Philippine companies are operating about two years behind the global artificial intelligence curve, according to a new study from Boston Consulting Group and the Makati Business Club. The assessment places local businesses in the emerging tier, where firms have begun experimenting with AI but have not yet scaled it into consistent value. In the global benchmark, companies had reached the AI Scaling tier by 2026. Adoption is moving, but capability, governance, and execution are not advancing at the same speed across Philippine industries.
The study surveyed senior business and technology executives from 35 large Philippine companies across 12 industries, measuring maturity through 41 dimensions grouped into eight capability areas. Six in 10 respondents remained in the two lowest tiers, and none reached the Future-built category. Technology, retail, and financial services were generally more advanced worldwide, while real estate, industrial goods, and aviation tended to lag. The Philippines appeared to sit in the middle of Southeast Asia, behind Singapore and Indonesia but not completely outside the regional race.
The barriers are practical rather than purely technical. BCG identified fragmented data systems, shortages of advanced AI talent, and high implementation costs as the main constraints on wider deployment. Many companies are still focused on encouraging employees to use individual AI tools instead of redesigning entire business processes around them. That distinction matters for Philippine firms competing in services and business-process outsourcing, where repetitive work is increasingly exposed to automation. Better data foundations and workforce training may determine whether experimentation becomes productivity.
The participating companies are aiming higher: more than half want to reach the Future-built tier by 2029. The report says that would require local AI maturity to improve at roughly 2.5 times the recent global pace. That target turns the study into a management challenge. Philippine businesses will need clearer use cases, stronger controls, and investment that survives beyond pilot projects. For employees, the transition also raises the value of practical AI literacy, process redesign, and the judgment needed to use automated systems responsibly.
