Steering of Roaming is becoming difficult to manage as partner network performance and subscriber expectations evolve faster than operational teams can respond. When service alerts or faults arise, teams must quickly decide what to adjust, but this process often depends on a limited number of specialists.
For most users, optimizing steering performance is complex. It requires interpreting alerts, troubleshooting issues, identifying the right configuration levers, and validating changes through trial and error. This leads to three common challenges:
Slow response to incidents: Resolution times can be long and are often reactive, with additional delays caused by extended change management cycles to identify the right configuration.
Inconsistent optimization quality: When only specific scenarios are addressed, results can vary, leading to uneven steering performance and inconsistent subscriber experience.
A widening expert gap: Critical knowledge is concentrated among a few specialists, while other users lack the guidance and confidence to act effectively.
The result is increased operational friction, slower performance improvements, higher roaming costs, and missed opportunities to keep steering aligned with business objectives.
TOMIA’s Autonomous Service Optimization uses AI to translate service alerts and faults into clear, configuration-level recommendations that teams can act on immediately.
Rather than requiring users to interpret alerts and log data manually, AI analyzes context and performance signals to suggest targeted optimizations, such as:
Increasing the number of network redirections when performance degradation is detected
Adjusting or narrowing the manual selection time frame to prevent subscribers from being stuck on underperforming networks
Fine-tuning steering rules and thresholds to improve consistency
Highlighting the changes most likely to deliver the greatest impact, enabling confident prioritization
This approach reduces reliance on specialist expertise by embedding best practice logic directly into the workflow. It enables everyday users to act faster and more effectively, while allowing experts to focus on higher-value activities such as optimization strategy, policy design, and exception handling.
In essence, AI closes the loop between detection and action, turning alerts into recommendations and recommendations into optimization.
Autonomous Service Optimization delivers tangible benefits across operations, service quality, and overall performance:
Faster corrective action: Teams move from identifying an alert to applying optimization recommendations in minutes, not hours.
More consistent steering performance: Recommendations standardize how issues are addressed, improving traffic distribution accuracy.
Reduced reliance on specialists: Guided recommendations empower a broader set of users, narrowing the gap between experts and the wider operations team.
Lower operational effort: Less manual tuning, fewer trial and error cycles, and reduced time spent hunting through configurations.
Better customer experience and revenue protection: Quicker optimization reduces the duration and impact of service issues, helping protect roaming experience, satisfaction, and commercial performance.
Explore TOMIA’s AI‑driven approach to smarter roaming here.