Philippine public procurement has operated under a robust legal framework since Republic Act No. 9184 of 2003, updated by Republic Act No. 12009 of 2024. Yet the 2025 flood control investigations, which identified approximately 421 ghost projects out of roughly 8,000 national-government infrastructure projects inspected, confirmed that procurement irregularities at scale persist not because of missing rules but because of missing integration: procurement records, audit findings, tax filings, contractor licensing status, and disqualification notices sit in five separate institutional systems that cannot be aligned in real time. This capstone study applies Design Science Research methodology to design, build, and evaluate ProcureGuard PH, a blockchain-anchored, AI-augmented procurement transparency prototype that unifies these silos into a single citizen-accessible, tamper-evident platform.

The study is guided by three research questions covering systemic gap analysis and the Senate Bill No. 1506 (CADENA Bill) legislative response, vendor qualification and eligibility enforcement through blockchain and artificial intelligence, and the prototype’s role as a demonstration vehicle for procurement transparency policy institutionalization. ProcureGuard PH implements five role-based modules, an append-only SHA-256 chained ledger, a Vendor Qualification and Eligibility Enforcement Module operationalizing the complete twelve-document RA 12009 eligibility framework including ledger-anchored NFCC computation that verifies government contract obligations across participating procuring entities, a rule-based analytics layer with seven implemented and six designed extension rules (two rule types actively firing in the current demonstration dataset), and an AI-augmented analytics layer.

Walkthrough self-evaluation confirms substantial coverage of design requirements derived from the three-lens conceptual framework. Practitioner expert evaluation was conducted via Focused Group Discussion in May 2026 with seven participants representing BAC Secretariat, Technical Working Group, Requesting Unit, Commission on Audit, Bureau of Internal Revenue, and Finance and Accounting roles; findings confirm that the prototype addresses the most consequential operational gaps identified by practitioners, and identify Senate Bill No. 1506 (CADENA Bill) Section 15 enactment as the governance prerequisite for converting technical tamper-evidence into legally actionable audit evidence. Comparative architecture assessment establishes alignment with the CADENA Bill Section 15 prima facie evidentiary standard within the system boundary.

This research assessed the current Service Transition Management practices within a suborganization of a Fast-Moving Consumer Goods (FMCG) company. The study revealed significant challenges in the current STM processes, notably insufficient knowledge transfer, inadequate documentation, and communication breakdowns. These deficiencies directly led to the operations team consistently failing SLAs, impacting business efficiency, and overall service quality. The investigation incorporated stakeholder feedback and benchmarked against industry standards, underscoring the critical need for a more structured approach for managing Information Technology (IT) product transitions.

To address these identified challenges, this study proposes an operative service transition framework - Service Transition – Dimensions, Deliverables, and Evaluation (ST-DDE) This framework systematically integrates key dimensions of service management, with essential deliverables, and a comprehensive evaluation component utilizing specific KPIs and metrics. The framework provides structured, phased approach designed to enhance efficiency, mitigate operational risks, and improve service quality and reliability of utilizing the applications. Recommendations stemming from this framework emphasize formalizing knowledge management, strengthening training, improving communication, and developing data-driven evaluation process, thereby offering a practical pathway for continuous improvement in the team’s service transition capabilities and supporting its digital transformation journey.

This paper studies the organizational readiness and performance in the adoption of agentic artificial intelligence (AI) in the medical knowledge process outsourcing (MKPO) industry, with a focus on Optum Philippines. The study discusses the transformative potential of agentic AI and presents the specific challenges posed by this AI system. MKPO companies are uncertain as to whether they are prepared to deploy agentic AI, given the current technical, workforce and governance challenges. The study adopts the conceptual framework of technology acceptance theory and recent AI adoption literature as of January 2026, to analyze organizational readiness in successful AI integration. A combination of quantitative surveys and qualitative interviews are done. The study also reviews secondary data about Optum’s use cases to evaluate AI performance. The research findings resulted in Optum having a high organizational readiness index however, barriers to adoption were identified. The study is limited to a single organization, and it acknowledges that agentic AI and its regulations are rapidly changing, so its relevance is relative at the time when the research is conducted. The research concludes that organizational readiness and technological innovation alignment are the key to sustainable business performance improvement. It recommends to invest in a more simulation-based training for clinicians, to strengthen data privacy measures, to support legacy system integration, to advance governance and monitoring frameworks, to weave AI into daily workflows, and to develop a structured AI maturity roadmap. All these will collectively unlock the full potential of agentic AI and foster responsible innovation within the healthcare outsourcing companies.

