may require complete overhauls as business needs change.
In conclusion, for SMB leaders contemplating digital transformation, the emerging tools present a compelling opportunity—not merely to digitize records but to unlock invaluable insights through AI integration. The examples provided demonstrate clear strategies in selecting platforms based on strengths and weaknesses, while also considering initial costs and future scalability. By adopting an AI-supported copilot model, businesses can continue leveraging traditional methods while also benefiting from the powerful capabilities that modern technology offers.
FlowMind AI Insight: Emphasizing a copilot approach allows small businesses to transition into the digital age without sacrificing the core values of human labor, meaning they can maintain their operational heritage while enhancing efficiency. As AI tools evolve, they promise not only to provide insights once thought accessible only to larger players but also to democratize business intelligence for all SMBs.
Original article: Read here
2025-09-02 13:16:00