Rakuten Mobile is steadily weaving artificial intelligence into its Open RAN operations in Japan, presenting a blueprint that industry observers view as a potential model for AI-powered telecommunications infrastructure worldwide. The company’s leadership argues that the integration marks the first commercially scalable Open RAN deployment of its kind, a milestone underscored by Rakuten Mobile’s profitability and EBITDA positivity. With the network now generating sustained financial returns, the operator is attracting significant interest from peers who are exploring how AI can reshape operational metrics while simultaneously expanding network capabilities. Leaders within the organization say the momentum is grounded in practicality: operators want not only to optimize current performance but also to unlock new monetisation opportunities as AI-driven processes become routine in network management.
The broader implication of Rakuten Mobile’s approach lies in how AI is deployed across the lifecycle of network operations, from planning and deployment to ongoing optimization and sustainability. Anshul Bhatt, who heads the Open RAN products unit, has repeatedly emphasized that adoption is not merely about adding AI capabilities; it’s about rethinking the entire workflow of network construction, maintenance, and evolution. As operators consider how to scale AI responsibly, Rakuten Mobile’s experience is being framed as a practical case study in achieving measurable gains—improved deployment velocity, reduced operating costs, and a clearer path toward monetisation of AI-enabled services. The company reports strong appetite from other operators who are evaluating whether AI can meaningfully transform KPIs such as time-to-availability, capital efficiency, and the reliability of their networks.
This comprehensive rewrite delves into Rakuten Mobile’s AI strategy, focusing on concrete mechanisms—especially Site Manager, autonomous network workflows, and the emerging concept of an AI site companion. It also explores sustainability initiatives tied to AI-driven network optimization, with a particular emphasis on green slicing as a pathway to energy efficiency without compromising performance. Throughout, the aim is to present a thorough, technically grounded understanding of how Rakuten Mobile is embedding AI across operations, why this matters for telcos, and what it portends for the global deployment of AI-enabled networks.
A foundational shift: AI-embedded Open RAN as a scalable reality
Rakuten Mobile’s Open RAN deployment in Japan is being positioned as a watershed moment for the telecom industry. The company’s executives frame the initiative as a strategic milestone that proves AI can be integrated at scale within a commercial network, delivering tangible benefits beyond theoretical gains. The assertion that this is the world’s first commercially scalable Open RAN deployment is not simply a marketing claim; it is grounded in the practical realities of running an EBITDA-positive, revenue-generating network with AI-enabled operations. As Rakuten Mobile reaches profitability, the credibility of its model increases, and the conversation in the global telecom community shifts from questions of feasibility to questions of implementation and monetisation.
Industry observers report that the growing interest is driven by a combination of factors. First, there is a recognition that AI can unlock end-to-end visibility across complex network lifecycles, enabling operators to compress timelines from planning to rollout while maintaining high standards of quality. Second, the availability of tools that can operate at the edge and in real time—without introducing unacceptable latency or risk—makes AI-driven automation more practical for mission-critical infrastructure. Third, operators are increasingly aware of the potential to turn AI-enabled efficiencies into new revenue streams, whether by offering enhanced service levels, reducing total cost of ownership, or delivering data-driven services that leverage AI for network optimization.
Rakuten Mobile’s leadership stresses that monetisation sits alongside efficiency and reliability as a central pillar of the AI strategy. The company is exploring how AI-enabled processes can create value not only by lowering capex and opex but also by enabling new pricing models, service level guarantees, and predictive maintenance that reduces costly downtime. The emphasis on monetisation reflects a broader industry shift toward outcomes-based models in which operators seek to demonstrate measurable improvements in network performance and lifecycle costs, then translate those improvements into customer value and revenue opportunities. The combination of profitability, practical AI deployment, and market interest positions Rakuten Mobile as a possible blueprint for operators worldwide as they navigate the complex transition to AI-enhanced networks.
