In the customer service industry, customer feedback provides rich insights that can benefit BPO companies seeking to continuously improve the processes and services for end customers. However, these insights are often underutilized due to the lack of efficient analysis solutions. Traditional methods are manual, time-consuming, and prone to human error, resulting in an average of 480 seconds of scrubbing time per comment.

This capstone aims to develop an AI-powered solution to automate and enhance the analysis of each DSAT comment received and classify it into the DSAT attributes present in the customer’s comment using Microsoft Power Platform. The solution incorporates features such as sentiment analysis, and multiple custom RAG-based prompts that power the AI automated classification process.

The testing results show a 96.85% reduction in processing time from 480 seconds to 15.1 seconds per comment, significantly reducing the scrubbing time and improving the leader’s accessibility to the insights. This enables faster execution of action plans to address the opportunities identified by the solution. The AI-driven DSAT VOC scrubbing solution promotes scrubbing at scale, where BPO companies can continuously harness unstructured feedback data to drive service delivery, customer satisfaction, and innovation.

This study examined how AI-driven microlearning would shape the leadership development of newly promoted managers in fully virtual consulting firms over the next 2 to 3 years. Grounded in technology foresight, the study applied Prof. Glen Imbang’s Ten-Stage Scenario Building Model to explore multiple AI adoption pathways and their organizational implications. The scenarios address key uncertainties such as AI trust, regulatory constraints, and the balance between automation and human mentorship. The study identified key predictable variables, critical uncertainties, and scenario logics that shaped four distinct scenarios: AI-Optimized Leadership Development, Human-AI Hybrid Model, AI Hesitation and Virtual Leadership Gaps, and AI Adoption with Compliance Bottlenecks. The findings highlighted that while AI-based microlearning enhances training scalability and personalization, its effectiveness depends on cultural acceptance, governance frameworks, and alignment with leadership models. The study contributed a strategic foresight framework to guide virtual consulting firms in integrating AI for leadership development while preserving human-centered capabilities essential to long-term organizational success.

This study explores the integration of Battery Energy Storage Systems (BESS) into a proposed onshore wind energy project in Mindoro Province using technology foresight. With Mindoro’s continued energy insecurity and off-grid status, the study positions wind-BESS systems as a strategic response. Using ten-stage scenario-building, the research identifies key predictable variables and critical uncertainties that influence BESS adoption in the wind energy project.

The study relies on qualitative research methods, with insights from stakeholders across the renewable energy value chain, including project developers, transmission engineers, sustainability consultants, and policy advisors. Through structured environmental scanning (PESTLE), stakeholder surveys, and scenario building, two distinct scenarios were developed: one where Mindoro emerges as a renewable energy hub exporting to the national grid, and another where localized deployment prevails due to infrastructure limitations.

Further, SWOT analysis and scenario assessment provide insights and recommendations for the industry stakeholders.

By offering analysis grounded in stakeholder realities and frameworks, this study contributes to ongoing efforts to scale renewable energy solutions in off-grid settings, to align with national energy transition goals.

Preventing foodborne illnesses and ensuring hygienic conditions remain a public health concern in developing countries like the Philippines. Poverty is still a major factor that affects access to safe food. Meat based products were identified as the most common food vehicle for foodborne illness outbreaks. In order to address these challenges in the entire supply-chain, from farm to fork, the food industry is assessing the potential benefits of implementing artificial intelligence solutions. This study employed technology foresight through a 10-stage scenario building methodology to develop future scenarios and evaluate the key factors that can significantly affect the artificial intelligence adoption in the Philippines food industry. The common food safety challenges and the potential role of artificial intelligence in enhancing food safety in the next 3 to 5 years was discussed in this paper. Four future scenarios were developed by the researcher which can help food manufacturers, artificial intelligence technology suppliers, and the government in making sound decisions and strategies. This will aid in successfully integrating artificial intelligence solutions in the food manufacturing set-up.

This study examines the creation of design system wireframes for the Globe One App, in accordance with Nielsen Norman Group's (NNG) Usability Heuristics. The study seeks to address the current usability issues faced by users, explore potential options for developing a design system in accordance with NNG's heuristics, and pinpoint the elements that require standardization to enhance usability. The project included a design process that required figuring out what the real users' pain points were, ideating the user experience, designing the wireframes and performing a heuristic evaluation.
A user test was conducted with five postpaid subscribers to assess billing labels and the effectiveness of the prompts. The results indicated that the users are confused with redundancy and ambiguous screens. Wireframes were designed using Figma in black-and-white format to show the form, function and flow. The prototypes focused on three main areas: Account Card, Bill Overview and Bill Charges.
Eleven UI/UX specialists, each with over two years of experience, analyzed the wireframes using NNG's criteria on a 5-point scale. The average score was 4.3, and the highest marks were in Consistency and Standards, Visibility of System Status, and Match Between System and the Real World. The results of the heuristic evaluation show that the wireframe made passes the NNG's heuristics. The design system can be used for other parts of the app, such as finding things, buying things, managing accounts, and getting incentives.