There is a tendency for organizations to focus on the technical side of artificial intelligence. We see models, data pipelines and infrastructure while putting ethics and governance as secondary, if not an afterthought. As companies move from experimentation to real deployment, the question is not just whether AI works, but whether it should be used in the way it is designed. This is where roles focused on ethics and governance become not just relevant but necessary.
In a corporate setting, the concept of an ethicist is more commonly framed within formal roles such as a chief ethics officer, head of responsible AI, or AI ethics lead. Large organizations have begun institutionalizing these roles, not as symbolic positions but as operational ones tied to risk management, compliance and long-term trust. The shift reflects a deeper realization: AI decisions can affect customers, employees and entire markets. That level of impact requires accountability.
A chief ethics officer, or its equivalent, sits at the intersection of technology, law and business strategy. The role is not about abstract philosophy. It is about translating ethical principles into policies that can be applied in real situations. For example, when a company deploys an AI model for credit scoring, the question is not only about predictive accuracy — it also involves fairness, bias, explainability and the potential exclusion of certain groups. An ethics lead ensures that these dimensions are assessed before deployment, not after a problem arises.
This role also acts as a counterbalance within organizations that are driven by speed and performance metrics. AI teams are often incentivized to ship quickly. Product teams are focused on user growth. Sales teams are under pressure to deliver results. In that environment, ethical considerations can easily be sidelined. A formal ethics function creates a structured way to pause and evaluate decisions that carry broader consequences. It introduces friction where needed, which in this case is not inefficiency but discipline.
Governance, on the other hand, provides the system that supports these decisions. If ethics defines what is right, governance defines how decisions are made, who makes them and how accountability is enforced. In AI, governance cannot be a one-size-fits-all model. It has to be aligned with the specific use cases of the organization.
For low-risk applications such as internal process automation, governance may be lightweight. A review process, documentation and periodic audits may be sufficient. But for high-risk use cases —healthcare diagnostics, financial decision-making, public-facing AI systems — the governance structure needs to be more robust. This may include cross-functional review boards, mandatory bias testing, external audits and clear escalation paths when issues are identified.
One emerging practice is the creation of AI governance councils. These are not purely technical committees. They bring together leaders from legal, compliance, data, technology and business units. The purpose is to ensure that decisions are not made in silos. AI systems often cut across functions and so should the oversight. A governance council can review high-impact projects, set standards and ensure consistency across the organization.
Another important layer is operational governance. This is where policies are translated into day-to-day processes. It includes model documentation, data lineage tracking, version control and monitoring systems that detect drift or unintended outcomes. Without this layer, governance remains theoretical. The real challenge is embedding it into workflows so that it becomes part of how teams build and deploy AI, not an additional step they try to bypass.
There is also a growing recognition that governance is not static. AI systems evolve over time. Models learn from new data, and their behavior can change. Governance frameworks need to account for this. Continuous monitoring, periodic reviews and the ability to intervene when systems behave unexpectedly are essential. This is particularly important in environments where decisions have real-world consequences.
In the Philippines, the conversation around AI ethics and governance is still developing, but the urgency is clear. As companies adopt AI in customer service, financial services, healthcare, and even government functions, the risks become more visible. Data privacy laws such as the Data Privacy Act already set a baseline, but AI introduces new layers of complexity that go beyond traditional compliance. In our discussions with public and private organizations several use cases utilize AI, signifying the need for a human-centered governance to ensure responsible AI capacity building.
Building these roles and structures early offers an advantage. It allows organizations to scale AI responsibly, rather than retrofitting controls after issues emerge. It also builds trust with customers and stakeholders, which is increasingly becoming a competitive factor. In a market where technology adoption is accelerating, trust can be the differentiator.
The challenge is not just hiring a chief ethics officer or setting up a governance council. It is ensuring that these roles have real authority and are integrated into decision-making processes. Without that, they risk becoming symbolic, existing on paper but not influencing outcomes.
Organizations looking to build AI capabilities need to think beyond the technology stack. They need to design the human and organizational structures that will guide how that technology is used. Ethics and governance are not constraints, they are enablers that allow AI to be deployed at scale without compromising integrity.
In the end, the question is simple. As AI becomes more embedded in business operations, who is responsible for ensuring that it is used in a way that is fair, accountable, and aligned with the organization’s values? The answer cannot be “everyone” in a vague sense. It has to be specific roles, clear processes, and a governance system that works in practice.
Kay Calpo Lugtu is the chief operating officer of Hungry Workhorse, a digital and culture transformation firm.