Hybrid and remote work arrangements have weakened traditional mechanisms of sprint visibility, increasing the risk of delayed issue detection and reduced delivery predictability in Agile teams. This study presents an integrated organizational adoption framework and PPTO-based roadmap for AI-assisted, mobile-first sprint health monitoring. The proposed Sprint Pulse approach combines operational delivery metrics with human-centered signals, including sentiment, perceived workload, and reporting behavior, to enable earlier and more actionable risk detection.

Guided by three research questions, the study examined (1) visibility gaps and tool effectiveness in hybrid Agile environments, (2) feature and data requirements for a reliable Sprint Health Score and automated risk flagging, and (3) the organizational roadmap needed for responsible implementation. A mixed‑methods design was employed, combining a descriptive survey of 365 Agile practitioners from a defined population of 7,000, semi‑structured interviews with Agile leaders, and benchmarking of existing Agile and AI‑enabled tools.

Findings indicate that dashboards are most effective when supported by fresh, low‑friction inputs captured during the sprint. Results suggest that early risk detection is feasible when flow‑based indicators (e.g., blocker age and scope churn) are combined with lightweight sentiment and workload pulses and surfaced through explainable AI patterns. The study concludes that successful adoption depends less on analytics sophistication and more on organizational readiness across people, process, technology, and operations (PPTO), including psychological safety, governance transparency, workflow maturity, and low-friction reporting behaviors. Findings suggest that AI-assisted sprint health visibility becomes organizationally feasible only when supported by phased capability development and sustained trust-building mechanisms.

Over one and eight-tenths million people work in the Philippines' Information Technology – Business Process Outsourcing (IT-BPO) sector, and contribute about 7% to its Gross Domestic Product (GDP). However, the sector continues to utilize conventional approaches toward accessing operational knowledge. Operational knowledge is stored in various forms of documentation. Information regarding operational knowledge is difficult to locate unless adequate documentation exists. It is equally challenging to identify solutions to recurrent ticket requests. Only approximately 14.9 % of Philippine companies have employed Artificial Intelligence (AI) in some form. Therefore, the majority of knowledge operation activities including knowledge retrieval remain traditional. Enterprise Retrieval-Augmented Generation (RAG) provides an opportunity to resolve the access issue by aggregating standard operating procedures (SOP), run books, and resolution history into one search engine. This research has positioned RAG specifically as a knowledge access solution. RAG improves the rate at which employees can locate documented information; however, it does not substitute the mentoring, coaching and tacit learning that represent knowledge transfer itself.
To develop a decision-making framework for the adoption of enterprise RAG technology for knowledge operations within Philippine IT-BPO industry, this research used a ten-step intuitive logic scenario planning methodology based upon the original developed by Schoemaker (1995). A STEEPL environmental scan identified 23 factors or "drivers" that influenced the application of RAG technology. A structured survey was administered to 14 IT-BPO practitioners who rated the potential influence and uncertainty associated with each driver. Those drivers scoring high in terms of both dimension were clustered along two independent axes: Technical and Operational Readiness; Economic and Leadership Commitment. From these two axes, four distinct scenarios were created, each evaluated based upon knowledge transfer speed, service delivery quality, risk exposure and cost.
Three base-level strategies were found to be applicable across all four scenarios. Conditional strategies were also identified based upon each axis. Finally, early warning signs or indicators were identified that allow organizations to recognize whether their current environment is trending towards a specific future scenario. Organizations may therefore make adjustments prior to being left behind.