SPECIAL REPORT | Philippine AI adoption at 92%, but scaling stuck in pilot stage
AI (Artificial Intelligence) concept. Electronic circuit. Communication network.
Artificial intelligence is now mainstream across Philippine enterprises. Scaling it is the problem.
A new industry study shows 92% of Philippine organizations used AI in some capacity last year. Yet 65% remain in proof-of-concept mode — experimenting, not transforming.
“AI has hit critical mass, but scaling is the bottleneck,” the Philippine AI Report 2025 Executive Brief states.
The numbers reveal a system comfortable with tools but not yet integrated.
Surface adoption, shallow integration
More than half of firms — 54% — have used generative AI tools for more than 12 months. Only 2% are just beginning. Executive oversight is present, with 61% reporting C-level supervision of AI initiatives.
But integration remains thin.
“Most adoption still sits on top of workflows rather than inside core systems,” the report notes.
Internal automation (65%), content creation (64%) and data analysis (60%) dominate use cases. These are productivity gains, not structural redesigns.
The constraint is not enthusiasm. It is capability.
“Two constraints will decide who breaks out of POC: talent + trust.”
Skills gaps were cited by 57% of respondents as the top barrier, followed by security and privacy concerns at 40%.
Buying AI, not building it
The Philippines is consuming AI at scale, not producing it.
ChatGPT leads adoption at 83%, followed by Google AI platforms (62%), Claude (44%) and Microsoft Copilot/Azure (39%). Yet only 12% use machine learning frameworks such as PyTorch or TensorFlow, and 10% use CUDA.
“Philippine companies are prioritizing rapid deployment through ready-made platforms over custom development,” the report says.
This is platform dependence. It accelerates deployment but limits technical depth and intellectual property creation.
The pattern is clear: enterprises are embedding AI as a feature, not as infrastructure.
2026: Inflection or plateau?
Companies intend to scale.
By 2026, AI use in HR is projected to rise from 23% to 43%. Forecasting could increase from 36% to 51%. Chatbots may grow from 42% to 57%.
“2026 is positioned as the year of scaling successful pilots,” the report concludes.
But scaling pilots into systems requires workforce transformation, not just licenses and APIs.
The workforce gap
The National AI Upskilling Roadmap directly targets the structural weakness identified by industry.
Its five pillars — AI literacy, sector reskilling, education reform, public-private collaboration and governance and security — align with the two reported bottlenecks: talent and trust.
Priority sectors include agriculture, manufacturing, healthcare, education, government services and IT-BPM.
The roadmap calls for standardized AI training, certification pathways, curriculum integration and industry-led training hubs.
The intent is clear: move the country from tool usage to systems capability.
The structural risk
The risk is not low adoption. It is asymmetrical development.
If enterprises continue to rely on ready-made AI layers without building internal model expertise, the country could see short-term productivity gains while deep technical capacity lags.
The report summarizes the gap bluntly:
“Executive commitment is strong; enterprise capability is the gap.”
That gap will determine whether AI becomes embedded infrastructure or remains an automation overlay.
The Philippines has crossed the experimentation threshold. The next phase is institutionalization.
Scaling AI is no longer about adoption rates. It is about systems design, skills architecture and governance discipline.
Without those, 92% adoption becomes a ceiling, not a foundation.
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