First published in The Manila Times
By Cosette V. Canilao
Across industries, artificial intelligence (AI) is no longer a distant frontier. It is increasingly becoming part of how organizations operate, compete, and grow. But while the promise of AI is easy to talk about, the real work lies in applying it well.
At Aboitiz InfraCapital (AIC), we see both the opportunity and the discipline required to do this right. Our approach to AI is anchored on the Aboitiz Group’s broader transformation, where technology, data science, and innovation are becoming more deeply embedded across the organization. For us, this is not about adopting technology for its own sake. It is about finding smarter, more scalable ways to deliver value.
That, increasingly, is what many organizations are learning as well: AI creates the most value when it is applied intentionally and built into real workflows. The point is not simply to use AI, but to use it where it can genuinely improve how work gets done.
From Experimentation to Enablement
At AIC, our AI philosophy is simple: practical, use-case driven, and anchored on real business needs.
We are focused on three areas where AI can create immediate value: operational efficiency, decision support, and asset optimization. Just as important, we do not see AI as a replacement for people. We see it as a tool that can support our teams, reduce friction, and free up more time for work that requires judgment, experience, and insight.
That principle guides how we think about AI across the organization.
AI in Action: Early Wins Across AIC
While we are still early in our journey, we are already seeing promising results from targeted AI applications.
Within Unity Digital Infrastructure, Inc., for example, we have piloted a Gemini-enabled “Spare Locator” use case designed to improve network resilience. Traditionally, identifying the nearest module needed during outages could take anywhere from 30 minutes to two hours. Today, that process is nearly instantaneous. By integrating data from Unity Digital’s Remote Monitoring System, the tool helps accelerate response time and improve decision accuracy.
The impact is practical and easy to understand: reduced mean time to repair, lower service-level risks, avoided emergency procurement costs, and stronger service reliability. It is a good example of how AI, even at a pilot level, can deliver real operational value.
In our Airports business, particularly at Mactan-Cebu International Airport, we are also laying the groundwork for more intelligent operations. We are exploring AI applications in predictive maintenance, passenger flow optimization, and smarter resource allocation. These are still early-stage efforts, but they point to where AI can be genuinely useful in infrastructure: helping operations become more responsive, efficient, and resilient.
Beyond operations, AI is also starting to shape how we support our people. Within our People & Culture function, we are using tools such as NotebookLM to synthesize policies and learning resources more efficiently. We are also exploring AI-powered assistants to help address routine queries and improve access to information.
Though still early, the benefits are already visible: less manual work, faster access to information, and more time for teams to focus on higher-value priorities. Over time, we expect this to become a more deliberate part of how we improve team member experience and build internal capability.
At the enterprise level, our broader digital transformation efforts are also laying the foundation for scaling AI. Investments in enterprise resource planning modernization and more integrated systems matter because they provide the data backbone that AI depends on. Without stronger digital infrastructure, it is difficult to scale AI in a meaningful and sustainable way.
AI in PPP Processes: A More Efficient Way to Review and Evaluate
One area where AI can also be useful — and one that is especially relevant to infrastructure — is in public-private partnership (PPP) processes.
PPP transactions are typically document-heavy, iterative, and time-sensitive. They involve completeness checks, technical and financial review, comparisons across draft terms, identification of inconsistencies, and repeated coordination across agencies and teams. Much of this work is necessary, but it can also be labor-intensive and time-consuming.
AI can help make some of these steps more efficient.
For example, AI tools can assist reviewers in checking submissions against documentary requirements, flagging missing items, surfacing inconsistencies across annexes, organizing information into a more usable format, and tracking revisions across successive submissions. That can be especially helpful when proposals involve large volumes of material.
AI can also support evaluation by helping teams compare proposals against defined criteria, summarize risk allocation, identify departures from standard provisions, and highlight assumptions that may need closer review. It can also help organize precedent comments, recurring concerns, and prior review issues so that teams can work from a more structured knowledge base.
This is consistent with Organisation for Economic Co-operation and Development work on AI and digital transformation in public procurement, which points to the potential of these tools to improve administrative efficiency, strengthen oversight and transparency, and support more data-driven decision-making in public-sector processes.
Of course, AI should remain a support tool rather than a substitute for judgment. In PPPs, questions of public interest, fiscal exposure, legal enforceability, service quality, and bankability still require careful review by accountable officials and decision-makers. But used properly, AI can help make the process faster, more consistent, and easier to manage.
Challenges and Realities
As promising as these developments are, we remain clear-eyed about the challenges.
Data readiness and integration gaps continue to be a real constraint. AI systems are only as effective as the data that powers them, and ensuring data quality, accessibility, and interoperability requires sustained effort.
Talent and capability building is another priority. Successfully deploying AI is not just a technical challenge; it is also an organizational one. It requires new skills, new ways of working, and a culture that is open to learning and adaptation.
Governance, cybersecurity, and ethical considerations also require careful attention. As AI becomes more embedded in operations, it must be deployed responsibly, securely, and in a way that remains aligned with organizational values.
These realities remind us that capturing AI’s value requires more than isolated pilots. It requires stronger systems, clearer processes, and the discipline to scale what works.
The Road Ahead
Our journey with AI is still in its early stages, and much remains ahead. As we continue with it, our focus remains clear: to apply AI where it matters most — in ways that are practical, responsible, and grounded in real operational needs, both within our business and, increasingly, in the broader systems that support infrastructure delivery.