Modernising site management: The Site Manager as a cornerstone of autonomous networks
A central element of Rakuten Mobile’s AI strategy is the modernization of site management through a dedicated platform known as Site Manager. The platform is designed to streamline deployment workflows across the site development lifecycle, leveraging machine learning to identify bottlenecks, automate repetitive tasks, and provide real-time visibility into progress, risks, and compliance. The overarching objective is to establish a robust, data-driven foundation that supports autonomous network operations—ensuring that site construction, upgrades, and even future satellite integration can be executed with precision and speed.
From the outset, Rakuten Mobile has framed site management as a transformative capability rather than a mere project-management tool. The Site Manager platform targets inefficiencies that have historically plagued telecom deployments, particularly in the complex, multi-party environment of site development. The conventional approach—relying on manual processes such as Excel-based tracking, emails, and ad hoc communications—was found to introduce delays, miscommunications, and a lack of end-to-end visibility. In contrast, the AI-enhanced site management system seeks to create a cohesive, auditable, and automated pipeline that reduces time-to-deploy, enhances collaboration among contractors and internal teams, and ensures that every stakeholder aligns with the approved design and compliance requirements.
The platform’s capabilities extend across the full spectrum of site development, starting with the initial network planning and nominal planning stages and continuing through to the execution and installation phases. Rakuten Mobile recognizes that site development is inherently complex, characterized by iterative back-and-forth, design validation, and strict adherence to regulatory and architectural standards. The AI-infused approach is designed to minimize back-and-forth frictions by providing intelligent recommendations, flagging deviations from design intent, and enabling rapid course corrections. In practical terms, the Site Manager helps to ensure that rollouts, upgrades, and even future satellite integrations can be performed with a consistent level of quality while also enabling the organization to measure progress with high fidelity.
Beyond process automation, the platform also acts as a repository of institutional knowledge. By consolidating historical data from past deployments, it learns patterns that can inform future projects, enabling faster decision-making and more accurate forecasting. The AI layer can anticipate potential obstacles—such as regulatory bottlenecks, supply chain constraints, or engineering design conflicts—and suggest mitigations before they become critical impediments. Such predictive capabilities are particularly valuable in large-scale deployments where timing and resource allocation are tightly coupled to project success.
Site Manager also plays a crucial role in the integration of AI into the field. For on-site personnel, the platform provides dashboards and guided workflows that align with the real-time realities of construction and installation work. It helps field engineers to verify that installations conform to the approved design, ensuring that the physical build matches the intended architecture. This alignment between digital design and physical execution is essential for the reliability and performance of the network, particularly in a deployment where AI is expected to autonomously optimize operations over time.
As Rakuten Mobile positions this solution within the broader Open RAN ecosystem, Site Manager is designed to be interoperable with other AI-enabled tools and platforms, enabling a coherent, end-to-end AI-enabled workflow. The ambition is to create a scalable framework that can be replicated and adapted across different markets and regulatory environments, allowing operators to adopt a common language for AI-driven site development while preserving the flexibility needed to accommodate local conditions and constraints. The platform’s success thus far underscores the potential of AI to bring a new level of discipline, efficiency, and transparency to site development in telecoms, ultimately enabling networks that are not only faster to deploy but also more resilient and sustainable over their lifecycles.
Building an AI site companion: empowering field engineers with vision, language, and automation
A standout element of Rakuten Mobile’s AI strategy is the development of an “AI site companion”—an integrated set of AI capabilities designed to assist field engineers during construction and installation. The concept centers on using computer vision and large language models to augment human workers, providing real-time guidance, verification, and efficiency gains that touch multiple facets of on-site operations. The aim is to reduce the cognitive and administrative load on field engineers while improving accuracy and consistency across deployments.
At its core, the AI site companion is meant to operate as a dependable partner for engineers, rather than a replacement for human expertise. The emphasis is on collaboration: AI handles repetitive, high-volume tasks; it provides intelligent insights; and it supports decision-making with data-driven recommendations. This partnership-style approach aligns with a broader AI adoption narrative in which human workers and intelligent systems work together to achieve superior outcomes more efficiently than either could alone.
