According to the Ericsson Mobility Report 2024, the global mobile data volume will reach 529 exabytes per month by 2030. However, with the massive boost of 5G and IoT systems, the telecom industry is facing latency issues, network issues, and poor customer experience.
Traditional cloud-based architecture cannot deal with this pressure. This is the world of AI, especially Edge AI. It can improve telecom operations, remove bottlenecks, and help with better decision-making. Do you want to implement it within the industry? You have to work with an AI software development company.
Why does the telecom industry need Edge AI?
- Reduce 80% network latency and 30-40% network failures
- Detect cyber anomalies in real-time and block them 40% faster
Reducing Churn & Creating Profit Engines
Proactive Issue Resolution
AI-driven anomaly detection reduces customer-impacting outages by 68%, cutting churn rates by 14% while lowering call center costs by 23%. However, to achieve this, the telecom company owner should invest in a good AI software development company.
Real-Time Personalization
Localized data processing enables hyper-targeted ads with 34% higher click-through rates, directly boosting ad-driven revenue.
Instant Support
Edge chatbots resolve 83% of inquiries in under 50 seconds, improving customer satisfaction scores by 29%.
Improving operational efficiency
AI-driven orchestration reduces manual network tasks by 50%, accelerating 5G slice deployments for enterprise clients. Edge AI cuts latency to <10ms, enabling mission-critical IoT contracts that command 22–35% revenue premiums.
Creating new revenue streams
Selling Predictive Service Level Agreements (SLAs)
By leveraging advanced analytics, telecom operators can offer SLAs that guarantee uptime based on predictive models, potentially generating millions in additional revenue from enterprise clients seeking reliability.
Monetizing IoT Connectivity
As IoT devices proliferate, telecoms can capitalize on this trend by offering specialized bandwidth contracts tailored for connected devices, which could account for a significant portion of overall revenue by 2030.
Zero-Touch Automation
Automation platforms powered by edge AI reduce manual interventions in network management. This includes provisioning infrastructure or upgrading systems autonomously, leading to faster deployments and lower operational costs.
Resource allocation on demand
AI algorithms optimize resource allocation by dynamically adjusting network capacity based on demand. For example, during peak hours or major events, traffic can be rerouted instantly to avoid congestion.
Better customer experience
The telecom industry can use Edge AI to provide customized plans to their users. Using the inherent NLP (Natural Language Processing) algorithm, the companies can come up with better pricing plans or troubleshoot to create a more proactive image in the marketplace.
Top four pillars of Edge AI implementation
Network optimization
Edge AI requires specialized hardware installed throughout the distributed network and within the local servers. These things can improve on-site processing capacity, and offer seamless operation and customer experience. If you need to integrate Edge AI with existing infrastructure, you have to consult an AI software development company. Later, you can work with self-optimizing networks to adjust network parameters, based on traffic.
AI systems analyze performance data to detect anomalies that may indicate potential equipment failures. This proactive approach allows operators to intervene before disruptions occur, significantly reducing downtime.
AI can also monitor environmental factors affecting infrastructure, such as extreme weather conditions, ensuring resiliency in challenging environments.
Reliability and scalability
As telecom companies expand their services, they must manage a growing number of edge devices distributed across various locations.
Each device requires individual configuration, maintenance, and updates, which can create significant overhead for IT teams. This needs strategic intervention, involving automated deployment, and management solutions that can streamline these processes.
With an increasing number of devices, the synchronization between data flows may become challenging. The age-old networks will be unable to handle it, hence, the telecom industry requires robust data management frameworks to offer consistent performance across all devices.
So what should companies evaluate before choosing Edge AI solutions?
- Total cost of scaling including hardware and software costs
- Cost for predictive maintenance, and additional support
Privacy
With the rise of Edge AI and AI agents, ensuring data privacy and security has become an area of concern for telecom operators.
You can use Edge AI to process private data locally, without transmitting it over networks and exposing it to risks. It gives you an edge to comply with data compliance regulations and jurisdictions and protects your brand image in the process.
Edge devices can suffer from physical tampering and cyber attacks. What should you consider in these cases?
- Comprehensive security protocols and regular security audits
- Data encryption
- Access control to maintain data integrity and confidentiality
So how to ensure data security in edge AI, according to an AI software development company?
- Understand the principles of data anonymization
- How to balance data anonymization with computational efficiency?
- How to adhere to privacy regulations while maintaining high performance?
How an AI software development company can help?
Develop done-for-you Edge AI solutions
An AI software development company has the experience to create done-for-you, specialized Edge AI solutions. At Chirpn, we understand that not every telecom operator will have the same requirement or vision. We will work with you to understand your concerns and suggest ideas to work around them. We have experience in developing solutions to solve issues like network performance, and downtime, and offer better and more innovative services to customers.
Integrating Edge AI with traditional systems
Why do most Edge AI implementation strategy gets “nipped in the bud”? It’s due to the existing legacy infrastructure. Yes, some instances require an overhaul, however, we can mostly integrate Edge AI with the traditional systems. This includes setting the entire system up, resolving any compatibility issues, and working on developing customized middleware to establish communication channels. With these advanced solutions, telecom operators can minimize disruptions, and improve service quality, without worrying about overheads.
Deploy AI-powered automation for real-time decision-making
As an AI software development company. Chirpn will integrate machine learning algorithms into network management systems to make them suitable for Edge AI. This will help telecom operators combat traffic congestion and equipment malfunction, and work on dynamic service adjustments. With these advanced systems, telecom units can address issues, work resource optimization, and allocation and improve customer satisfaction by engaging in data-driven actions.
Enhance security and compliance
An experienced AI software development company can bolster telecom security by integrating robust security measures into Edge AI deployments.
We will design better and more secure data protocols that come with proper encryption standards. Our team will introduce multi-layered authentication processes to protect sensitive data from outside cyber-attacks. While working with us, you don’t have to worry about compliance, industry regulations, or privacy frameworks, we will take care of that. The goal is to build hyper-resilient and reliable networks to offer the best connectivity to users.
