With the establishment of a national AI policy in 2021, the Philippines aims to enhance innovation and government operations. However, data privacy concerns and regulatory challenges hinder full AI implementation. This study investigates AI adoption in Bangko Sentral ng Pilipinas (BSP) through scenario-building and technology scouting, addressing three research questions: (1) What future scenarios reflect AI adoption in the BSP? (2) What challenges and pitfalls arise in AI implementation? (3) What scenarios can guide policymakers in drafting AI-related policies? Through qualitative methods and a ten-stage scenario-building approach, the gathered data was utilized to develop scenarios reflecting AI adoption, addressing challenges, and guiding policymakers in drafting AI-related policies.

Results revealed that the key scenarios, Time to Consider, No Guts, No Glory, and Input and Output, demonstrated the need for careful planning, innovative transformation, and ongoing adaptation. These scenarios illustrate how AI can balance innovation and legal compliance. Additionally, proposed targeted solutions like technological foresight and third-party monitoring are essential to bridge identified gaps. In conclusion, this study provides strategies for BSP to enhance AI adoption while navigating challenges such as data security and privacy. Using scenario-building proves valuable for understanding the complexities of AI integration in central banking.

This study examines the impact of wearable technology on running performance in the Philippines over the forthcoming decade. This endeavor seeks to address the inquiry: What impact will the adoption of wearable technology have on the running performance of Filipino runners within the next 10 years? The research employs scenario-building to anticipate future developments that address the requirements of Filipino runners, emphasizing cultural relevance, ethical data utilization, and sustainable solutions.

The study formulates three scenarios—The Ethical Athlete, EcoFit Revolution, and The Smart Stride—each illustrating distinct opportunities and challenges in the incorporation of wearable technology. The research team gathered data through a comprehensive review of the literature and expert questionnaires. The analysis identifies key drivers, potential uncertainties, and strategic solutions.

The findings highlight the necessity of prioritizing data privacy, undertaking localized research, and using sustainable materials to promote long-term adoption. Wearable technology, tailored to cultural and environmental contexts, can enhance training outcomes, ensure runner safety, and foster community engagement. The paper provides strategic ideas focused on ethical AI, sustainability, and partnerships to aid wearable technology companies in fulfilling regulatory and customer expectations.

These insights offer recommendations for a strategic framework enabling stakeholders to create a future where technology-driven performance enhancement is accessible, culturally aligned, and beneficial to the Filipino running community.

Makati City needs to explore new ways to keep up with up with the increasing solid wastes due to the rising population and economic activities. One of the promising technologies that can be applied in the improvement of its solid waste management system (SWMS) is Artificial Intelligence (AI). This can be used to analyze huge volume of real-time data using high-performing computers programmed to function like humans to solve modern-day problems.

This study aimed to identify various AI implementations on Solid Waste Management and determine which among these are most applicable to Makati City using the Political, Economic, Social, Technological, Legal, Environmental (PESTLE) and Strength, Weaknesses, Opportunities, Threats (SWOT) analyses.

Using the technology foresight through scenario building, this study has identified three plausible scenarios coming from the implementation of AI in Makati City’s SWMS. The first scenario is City as Smart as Singapore where Makati City was foreseen to have improved its SWMS (i.e., garbage segregation, recycling, collection, and transportation) using various AI implementation such as automated waste segregator, smart bins, and transport route optimizer. This scenario was assessed to be the most applicable for Makati City in its current state.

The other two scenarios were Japan’s Discipline and Burn like Sweden that focused AI implementation in social shaping and incineration. However, both scenarios are not yet plausible for Makati City because of the uncertainty in the success of behavioral change using AI and with the dependency on passing of a law that will allow incineration in the Philippines.

The Balik Scientist Program (BSP), a brain gain initiative of the Department of Science and Technology, aims to reverse brain drain by encouraging Filipino science, technology, and innovation (STI) experts abroad to return and contribute to national development. This study assesses the impact of the Balik Scientist Program (BSP) in strengthening the local R&D capabilities within the country’s industry, energy, and emerging technology (IEET) sectors, which are under the purview of the Philippine Council for Industry, Energy and Emerging Technology Research and Development (PCIEERD). It focuses on analyzing the program’s innovation, knowledge transfer, and technology transfer contributions.