One practical application of this approach involves embedding AI directly into cameras used by field engineers during construction and installation. The AI-enabled cameras perform intelligent auditing in real time, assessing whether construction work adheres to the approved design and specifications as photographs and video are captured on-site. This capability allows for immediate feedback to workers, enabling on-the-spot corrections and reducing the likelihood of rework downstream. The benefits extend beyond quality control; real-time validation accelerates the inspection cycle and helps teams meet tight project schedules without compromising standards.
An additional facet of the AI site companion is its role in document management. Engineers and project managers frequently interact with a wide range of forms, drawings, permits, and procurement documents—often belonging to different leasing companies or real estate arrangements. The AI system can read, interpret, and autofill forms based on the underlying document data, significantly reducing manual data entry and the potential for human error. Rakuten Mobile reports that this capability can save substantial time per site, with estimates suggesting notable reductions in the hours spent on routine administrative tasks. Even small time savings, when aggregated across multiple sites and projects, translate into meaningful improvements in overall deployment velocity and cost efficiency.
The user-facing component of the AI site companion includes a conversational agent—a chatbot that engineers can query when questions arise. This chatbot is not a generic assistant; it is designed to understand the specific context of site management and to retrieve answers from the relevant documents and project data. The underlying large language model (LLM) is trained to extract and present information in a way that is practical for field use, turning raw data into actionable guidance. The chatbot thus serves as an on-demand advisor, helping engineers to navigate complex forms, verify specifications, and access the correct procedures or compliance requirements without leaving the field.
Beyond the chat interface, Rakuten Mobile envisions an increasingly agentic approach to site management. An agentic architecture implies that tasks and responsibilities can be delegated to autonomous software agents that operate with minimal human intervention, triggering actions, updating records, and coordinating related activities across the project ecosystem. The company is actively identifying opportunities to expand automation across the site development workflow, seeking to automate repetitive tasks and routine checks while maintaining strict governance and traceability. This evolution toward greater autonomy is designed to enhance consistency, reduce cycle times, and free engineers to focus on higher-value activities that require domain expertise.
In practice, the AI site companion is designed to support a spectrum of activities—from planning and design validation to on-site execution and post-implementation review. For planning, AI can digest design specifications, regulatory requirements, and historical performance data to provide recommendations on layout, equipment selection, and installation sequencing. During construction, AI-powered guidance helps technicians verify alignment with drawings, measure tolerances, and confirm the correct installation of components. For installation and commissioning, the companion can assist with readiness checks, ensure that configurations reflect the approved state, and document deviations for escalation and resolution. In the post-deployment phase, AI can support ongoing maintenance planning, trend analysis, and optimization opportunities, creating a feedback loop that informs future deployments and refinements to the site design.
Rakuten Mobile’s approach to the AI site companion is grounded in a practical recognition that field operations are where many gains in efficiency and quality can be realized. Real-time AI assistance reduces the likelihood of human error and speeds up decision-making under the challenging and often dynamic conditions of on-site work. The emphasis on AI as a supportive ally—rather than a constraint—reflects a careful balance between automation and human expertise, ensuring that workers retain critical control while benefiting from augmented capabilities. The company’s ambitions in this space are to build an architecture that is adaptable, scalable, and capable of evolving as AI technologies mature and as field needs change across different deployment contexts.
On-device intelligence: embedding AI into the field toolbox
A key characteristic of Rakuten Mobile’s AI strategy is the move toward on-device intelligence that can operate at the edge of the network and within field equipment. By embedding AI capabilities directly into cameras and other site-devices, the organization seeks to deliver fast, reliable insights without relying on centralized processing that could introduce latency or depend on bandwidth-intensive data transfers. This approach supports real-time auditing, immediate decision support, and rapid data capture, which are critical in complex deployment environments where timing and accuracy directly affect project outcomes.
