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.

This study aims to assess and develop an existing troubleshooting playbook of the organization for support engineers that uses manual keyword search as its initial course of investigation, integrating the AI-assisted search as initial part of troubleshooting process with human-decision skills and expertise. Working with support engineers drawn from multiple product teams, the research began assessing the sentiments of the users using the existing process to map how they find and acquire knowledge on their day-to-day productivity. These engineers helped in co-designing the structure of the playbook, and initiated a controlled pilot deployment using tagging, surveys, interviews, and Net Promoter Score comparisons. The evaluation focused on practical outcomes (average resolution time, trends on how AI improved the percentage of resolved cases, and how support engineers adopted the use of the tool.

The result shows positive user sentiments after the AI process implementation, support engineers reported faster starts on their investigation and few repetitive searches. While the NPS distribution moved towards greater share for the AI-assisted method, the actual reactions are still parted, some support engineers welcomed the AI as learning improvement and time-saver, while others preferred manual keyword searching for control and contextual accuracy. The qualitative data revealed common sentiments that Gen AI provided too vague and sometimes irrelevant suggestions that still need human validation and review.

The study concluded that AI can materially improve initial triage process through the average resolution time cases were resolved and its consistent increase of volume on cases resolved each week when implemented as a troubleshooting partner rather than replacement to support engineers. The trust and confidence from users of utilizing the tool shows huge improvement as well overtime. Recommendations include continuous user-feedback loops, tagging of cases, and monthly surveys to gauge users sentiments and usability, also, the developers should also include JIRA bugs and fixes as part of Gen AI knowledge pool. The research contributes a practical and effective assessment of the organization’s future development for the next 6 to 12 months using the support troubleshooting playbook as productive day-to-day tool.

This study examined how different product development directive origins whether top-down or market-led, affects commercialization outcomes and consumer adoption within Philippine QSRs. Despite the volume of R&D activity in our country’s F&B sector, there is limited evidence on how the said directive types of influence OTIF launches, testing and validation practices, and eventually sustained product performance in the menu. Using a mixed-method design, the study gathered quantitative data from 30 survey respondents and qualitative insights from 10 interviews, triangulated by secondary and readily available industry sources. Key variables measured included commercialization success, adoption outcomes, time-to-market pressure, compressed Product Development lifecycles, and the risk of bypassing validation stages.

Findings show that market-led directives outperform top-down directives in both commercialization success and consumer adoption-related metrics. Top-down initiatives are perceived by respondents to usually launch on schedule but often experience compressed timelines and reduced validation steps, while market-led initiatives demonstrate stronger discipline in testing and sustained higher consumer adoption. Correlation analysis revealed a strong link between executive-drive directives and compressed timelines, and a positive relationship between more frequent validation and stronger adoption. However, timely launches do not guarantee product longevity in the market. Qualitative results supported these patterns, and the findings also align with the Innovation Diffusion Theory, the Resource-based View Theory and the Dynamic Capabilities Theory. The study recommended a hybrid model moving forward, leveraging the market-led initiatives for consumer-dependent projects and reserving the top-down directives for high-impact strategic initiatives. This study aimed to contribute empirical evidence on directive origin and impact to innovation outcomes in the PH QSR market, addressing a notable gap in the currently available R&D-related research.