For years, the business process outsourcing (BPO) industry has been exceptionally good at measuring and responding to service delivery outcomes, including customer satisfaction, productivity, and operational performance.
However, as AI becomes more integrated into BPO operations, this dynamic has been changing. AI is no longer simply helping employees perform their tasks but actively recommending what should happen next.
This creates a different management question regarding whether the AI agent’s recommendations were right, and why it thought it was right. With AI making a greater presence in BPO workflows, accuracy alone is no longer enough to establish trust in AI-assisted decisions or ensure meaningful human oversight.
In a given workflow, an AI system might recommend escalating a customer issue, flagging a transaction, assigning a quality score, prioritizing a case, or evaluating an employee. Since each output can influence what happens next and, in some cases, materially affect another person’s experience or livelihood, it creates a black-box problem — where employees can see the highly precise advice AI gives but none of the internal reasoning that drove it.
When the recommendation becomes the decision
Consider a scenario at a contact center where AI analyzes a conversation and recommends an escalation. The recommendation could have drawn on sentiment, language, customer history, call duration, and hundreds of other signals before reaching the employee.
But, when the employee sees the recommendation, do they understand what drove it? If not, human oversight becomes largely symbolic, where the employee technically remains in the loop but has little practical ability to review the machine’s logic or challenge it.
Meaningful human oversight requires more than just a person clicking approve. It requires enough context to understand an AI output, recognize when it may be wrong, and intervene when necessary.
This is where explainable AI becomes more than a technical concept.
Explainability is about making an AI output understandable enough for the person who must act on it. In high-impact environments, this can support validation, accountability, error detection, and effective intervention.
The increasing stakes in the Philippines
This need to understand and validate AI-driven recommendations is particularly relevant to the Philippine IT-BPM industry.
The sector generated $32 billion in revenues in 2024, equivalent to 6.9% of Philippine GDP, according to the Bangko Sentral ng Pilipinas (BSP). Its research on generative AI suggests that, at least in the near term, AI is expected to augment rather than simply replace workers while creating an urgent need for reskilling and upskilling.
This points toward a future in which Filipino BPO professionals increasingly work alongside AI systems. Because of this, the important question for BPO providers is not simply whether to trust AI or not. It is whether their employees understand enough about an AI recommendation to know when to trust it.
Blind trust creates automation bias, and blind rejection wastes technology. The objective for providers should be calibrated trust, where employees understand when an AI output is useful, when it deserves scrutiny, and when a human should override it.
Governance cannot be an afterthought
There is another reason explainability matters. BPO providers increasingly operate as extensions of their clients’ businesses. A seemingly routine process may involve financial information, healthcare data, customer authentication, employee performance, or fraud detection.
This means that an AI error does not necessarily stay inside the AI system. It can move through a workflow and eventually reach a customer, employee, client, or regulator. Therefore, explainability should not be viewed simply as a feature but as a risk control.
A useful explanation can help identify unexpected behavior, reveal problematic inputs, support audits, and give managers a basis for investigating an outcome. Without it, organizations can end up with a paradox: They have increasingly sophisticated decision systems that are increasingly difficult to hold accountable.
Recent Philippine research reinforces this challenge. AI adoption is accelerating across the IT-BPM sector, with the Institute for Labor Studies finding that 51% of surveyed employers had integrated AI tools.
However, according to BSP’s findings, organizations continue to face skills, data privacy, integration, and governance challenges as they move from experimental stages toward wider deployment.
If providers are expected to lead AI transformation while enterprises struggle to govern it, the ability to explain, monitor, and challenge AI decisions must become an integral part of the service offered.
The next BPO advantage may be trust
The BPO industry’s traditional measures of cost, productivity, service levels, and scale will remain important. What changes is what customers can reasonably expect from a provider.
As organizations increasingly rely on AI-assisted decisions, they should also be able to answer a simple question: How did your AI arrive at that outcome?
Whether the outcome is an automated quality score, a fraud alert, a customer-priority ranking, an employee assessment, or a recommended response, the ability to challenge the reasoning behind it must be consistently present.
The most effective AI-enabled operating model may not be the one that merely removes human errors, but the one that makes human decisions better informed. This requires designing AI around people rather than simply inserting AI into existing workflows.
Employees need the skills to understand AI outputs. Managers need visibility into system behavior. Organizations need documentation and monitoring.
And, where the consequences are significant, people need a meaningful ability to intervene. In the Philippine BPO industry, which has spent decades building trust through process discipline, quality management, and operational accountability, AI should strengthen this proposition, not weaken it.
While explainability does not require exposing every internal reasoning an AI model makes, it does require providing enough transparency for employees to understand, evaluate, and challenge AI-assisted decisions.
As AI moves deeper into BPO operations, the critical question will not simply focus on whether the machine can make the decision, but whether the organization can understand, challenge, and take responsibility for it. This is the difference between simply deploying AI and deriving tangible business value from it.
The author is trhe chief IT security evangelist at ManageEngine