In practice, the on-device AI deployment involves running computer vision algorithms on cameras used by field engineers to monitor construction and installation activities. The edge processing capability enables instantaneous assessments of whether work has been completed in line with the approved design. This immediate feedback mechanism reduces rework, shortens inspection cycles, and improves the consistency of site work across multiple teams and contractors. The on-device approach also enhances resilience, as local processing is less susceptible to network outages or intermittent connectivity, ensuring that essential checks and validations can proceed uninterrupted.
Document management is another area where AI on devices proves beneficial. On-device AI can parse documents, extract key data, and autofill forms before data is transmitted to centralized systems for storage and governance. This capability reduces manual data entry, minimizes human error, and accelerates the flow of information through the deployment pipeline. The practical impact of these capabilities translates into faster site approvals, lower administrative overhead, and improved traceability of decisions and actions taken during construction and installation.
The on-device AI strategy also extends to other field equipment beyond cameras. For instance, sensor-equipped devices and handheld tools can carry lightweight AI models that assist technicians with tasks such as verifying measurements, validating equipment configurations, and ensuring compliance with safety and regulatory standards. The combination of edge AI with robust data governance and secure data handling practices is essential to maintaining the integrity of the deployment and protecting sensitive information.
Another crucial element of on-device AI is its role in documenting and sharing knowledge across the organization. By capturing insights at the point of action, the technology facilitates the creation of rich, granular records that can be analyzed later for continuous improvement. This approach supports a culture of evidence-based decision-making, where lessons learned from each site inform the design and execution of future deployments. Rakuten Mobile views this as part of a broader shift toward data-driven operations, enabling the company to scale its AI capabilities while maintaining high standards of quality and accountability.
AI-driven workflow automation: from planning to deployment
The AI strategy extends beyond on-site intelligence to a comprehensive automation of workflows that span the project lifecycle. By applying machine learning to planning, resource allocation, risk assessment, and scheduling, Rakuten Mobile aims to create end-to-end processes that are more predictable, transparent, and efficient. Such automation reduces manual effort, minimizes subjective variance, and produces consistent outcomes across projects of varying size and complexity.
In planning stages, AI can synthesize inputs from multiple sources—customer requirements, regulatory constraints, and historical project data—to generate optimized deployment plans. It can propose timelines, identify critical path activities, and highlight potential dependencies that could affect delivery. This capability reduces reliance on manual planning spreadsheets and accelerates the convergence of design, finance, and operations teams around a shared, data-driven plan. In the execution phase, AI-driven workflows coordinate procurement, logistics, field assignments, and quality control tasks. Automated triggers can alert teams to schedule changes, supply shortages, or emerging risks, enabling rapid adaptation to changing circumstances while preserving schedule integrity.
A significant advantage of AI-driven workflows is improved cross-functional coordination. In traditional telecom deployments, misalignment between planning, procurement, site construction, and commissioning can lead to delays and cost overruns. An AI-enabled system provides a common, auditable record of decisions, responsibilities, and timelines. It also fosters accountability by making it easier to trace the origins of delays or issues to specific steps, teams, or external partners. This level of visibility supports continuous improvement, enabling Rakuten Mobile and its partners to refine processes, replicate success patterns, and scale best practices across multiple sites and markets.
The automation journey is not without challenges. Ensuring data quality, maintaining governance, and protecting sensitive information require robust security and compliance frameworks. Integrating AI tools with legacy systems and diverse stakeholder ecosystems presents technical and organizational hurdles. Rakuten Mobile has approached these challenges with a layered strategy that prioritizes data integrity, clear ownership of AI outputs, and transparent decision-making processes. This approach helps to mitigate risk while delivering the practical benefits of automation, such as faster cycles, higher consistency, and better forecasting accuracy for deployments.
