Unlocking AI's Full Potential with
Humans in the Loop

By: Miguel Carames

AI is already a critical component of today’s telecom networks. However, utilization does not imply that operators are ready to hand over the control and operation of their networks to AI agents. According to the TM Forum, 46 percent of CSPs surveyed said they have deployed AI in production for specific functions within IT & digital systems and networks, with slightly fewer in operations and service management (41 percent) and security (39 percent). We are at the tipping point of entering the AI-enabled era. 

AI has the potential to reduce costs, drive growth, differentiate customer experience, and ensure secure, reliable operations.  In fact, it's imperative to achieve the promise of autonomous networks and deliver a “Zero X” experience – zero wait, zero touch, zero trouble through AI-enabled self-management, configuration, optimization, and security. According to the World Economic Forum, reaching Zero X operations is estimated to be worth $794 million per year in cost savings, efficiency gains, and revenue generation opportunities for CSPs.

But we are not there yet. AI still has a way to go before it can prove that it is trustworthy to be handed the keys to the operations. AI-related operational and security incidents remain a common challenge for CSPs around the globe. In fact, the TM Forum found that only 4 percent of CSPs reported no AI-related incidents in the past 12 months. The report revealed that the most AI-related risk came from:

• 47 percent said that AI generated inaccurate outputs that affected operations or customers in the past year

• 28 percent had experienced incidents of prompt injection, whereby hackers disguised malicious inputs as legitimate prompts

• 16 percent had suffered AI model tampering or “poisoning.”

These statistics underscore why a human-in-the-loop approach – where people review, validate, and guide AI decisions - isn't optional: it keeps operations efficient and reliable while catching risks such as configuration drift (where configurations gradually slip from their intended baseline) and model hallucinations (inaccurate or nonsensical output), problems that surface only through sustained human oversight and deep domain knowledge. Human-in-the-loop isn’t just about risk mitigation, though; it is also necessary to advance the journey toward autonomous operations. By providing context, validating AI recommendations, and continuously refining the models and the reference data that grounds those models, human experts help AI become more accurate, reliable, and trustworthy over time.

Here are the top five areas where human-in-the-loop is critical to unlocking AI’s full potential.

1. Risk Reasoning

Risk management in telecom has long relied on predefined controls, rule-based monitoring, and manual analysis to catch revenue leaks and fraud threats — an approach that's straining under the growing complexity of telecom services, digital payments, and fraud tactics. AI changes this by providing reasoning over risk signals directly: it identifies emerging risks, assesses key risk indicators, and gives fraud management, business assurance, finance, and operations teams the lead time to intervene before issues escalate. It can also map that reasoning onto a broader universe of potential solutions, pinpointing key risk areas and giving teams a framework to strengthen resilience and sharpen decision-making. An agent configured to view charging and revenue through an IFRS 15 lens, for instance, can explain the impact on performance obligations, allocation, and recognition timing, turning fraud and revenue assurance signals into explanations that finance and operations teams can investigate and act on.

Where the value of human-in-the-loop is required is where analysts validate the AI's reasoning, correct its blind spots, and address concerns around bias, discrimination, liability, and compliance. As a result, they gain true real-time visibility into their risk posture so that they can respond to incidents faster and with more confidence and can develop a unified risk strategy across their operations, from billing and revenue collection to fraud management, revenue assurance, and compliance.

2. Testing and Monitoring Root Cause Analysis

That same interplay between AI and human judgment shows up just as clearly on the network side, where data volumes are exploding. According to PWC, data consumption is expected to increase from 3.4 million petabytes (PB) in 2022 to 9.7 million petabytes (PB) by 2027.  With the help of AI, it is possible to turn the sheer volume, variety, and velocity of raw data into instant KPI reporting and benchmarking, flagging network issues, and triggering alarms before customers ever notice.  However, true Root Cause Analysis (RCA) automation is what transforms testing and monitoring from reactive troubleshooting into proactive assurance. Unleashing the potential of RCA automation still requires humans in the loop to validate AI-flagged anomalies, apply operational context to complex, multi-vendor issues, refine the balance between deterministic and probabilistic AI that ensures that even the more nuanced scenarios are properly analyzed and the best possible recommendations described. The opportunity for CSPs is to optimize network performance — ensuring superior Quality of Experience (QoE) and keeping operators ahead of industry trends.