The IBPAP recently lowered its 2028 target. Slashing their projection by 600,000 jobs. Why didn’t we see the early signs?
The signs were there. We even measured them.
In August of 2024, the IT and Business Process Association of the Philippines, or IBPAP, asked its members about artificial intelligence. The results came out around October. They showed an industry moving into AI much faster than many people understood.
Eleven percent of member firms said they had fully put AI into daily work. These were not small tests. The systems were already being used. Another 56 percent said they were in the process of doing the same.
That 11 percent may have looked small. It was not. A global Deloitte survey from around the same time found that only about 4 percent of firms had put at least 70 percent of their AI plans into use. By that rough measure, Philippine BPO firms were almost three times as likely to be far along.
At the time, this sounded like good news. It showed that the industry could move fast. But it also told us that a deep change had begun.
The jobs data gave another clue. Eight percent of IBPAP members said AI had already led to job losses. Yet 13 percent reported job gains. This fit a wider view: AI would remove some jobs and create others.
The biggest warning sat between those two numbers. Twenty-four percent said jobs had changed. Twenty-six percent said workers needed new skills. Put together, half the industry was saying that work or skills had to change.
Why, then, did the industry expect a constant growth rate for several years? Why did it not pivot when the evidence was showing otherwise? A forecast is not a promise. It must change when the facts change. Yet the old growth story stayed in place long after AI began to rewrite the work.
The Philippines also had limited control over demand. About 70 percent of the sector depended on US outsourcing. If American clients cut staff, moved work, or used more AI, local firms had to react. But the country did control how it trained people. That chance was missed or taken too slowly.
Companies wanted newer tools: chatbots, language systems, content tools, and AI agents that can carry out tasks. Schools were still focused on older forms of data science and machine learning. Those skills still mattered, but demand was moving elsewhere. There were also too few teachers ready to teach the new tools.
The barriers were known. AI cost money. Old computer systems could not easily connect to it. Firms needed strong data and AI engineers. They faced weak data, privacy risks, and a lack of skilled workers. None of these problems came as a surprise.
Companies also said why they wanted AI. They wanted more output, lower costs, better service, and new sales. Less attention went to risk and worker well-being. That was another mistake. Automation can make a firm faster while making a worker’s day worse. A business can become more machine-like even when people still keep it running.
Looking back, the survey was not just a proud report about early AI use. It was a warning. Adoption was ahead of the world. Roles were already changing. Half the industry needed some form of retraining. Schools were behind. Old systems were hard to fix. Foreign demand could shift at any time.
The better response would have been to revise the forecast early, retrain workers at speed, update school courses, and build plans for both job gains and losses. It also would have meant measuring the quality of new jobs, not just counting seats.
The target cut does not mean the sector will vanish. The Philippines still has talent, experience, and a strong service base. But the old model of adding huge numbers of workers year after year no longer fits the facts.
This is the lesson of the missed signs: disruption does not arrive only as a layoff notice. It first appears in changed tasks, tools, training gaps, and forecasts that start to feel less real each passing quarter.
The IT-BPM party is likely over. Many people are still holding the balloons.
Dominic “Doc” Ligot is one of the leading voices in AI in the Philippines. Doc has been extensively cited in local and global media outlets including The Economist, Channel News Asia, South China Morning Post, Washington Post, and Agence France Presse. His award-winning work has been recognized and published by prestigious organizations such as NASA, Data.org, Digital Public Goods Alliance, the Group on Earth Observations (GEO), the United Nations Development Programme (UNDP), the World Health Organization (WHO), and UNICEF.