Sustainability at the core: green slicing and energy-aware networks
Another distinctive pillar of Rakuten Mobile’s AI program is its emphasis on sustainability, particularly in the context of network slicing. The company has introduced the concept of green slicing—a form of energy-efficient network slicing designed to align performance with environmental goals and cost efficiency. In this framework, certain network slices are optimized not only for service quality and capacity but also for energy consumption, enabling operators to deliver adequate performance while reducing power usage. This approach resonates with a broader industry imperative to balance customer demands with environmental responsibility and long-term operational viability.
Rakuten Mobile positions green slicing as a response to the dual drivers of operational cost pressure and sustainability expectations from customers, regulators, and society at large. By engineering energy-aware slices, the company seeks to demonstrate that high-performance networks can be delivered in a manner that minimizes waste and reduces carbon footprints. The concept aligns with the autonomous network journey that many operators are pursuing, in which intelligent orchestration and optimization lead to more efficient resource utilization across the network—without sacrificing the user experience.
From a technical perspective, green slicing involves careful coordination of compute resources, radio access network (RAN) configurations, and power management strategies. It requires sophisticated analytics to monitor energy consumption, performance metrics, and usage patterns, enabling dynamic adjustments to slice parameters in line with real-time conditions. In practice, this translates into adaptive power allocation, selective activation of network components, and intelligent scaling of services based on demand. Rakuten Mobile’s AI-driven approach seeks to automate these decisions, ensuring that energy efficiency is not a manual afterthought but an intrinsic characteristic of the network’s operation.
The sustainability narrative also extends to broader environmental goals, such as reducing emissions associated with site operations, equipment heating, and cooling. AI-enabled optimization can contribute to smarter cooling strategies, predictive maintenance that prevents losses due to equipment failure, and more efficient deployment practices that minimize material waste. By integrating environmental objectives with performance and reliability goals, Rakuten Mobile is illustrating how AI can drive a holistic improvement in network economics and ecological impact.
Industry observers note that green slicing and similar energy-aware strategies have the potential to transform operator economics by delivering long-term cost savings, enabling more aggressive capacity planning, and differentiating services through sustainability assurances. The ability to articulate measurable environmental benefits alongside performance improvements can be a compelling value proposition for enterprise customers and network partners who are increasingly mindful of their own sustainability commitments. Rakuten Mobile’s work in this area may serve as a blueprint for other operators seeking to reconcile rapid network growth with responsible energy use and sustainable business practices.
Economic and business implications: monetisation, ROI, and strategic value
A consistent theme in Rakuten Mobile’s discourse is the interplay between operational efficiency and monetisation. The company argues that AI-enabled improvements in deployment velocity, maintenance, and network optimization create a robust business case that extends beyond cost savings. By delivering faster rollouts, better uptime, and more predictable project outcomes, operators can unlock new value propositions for customers and partners and potentially command premium service levels. The monetisation discussion also encompasses data-driven services, where insights derived from AI-enabled network management could enable new offerings or partnerships that monetize operational intelligence without compromising user privacy and data security.
Executives emphasize that the AI-driven improvements are not merely incremental; they have the potential to alter the economics of network deployment and operation. Faster time-to-market means operators can monetize capacity growth sooner, while reduced field visits and administrative overhead translate into significant cost reductions. The aggregate impact on profitability strengthens the business case for AI investments and may influence capital allocation decisions across the broader telecommunications ecosystem. Moreover, the AI-enabled capability to demonstrate precise analytics, performance metrics, and predictive maintenance can be leveraged in customer engagements, supporting service-level commitments, warranty programs, and service guarantees that differentiate operators in competitive markets.
Rakuten Mobile’s experience suggests that industry peers are eager to learn how to translate AI capabilities into tangible commercial outcomes. Operators are exploring models that tie AI-driven performance gains to pricing structures, service-level commitments, and partner collaborations. The monetisation narrative also touches on the possibility of offering AI-enabled deployment-as-a-service, where operators or equipment vendors can leverage Rakuten Mobile’s platform and know-how to accelerate their own AI-enabled deployments. While such business models require careful alignment around data governance, interoperability, and intellectual property rights, the potential for scalable revenue streams is a compelling driver for continued AI investment across the telecom sector.
