Cebu Pacific is expanding its use of artificial intelligence across the airline after an initial rollout of ChatGPT Enterprise reduced processing times in legal, project management, and engineering workflows.
The airline is working with Thinking Machines Data Science and OpenAI to move its enterprise AI strategy beyond isolated experiments and into everyday business processes. The approach focuses on identifying high-value workflows, testing AI in actual operating environments, and applying governance based on the risks associated with each use case.
The early results provide a concrete view of where enterprise AI can create operational value. Cebu Pacific reported that legal contract reviews now take about 1.2 hours on average, compared with 2.5 hours previously. Project intake and triage has fallen from five days to about one day, with around 80% less manual review effort. Engineering reference searches that once took 10 to 15 minutes can now be completed in under 10 seconds.
What changed after Cebu Pacific introduced ChatGPT Enterprise?
The airline's reported improvements span different types of knowledge work rather than a single department.
Legal contract review - from 2.5 hours down to 1.2 hours. About 50% faster
Project intake and triage - from 5 days to about 1 day. About 80% less manual review
Engineering reference searches - from 10–15 minutes down to under 10 seconds. Substantial reduction in search time
Cebu Pacific also said that 86% of surveyed users reported that AI supported more than one-third of their daily work following the program. That figure comes from the airline's internal survey and should be understood as a measure of reported usage rather than an independent assessment of productivity.
The significance is less about any single time saving and more about where AI is being integrated.
Instead of treating generative AI as a standalone tool employees can optionally use, the airline is applying it to specific processes where delays or repetitive manual work can be identified and measured.
Why is moving beyond AI pilots difficult for businesses?
Many organizations can provide employees with access to an AI tool. The harder task is determining where AI should actually be embedded into the way work gets done.
That requires businesses to identify suitable workflows, understand the risks involved, establish appropriate controls, train employees, and determine whether an AI-enabled process produces a measurable improvement.
Cebu Pacific's collaboration with Thinking Machines Data Science follows this workflow-first approach. Business leaders and teams were involved in prioritizing use cases and testing solutions under real operating conditions, while evaluation and governance were adjusted according to the risk of each workflow.
This is particularly relevant for an airline, where business processes can involve legal documents, technical information, operational systems, customer interactions, and other data with different levels of sensitivity.
What role does Thinking Machines Data Science play?
Cebu Pacific selected Thinking Machines Data Science to help execute its enterprise AI strategy alongside OpenAI technology.
OpenAI currently lists Thinking Machines Data Science as an Advanced Partner in its partner network. The partner profile identifies the Philippines, Singapore, and Thailand as markets served and includes transportation and logistics among its supported industries.
The partnership model goes beyond deploying software. It involves working with business teams to identify practical applications, co-create solutions, and support adoption.
That distinction matters because enterprise AI projects can fail to deliver their expected value if employees do not incorporate them into everyday work.
The Cebu Pacific program therefore combines technology deployment with workflow redesign and change support.
What does this mean for employees?
For employees, the immediate value is concentrated on tasks that involve searching, reviewing, sorting, and processing information.
Legal teams, for example, can spend significant time reviewing documents. Engineering teams may need to locate technical references before solving a problem. Project teams can also spend considerable time screening and routing incoming requests.
AI can assist with these information-heavy activities, potentially allowing employees to spend more time on tasks that require professional judgment, context, and decision-making.
That does not mean AI replaces the underlying expertise.
In the Cebu Pacific program, the technology is being applied as part of existing workflows, with governance and evaluation built around individual use cases.
What comes next in Cebu Pacific's AI strategy?
Cebu Pacific now plans to expand its use of AI in several directions.
The airline's roadmap includes:
- expanding ChatGPT-enabled workflows across additional areas;
- developing solutions through OpenAI APIs, which allow organizations to integrate AI capabilities into their own applications and systems;
- strengthening change-management and employee support; and
- exploring agentic applications that can connect with enterprise data and systems.
An AI agent generally refers to a system designed to carry out tasks across multiple steps, potentially using tools or accessing connected systems rather than simply generating a response to a prompt.
For businesses, this represents a different stage of AI adoption. Instead of asking an AI system to help with an individual task, organizations can explore how AI might participate in larger workflows.
That also raises more complex questions around permissions, data access, monitoring, reliability, and human oversight.
Cebu Pacific's next AI phase is about scale
Cebu Pacific's collaboration with Thinking Machines Data Science and OpenAI illustrates a broader shift in enterprise AI adoption: the focus is moving from simply making AI available to determining how it can change the way work is performed.
The airline's reported improvements in contract review, project intake, and engineering searches provide early examples of measurable workflow changes.
Its next phase will test whether those gains can be extended across more functions while maintaining appropriate governance and employee oversight.
For Cebu Pacific, the longer-term objective is to use AI not only to process information faster, but to support employees and operations at greater scale.
The more important measure will ultimately be whether those systems continue to produce reliable business value as their use expands.