How supervised AI enables real-time monitoring of customer interactions
The delayed evaluation of customer interactions limits companies’ ability to detect service problems before they affect the user experience.
The BPO industry in Colombia closed 2025 with operating revenues of COP 56.5 trillion, accounting for 3.3% of gross domestic product (GDP), according to a report by the Colombian BPO Association (BPrO). In this sector, maintaining consistent service quality is a challenge given the volume of daily interactions between companies and consumers.
The impact of a poor experience is also reflected in purchasing decisions. According to PwC’s 2025 customer experience survey, 52% of the 5,511 consumers surveyed said they had stopped using or buying products from a brand because of a poor experience with its products or services. In addition, 29% said they had done so because of a poor customer service experience.
Service quality can gradually deteriorate as a result of signals such as responses that fail to address customers’ needs, requests that customers have to repeat, excessively long conversations, or expressions of frustration. By the time these problems appear in traditional performance indicators, the deterioration may have been building up for weeks.
Against this backdrop, Covisian has developed a patented supervised artificial intelligence model that enables AI to converse with customers while a human agent supervises several interactions simultaneously and intervenes when necessary. The company manages more than one million interactions daily.
According to César López, CEO for Iberia and Latin America at Covisian:
“AI can analyze large volumes of interactions and help identify deviations that manual supervision would find difficult to detect.”
The model is structured around five areas related to supervision, operational management, and the use of the capacity freed up.
1. Monitor 100% of operations in real time.
Traditional quality control relies on manually reviewing a small sample of calls, limiting visibility into the operation as a whole.
With conversational analysis and real-time supervision, evaluations can be conducted during each interaction rather than being limited to a subsequent review of a sample.
The shift involves moving from identifying what happened in selected calls to observing what is happening across the entire operation while the service is underway.
2. Monitor the process, not just the conversation.
Post-interaction monitoring can delay responses to problems that affect customer satisfaction and loyalty. Unlike traditional systems that track compliance with scripts, Covisian’s model incorporates active supervision of processes and the execution of critical tasks in real time.
Artificial intelligence analyzes interactions and alerts the agent to critical points, while the agent intervenes to manage the situation and maintain service quality.
3. AI converses while the human agent supervises.
Rather than replacing the agent with a bot or limiting artificial intelligence to an assistance role, the model allows a single professional to supervise several conversations simultaneously.
AI manages the dialogue, identifies signs of friction, and generates alerts. The agent contributes judgment, empathy, and decision-making skills when human intervention is needed. As a result, supervision does not depend solely on the conversations one person can handle individually.
4. Measure handling time, not calls avoided.
The number of calls avoided through artificial intelligence can provide a limited view of efficiency because it mainly counts shorter, simpler interactions, while complex cases retain their costs.
The model proposes measuring the time saved across all interactions, including the most complex ones. To do so, it incorporates improvements such as automated identification and authentication, automatic completion of the customer relationship management (CRM) system, assisted information searches, and call summaries.
Each reduction in the time spent on these tasks can free up operational capacity for other activities.
5. Reinvest the capacity freed up.
Reducing the time required for each interaction frees up capacity without changing team size. According to Covisian’s approach, the goal is not to reduce staff but to use that capacity to handle demand peaks without compromising quality, provide service through channels and during hours that were previously not covered, or improve service levels.
The approach seeks to link efficiency gains to greater responsiveness rather than measuring results exclusively by the number of automated interactions.
“The future of customer experience is not just about responding faster. It is about better understanding each interaction, detecting early warning signs, and acting before friction turns into a poor experience,” López said.
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