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.

In this digital age, the Philippine banking sector has been increasingly pursuing its initiatives for digital transformation. With this, the need for mature risk management, specifically the Operational and Technology areas, is pivotal to the success of such business endeavors.

The aim of this study is to assess the current state of risk maturity level of a thrift bank, which may potentially influence future strategies, and facilitate in improving operational efficiency and resiliency, risk treatment, effective oversight function of risk management, and digital transformation.

In order to assess this, SWOT Analysis, PEST Analysis, Interview with the Chief Risk Officer and Head of Enterprise Risk, and conduct of survey to Risk Champions are used to better understand the Risk Maturity of Operational and Technology Risk in XYZ Thrift Bank where the study revealed that the bank’s Risk Maturity is at Level 3 (Defined) where risk management is integrated to the business processes, risk ownership is assigned and risk management activities are launched simultaneously.

As XYZ Thrift Bank moves toward digital transformation, the operational and technology risk management must be able to collaboratively function with the bank’s digital transformation initiatives in order to support the potential risks that may arise from the bank’s digital pursuits. A Technology Roadmap for Operational and Technology Risk Maturity aims to ensure that thrift banks can improve its level of risk maturity while navigating through the complexities of digital transformation by the enhancement of current operational and technology risk activities and practices, and adopting emerging technologies for risk automation processes and implementing continuous improvement on risk tools, evaluation and pro-active work on risk sources.

There has been an interest in implementing technologies to digitalize the business processes of Leopard Connectivity Business Solutions Inc. to improve the delivery of services leading to customer satisfaction improvements and an increase of subscribers. The study seeks to answer what methods the company must use to know the needed technologies it should implement; the possible scenarios the company may find themselves in the next five (5) years; and the type of technologies they must implement to improve their customer satisfaction and grow their current number of subscribers.

A combination of a scenario building and relevance trees analysis were used to identify the needed technologies that the company must implement. A technology stack for each scenario was then used to show the relationship between the future technologies and how they are connected to the company’s business processes. A scenario building activity was selected as it looked into future environmental trends, predictable variable, and uncertainties around the company. Scenario building provided multiple scenarios and strategies applicable to the company’s uncertain future. To help in the identification of needed technologies, a relevance trees analysis as used to break down the strategies from each scenario.

The method resulted into three (3) scenarios based on the support the company will get in their technology implementations. The scenarios resulted in future technology stacks with varying degree of automation and interconnectedness of business processes. A scenario of full automation must be aimed to greatly improve services that would improve customer satisfaction and grow the current number of subscribers.