Ericsson uses AI agents to scan existing product catalogs and identify underused or redundant offers that contribute to operational inefficiency and bloat. This shift represents a fundamental departure from the reactive maintenance models that have dominated the telecommunications landscape for decades. As the industry moves deeper into the era of pervasive 5G connectivity, the sheer volume of daily transactions and network events has made manual oversight impossible. Communications Service Providers (CSPs) are currently navigating a transition where software-defined infrastructure must be managed with the same speed at which data travels across the fiber. The collaboration between Ericsson and Amazon Web Services (AWS) focuses on modernizing Operations Support Systems (OSS) and Business Support Systems (BSS) by integrating “agentic” artificial intelligence. This approach allows systems to do more than just follow static scripts; it enables them to reason, plan, and execute complex workflows autonomously. By addressing the “technical debt” of legacy platforms, this partnership is transforming billing and charging engines into agile revenue generators capable of supporting millions of concurrent users without the friction of outdated configurations.
Implementing Agentic Intelligence and Digital Twins
The integration of agentic AI into telecom operations marks the beginning of a self-healing and self-optimizing network era. Unlike traditional automation, which relies on “if-then” logic, agentic intelligence employs specialized workers designed to manage specific domains of the business. These agents act as a bridge between high-level business goals and the granular technical execution required to achieve them. By utilizing this model, service providers can significantly reduce the cognitive load on human operators, allowing the system to handle routine optimizations while humans focus on strategic growth and customer experience. This modernization is essential because legacy environments often lack the flexibility to adapt to the dynamic traffic patterns of modern enterprise and consumer applications.
The Power of Specialized AI Agents
These specialized AI agents possess deep domain expertise, allowing them to perform intricate tasks such as technical knowledge retrieval and capacity planning with minimal human intervention. For instance, when a system faces a complex configuration issue, an operational knowledge agent can instantly scan thousands of pages of technical documentation to find the exact resolution. This capability eliminates the hours or days of manual research typically required by support teams. Furthermore, these agents utilize topology analysis to understand how different layers of the network and business systems are interconnected. By mapping these dependencies, the AI can predict how a change in one area might impact the rest of the ecosystem, ensuring that updates are performed with a high degree of confidence and precision.
Beyond simple troubleshooting, agentic intelligence plays a vital role in proactive capacity management. Agents monitor real-time data streams to identify potential bottlenecks long before they result in service degradation for the end user. Because these agents can reason through historical patterns and current demand, they can suggest or even implement scaling actions that prevent congestion. This shift from a “break-fix” mentality to a “predict-and-prevent” model is what allows modern CSPs to maintain high service-level agreements even during periods of extreme network load. By delegating these complex, data-heavy tasks to AI, organizations can ensure that their infrastructure remains lean and efficient, avoiding the sprawl and redundancy that characterized previous generations of telecom software.
Simulations and Natural-Language Interfaces
A transformative aspect of this collaboration is the introduction of natural-language interfaces that allow operations teams to communicate with their systems as if they were speaking to a colleague. Instead of writing complex queries or navigating convoluted dashboards, a manager can simply ask the system if the current charging environment is prepared for a major upcoming event, such as a national holiday or a massive sporting event. The AI interprets this request, gathers the necessary data, and provides a clear, evidence-based answer. This democratization of data access ensures that decision-makers across the organization can gain insights without needing deep technical expertise in database management or network protocols.
To support this conversational interaction, the system utilizes “digital twins,” which are virtual replicas of the live production environment. When an operator suggests a change or asks a “what-if” question, specialized agents run simulations within this digital twin to observe the potential outcomes. This “sandbox” approach acts as a critical safety mechanism, providing a risk-free space to test configuration changes or new service launches before they go live. If a simulation reveals a potential conflict or a drop in performance, the agents can refine the plan until it meets the required safety and efficiency standards. This ensures that every automated action implemented in the production environment has been thoroughly vetted against grounded, real-world operational data.
The Technological Infrastructure of the AWS Stack
The partnership relies on a robust foundation of cloud-native services that provide the computational power and scalability required for global telecommunications. AWS contributes the underlying AI and data frameworks, while Ericsson provides the deep industry knowledge necessary to apply these tools to the specific challenges of billing and charging. This synergy allows for the “industrialization” of AI, moving technology from the realm of experimental proofs of concept into stable, repeatable production environments. By leveraging a unified stack, service providers can avoid the fragmentation that often occurs when trying to stitch together disparate software solutions, leading to a more coherent and manageable operational landscape.
Building an Industrial-Strength Foundation
At the heart of the agentic stack is Amazon Bedrock, which provides a secure gateway to various foundational models that power the platform’s reasoning and planning capabilities. To ensure that these models remain accurate and relevant to the telecom industry, Ericsson utilizes “grounding” techniques through Knowledge Bases for Amazon Bedrock. This prevents the AI from generating “hallucinations” or incorrect information by restricting its responses to verified business and operational data. This precision is non-negotiable in revenue-sensitive areas like billing, where even a minor error can lead to significant financial loss or customer dissatisfaction.
