This capstone project addressed the critical national security imperative of safeguarding the optical transport network of the Philippines. As the foundational backbone of the national digital economy, this network required protection against the emergent threat of quantum computing. The research noted that current public key cryptography, which secured all sensitive government, financial, and personal data transmissions, was vulnerable to quantum attacks. This risk was further amplified by the "Harvest Now, Decrypt Later" strategy where adversaries harvested encrypted data for future decryption.

To preempt this crisis, the study developed a proactive migration strategy to quantum resistant cryptography by employing a novel dual methodological framework. Through forecasting by analogy, the project extracted practical insights from historical technological transitions. Concurrently, scenario building was used to construct and stress test multiple plausible futures to formulate a resilient and adaptable roadmap tailored to the Philippine context. The deliverables of this capstone included a comparative analysis report and a strategic implementation roadmap for ensuring the cryptographic resilience and long-term digital sovereignty of the nation.

The Philippine food and beverage (F&B) industry contributes to approximately 23-24% of national GDP facing increasing pressures to enhance operational efficiency and sustainability amid water scarcity and tightening environmental regulations. The adoption of advanced digital solutions such as Ecolab® CIP IQ™– an AI-driven Clean-In-Place system remains limited in the Philippines due to context-specific organizational, regulatory, and infrastructural barriers. This study proposes a tailored implementation framework for CIP IQ™ technology based on thematic analysis of interviews with five purposively selected industry specialists (sales, engineering, and end‑users). Findings show that CIP IQ™ can enable resource‑efficient business model innovation by offering real‑time monitoring, automated cleaning control, and cloud analytics, delivering estimated 15-20% water use reduction and up to 25% shorter cleaning cycles while complying with the Food Safety Act (2013) and the Clean Water Act (2004). Successful adoption depends on three interlinked domains: technical readiness (infrastructure and system integration), organizational readiness (leadership, user capability, and change‑management), and external support (regulatory clarity, financing, and capacity‑building). The framework recommends five policy levers – targeted financing, streamlined regulations under the Ease of Doing Business and Efficient Government Service Delivery Act of 2018 (2017), state-led training programs, public–private partnerships, and strategic digital integration – to align industry growth with environmental stewardship.

Generative Artificial Intelligence (GAI) has experienced rapid adoption and development in the recent years. Its impact on an organization’s strategies is important for the company to be able to prepare for the radical change the technology is likely to bring. Current studies tackle the impact on the Information and Communications Technology (ICT) and Business Process Outsourcing (BPO) sectors, while this study addresses the lack of insight on the topic covering the Telecommunications industry in the Philippines, and its unique characteristics. Especially the unique contribution it brings as the enabling body that provides internet that allows access to AI-enabled platforms.

This study used the ten stage – technology foresight scenario building process demonstrated by Imbang (2023), in the forecasting of future scenarios that may help telco executives in the strategic planning in anticipation of GAI. The analysis was conducted using variables collected and examined via STEEP and TOWS analyses, which are then synthesized and assessed using the impact/uncertainty matrix to define critical variables that suggests patterns for likely futures.

The findings suggests three possible scenarios covering different sets of variables : 1) High impact, high uncertainty variables which suggests a scenario wherein full adoption and deployment of GAI is observed in the telco sector. Resulting in the disruption of workforce requirements, ways of working, and other critical considerations. 2) High impact, low uncertainty which suggests a more conservative approach wherein the sector focuses on the reinforcement of its infrastructure and services to support increasing demand and the delivery of the GAI services through other sectors. Lastly, 3) highest impact variables which suggests aggressive investment to support both infrastructure improvements, and GAI technology deployment support. These findings can help in the strategic planning and decision making of telco stakeholders, and serve as a forecast on the possible future impact of the technology on the industry’s workforce, capacity requirements, and technological advancement.

The rapid evolution of cyber threats, rising client expectations, and cyber workforce shortages were accelerating the integration of Artificial Intelligence (AI) into managed security services.

This study examined how Company X, a Managed Security Services Provider (MSSP), could strategically leverage AI to remain competitive in a rapidly evolving cybersecurity landscape.

Using scenario planning technology foresight, the study identified the key predictable drivers (e.g., cyber literacy and workforce shortage, digital transformation and technology adoption, cybersecurity risks and financial impact, and Philippine regulations) and critical uncertainties (e.g., AI governance maturity, multi-stakeholder collaboration, and pace of AI adoption). These variables were expected to shape AI’s role in cybersecurity over the next three years. The study also envisioned three (3) plausible future scenarios, namely (1) Human-AI Hybrid Model, (2) Fully Automated Cyber Defense Model, and (3) Open Innovation-Driven Cybersecurity Ecosystem.

Each scenario was analyzed for coherence, implications, vulnerabilities, and strategic responses, supported by Technology Management (TM) concepts, including technological capability, innovation capability, service operations management, social shaping of technology, open innovation, and technology transfer.

The findings indicated that the competitiveness of Company X depended not only on technological automation but on the effective integration of AI governance, workforce expertise, ecosystem collaboration, and differentiated advisory services. The study concluded with a multi-year AI integration roadmap and strategic recommendations for enabling Company X to become a future-ready, client-centered, and innovation-driven MSSP.

This project looks at how Company X's aftersales work changes as more of its service tasks move to one main 360 platform. This change is important because teams used to have to use a lot of different systems to finish simple requests. This change is important because teams used to have to use a lot of different systems to get even the simplest requests done. That made regular work slower and harder to follow or check. When new technologies are paired with clear processes and support, the 360 Platform works best. As more tasks were brought into the 360 platform, however, staff spent less time moving between systems, made fewer manual mistakes, and could see a clearer picture of each customer case.

As the rollout progressed, early patterns in aftersales performance suggested gradual changes linked to the consolidation of workflows. Aggregated operational data showed shorter handling times, higher rates of issues resolved during first contact, and clearer service-level tracking as more processes were brought into the 360 platform. These observations are consistent with prior studies noting that reduced system switching and unified case visibility may contribute to steadier customer interactions.

The study looks at Company X’s shift from a mix of separate tools to a single aftersales platform and compares this with what other organisations often go through during similar transitions. These day-to-day shifts—such as having fewer systems to check, reflect the practical side of integration work that is often left out.