Additionally, the emphasis on autonomy and agentic workflows may lead to a shift in the cost structure of network operations. As automation reduces manual intervention, the portion of the budget allocated to manual labor may shrink, allowing funds to be redirected toward AI maintenance, model improvement, and advanced analytics. This transition, if managed carefully, can enhance overall return on investment and support long-term strategic objectives such as global expansion, standardization of processes, and faster integration of future technologies, including satellite communications or advanced edge services.
Industry implications: global adoption, interoperability, and governance
Rakuten Mobile’s AI-enabled Open RAN approach holds meaningful implications for the broader telecom industry. If the model proven in Japan proves scalable and replicable, other operators may be inclined to adopt similar AI-driven workflows, with the potential for cross-market standardization around AI-assisted site development, field operations, and autonomous management processes. The global telco ecosystem could benefit from a shared set of best practices, common architectural patterns, and interoperable tools that enable faster deployment and safer integration of AI capabilities across diverse networks.
Interoperability remains a central challenge as operators seek to combine AI systems with heterogeneous equipment, vendor ecosystems, and regulatory regimes. Rakuten Mobile’s emphasis on a cohesive architecture—where AI components, data governance, and security measures align with Open RAN principles—addresses some of these concerns by promoting modularity, transparency, and auditable decision-making. A key consideration for any multinational rollout will be the harmonization of standards and the alignment of governance frameworks to support consistent AI behavior, data protection, and compliance across jurisdictions.
Security and privacy considerations are equally critical in the AI-driven telco landscape. As AI tools handle sensitive network and customer data, robust safeguards must govern data access, model training, and decision outputs. Operators will need to implement rigorous risk management protocols, including access controls, encryption, and robust auditing capabilities to maintain trust and resilience. Rakuten Mobile’s approach to governance, documentation, and traceability will be a key component of its broader strategy to ensure responsible AI usage within complex, mission-critical networks.
From a regulatory perspective, policymakers will be closely watching how AI-enabled networks address issues such as spectrum efficiency, radio resource management, and energy consumption. Green slicing and energy-aware network optimization intersect with environmental reporting and energy policy considerations in many regions. As telcos propose ambitious AI-driven improvements, regulators may require transparent reporting on energy savings, performance outcomes, and potential social impacts of autonomous network operations. The ability to demonstrate measurable benefits while maintaining compliance will be central to gaining regulatory acceptance and public trust.
The industry-wide implications also extend to supply chain and talent development. As AI capabilities become more embedded in network operations, there will be increased demand for personnel with expertise in data science, AI engineering, network automation, and cybersecurity. Operators will need to recruit, train, and retain talent capable of designing, deploying, and maintaining AI-driven systems, while vendors and research institutions collaborate to create standardized tools and platforms that simplify integration and ensure reliability. The ongoing dialogue among operators, vendors, and academia will shape the evolution of AI-enabled telecommunications for years to come.
Future roadmap: scaling, iteration, and continuous improvement
Looking ahead, Rakuten Mobile’s strategy envisions scaling the AI-enabled Open RAN ecosystem by expanding Site Manager’s capabilities, refining the AI site companion, and extending the green slicing framework to broader network architectures. The roadmap emphasizes iteration—learning from deployment data, refining models, and expanding the use cases that AI can address across planning, construction, operation, and optimization. The organization expects to broaden the deployment footprint, both within Japan and possibly in additional markets, while preserving core principles of data integrity, governance, and transparent decision-making.