Scaling these AI capabilities across a global network requires a sophisticated management layer, which is handled by Amazon Bedrock AgentCore. This service manages the full lifecycle of AI agents, from their initial deployment to their continuous monitoring and scaling. It allows CSPs to maintain a high level of control over their autonomous systems, ensuring that agents are performing as expected and adhering to established business rules. This managed approach to AI deployment means that service providers can expand their automated operations without the need for a massive increase in specialized AI engineering staff. The result is a system that grows in intelligence and capability as the network expands, providing a future-proof foundation for the next decade of innovation.
Data Management and Predictive Analytics
Effective AI requires high-quality, well-organized data, which is where Amazon SageMaker and Amazon Neptune come into play. SageMaker is used for high-level machine learning tasks, specifically in the areas of demand forecasting and predictive maintenance. By analyzing vast amounts of historical transaction data, the system can identify subtle trends that indicate a need for resource adjustments. Meanwhile, Amazon Neptune serves as a graph database that maps the complex dependencies and relationships within the OSS/BSS environment. This mapping is essential for understanding how a single customer’s service plan interacts with the network’s physical and virtual resources, providing a 360-degree view of the business.
These tools are supported by robust AWS data services that handle the massive, real-time streams of information generated by millions of connected devices. This data is not just stored; it is continuously processed to provide actionable insights that protect revenue and improve operational efficiency. For instance, by correlating real-time charging data with network performance metrics, a CSP can identify and resolve billing discrepancies before they appear on a customer’s invoice. This level of integration ensures that the “Get Paid” pillar of the business remains secure, even as the complexity of 5G services—such as network slicing and edge computing—introduces new variables into the charging equation.
Accelerating Business Cycles and Success Stories
The ultimate goal of modernizing telecom operations is to accelerate the “idea-to-cash” cycle, reducing the time it takes for a provider to conceive a new service and begin generating revenue from it. Ericsson’s approach is defined by the philosophy of “Sell, Deliver, and Get Paid,” which aims to remove manual friction at every stage of the service lifecycle. By automating the technical translation of business requirements, providers can respond to market shifts in real-time, launching targeted offers that meet the specific needs of different consumer and enterprise segments. This agility is what allows modern telecom companies to compete with hyperscale technology firms in the digital services market.
Streamlining the Idea-to-Cash Process
A major bottleneck in traditional telecom models is the manual configuration required to launch a new product. Historically, when a marketing team wanted to introduce a new 5G data plan, it required weeks of coordination between business analysts and IT staff to translate those requirements into technical catalog entries. Ericsson’s product configuration assistant changes this by using conversational AI to gather business requirements and automatically generate the corresponding technical offerings. This automation bridges the gap between commercial intent and technical execution, allowing providers to launch services in a fraction of the time it once took.
This streamlined process also extends to the orchestration of services across the network. Once a product is defined, the system ensures that it can be accurately provisioned and charged for, regardless of the complexity of the underlying network architecture. This end-to-end automation reduces the likelihood of human error, which is a common cause of service delays and billing inaccuracies. By creating a seamless flow from the initial sale to the final bill, CSPs can improve their operational margins and provide a better experience for their customers. The ability to iterate quickly on service offerings means that providers can experiment with new business models, such as tiered 5G performance levels, with minimal financial risk.
Real-World Evidence and Industry Consensus
The practical success of this cloud-native, AI-enhanced model was demonstrated when the Dutch provider Odido migrated five million customers to an Ericsson billing system hosted on AWS. This massive undertaking was completed over a single weekend with no reported errors, an achievement that would have been nearly impossible using traditional migration methods. Since the migration, Odido has seen bill runs that are 30% faster and B2B billing cycles that are five times more efficient. These results prove that the combination of Ericsson’s domain expertise and AWS’s infrastructure can deliver tangible business outcomes at scale, reducing the costs associated with maintaining customized legacy products.
As the industry moved through 2026, analysts from firms such as Omdia and Analysys Mason recognized this partnership as a definitive reference point for the future of the sector. The consensus emerged that modernization must involve a triad of cloud migration, data transformation, and AI adoption to be successful. Organizations that embraced this integrated approach found themselves better positioned to monetize their 5G investments and protect their revenue streams from the inefficiencies of technical debt. Moving forward, the industry must prioritize the continuous refinement of these agentic models, ensuring that AI remains grounded in operational reality while expanding its reach into more complex areas of network orchestration and customer engagement. In conclusion, the shift toward agentic AI provided the necessary tools for CSPs to reclaim their roles as agile innovators in a hyper-connected world.