Using a mixed-method research design, the study collected data from Balik Scientists and host institution representatives and analyzed annual reports, policies, and international benchmarking data.

The findings reveal that while the program has successfully facilitated knowledge transfer through training, mentorship, and research collaborations, the predominance of short-term engagements limits its ability to drive technology transfer and commercialization, which requires more time and sustained collaboration. Key challenges include low retention rates of Balik Scientists due to better compensation abroad, limited R&D funding and facilities, and bureaucratic hurdles.

Comparative analysis with brain gain initiatives in South Korea, India, China, and Malaysia highlights gaps in the BSP’s design and implementation, particularly the absence of a centralized database for tracking Filipino researchers, scientists, and engineers (RSEs) abroad and the need for stronger incentives to attract and retain experts. Recommendations include enhancing compensation and benefits, rebalancing engagement durations toward medium- and long-term stints, giving importance to technology transfer, and fostering academe-government-industry collaborations. Establishing a centralized database for tracking Filipino RSEs, increasing R&D investment, and adopting global practices are also critical to optimize the BSP’s impact on the Philippine innovation system.

This study reflects BSP’s potential to drive innovation and economic development but emphasizes the need for policy reforms and strategic interventions to address existing challenges. By aligning the program with national priorities and leveraging the expertise of the global Filipino talent pool, the BSP can play a transformative role in advancing the Philippines’ R&D capabilities and global competitiveness.

The rapid evolution of Artificial Intelligence (AI) in recruitment is transforming traditional hiring processes, enabling organizations to enhance efficiency, streamline talent acquisition, and improve decision-making. This capstone paper explores the future integration and implementation of artificial intelligence (AI) in recruitment processes, using scenario building a technology foresight methodology. It provides a high-level examination of current AI-driven recruitment initiatives, challenges, and opportunities, envisioning plausible scenarios for AI adoption in talent acquisition practices within the next three to five years.

The study identifies critical drivers influencing AI integration in recruitment, including AI-driven automation, technological advancements, organizational readiness, cost implications, acceptance by HR professionals, and potential returns on investment (ROI). It highlights that while AI technologies have significantly enhanced efficiency in recruitment especially in high volume tasks such as resume screening, talent sourcing, and interview scheduling full scale AI adoption varies considerably due to differing levels of organizational readiness, cost considerations, and human acceptance.

Three distinct future scenarios were developed to explore future landscapes:

1. Full AI Takeover: Substantial automation across the recruitment lifecycle, significantly reducing human intervention, and improving operational efficiencies, but raising concerns about potential bias, transparency, and reliance on AI algorithms.

2. AI in Handcuffs: AI adoption occurs but faces strong organizational resistance and constraints, resulting in AI serving primarily as a supportive tool with considerable human oversight, thus leading to increased complexity and operational overhead.

3. Stuck in Transition: Organizations hesitant or unable to commit fully to AI adoption, relying heavily on traditional recruitment practices. This scenario results in higher recruitment costs, prolonged hiring cycles, and reduced competitiveness compared to AI-enabled peers.

The paper also proposes a grand scenario named "Augmented Intelligence in Hiring: The Best of Both Worlds" which envisions an optimal, balanced integration of AI and human driven recruitment. This scenario advocates AI's role as a complementary tool rather than a replacement, promoting improved hiring effectiveness, candidate experience, inclusivity, and sustainable Human-AI collaboration. The study further applies an ROI framework to demonstrate how HR professionals can justify the investment for AI driven recruitment tools and how AI-powered hiring can enhance operational efficiency, talent management, business insights, and people experience.

Key challenges highlighted by this study encompass issues related to technological implementation, cost justification, cultural acceptance, talent availability, and operational readiness. To address these challenges and effectively leverage AI's potential, strategic recommendations are offered around incremental AI integration, targeted training and upskilling of HR professionals, data-driven approaches for measuring AI success, vendor partnerships, and fostering a culture of continuous learning.

Drawing insights from global AI recruitment practices and success stories, this research provides a practical framework for HR leaders, organizations, and policymakers seeking to strategically integrate AI in recruitment processes to enhance efficiency, effectiveness, and competitive advantage in talent acquisition.