A critical area of focus is the ongoing enhancement of agentic automation. As AI models mature and new capabilities emerge, Rakuten Mobile intends to advance from automated routines to more autonomous decision-making across a broader set of deployment activities. This progression requires careful governance, risk management, and continuous validation to ensure that the autonomy delivered by AI aligns with operator objectives, safety standards, and customer requirements. The company envisions a future in which autonomous network operations operate at scale, accelerating deployment cycles, reducing manual interventions, and delivering stable, predictable outcomes across diverse network environments.
Another dimension of the roadmap involves deeper integration with sustainability programs. Green slicing and energy-aware network optimization are early steps in a broader ambition to align network performance with environmental stewardship. The long-term plan is to embed energy efficiency into the core of network design and operations, ensuring that AI-driven improvements yield measurable reductions in energy consumption without compromising the quality or reliability of services. This alignment of performance, profitability, and sustainability will be a defining feature of Rakuten Mobile’s strategy as it expands AI-enabled capabilities beyond the current scope.
As Rakuten Mobile continues to evolve, the company remains attentive to industry feedback and market dynamics. It seeks to balance innovation with reliability, ensuring that new AI features are rigorously tested, well-documented, and supported by robust governance. The journey toward AI-augmented networks is both incremental and transformative, requiring disciplined execution, cross-functional collaboration, and a disciplined focus on customer value. By maintaining a clear focus on concrete outcomes—faster deployments, lower costs, better quality, and sustainable growth—the company aims to inspire confidence among operators, partners, and customers as the telecom sector navigates a future increasingly shaped by intelligent automation.
Industry-wide implications: lessons, cautions, and opportunities
Rakuten Mobile’s experience offers a rich set of insights for the telecommunications industry at large. First, the case underscores the value of coupling AI-driven process automation with a strong architectural foundation. By embedding AI across planning, site development, field operations, and ongoing optimization, operators can achieve end-to-end improvements that are more impactful than piecemeal AI implementations. The Site Manager, AI site companion, and edge-enabled capabilities collectively form a cohesive platform that demonstrates how AI can be orchestrated across a complex, multi-stakeholder ecosystem to deliver reliable, scalable results.
Second, the emphasis on monetisation signals a shift in how operators will evaluate AI investments. The ability to demonstrate tangible cost savings, accelerated deployment timelines, and enhanced service delivery will be essential for securing budget approvals and aligning with business objectives. The broader industry may see a diversification of AI-enabled offerings—from deployment acceleration services to managed automation platforms—that broaden revenue opportunities beyond traditional network operations.
Third, the focus on sustainability through concepts like green slicing highlights an emergent intersection between AI, network design, and environmental stewardship. As operators contend with rising energy costs and stringent sustainability expectations, AI-enabled energy management becomes a differentiator. The ability to quantify energy savings and present environmental benefits alongside performance gains strengthens the business case for AI while addressing broader societal concerns about the environmental footprint of digital infrastructure.
Lastly, Rakuten Mobile’s approach invites a broader conversation about governance, ethics, and security in AI-enabled networks. The deployment of AI across critical infrastructure requires robust risk management, transparent decision-making, and rigorous adherence to safety and privacy standards. The industry’s collective experience will shape the development of governance frameworks, standardization efforts, and security practices that enable safer and more effective adoption of AI in telco networks.
Conclusion
Rakuten Mobile’s AI-driven strategy for Open RAN in Japan represents a substantial step toward a future where telcos deploy, operate, and optimise networks with advanced AI capabilities. By embedding AI across site management, field operations, and autonomous workflows, the company is creating a practical, scalable blueprint that demonstrates real-world benefits, from faster rollouts and reduced costs to enhanced sustainability through green slicing. The AI site companion and on-device intelligence illustrate how human engineers and intelligent systems can collaborate to improve accuracy, efficiency, and knowledge sharing on the ground. As the industry observes Rakuten Mobile’s progress, the potential for global adoption grows, tempered by the need for careful governance, interoperability, and security. The ongoing evolution of AI-enabled telecom networks promises to reshape how operators design, deploy, and sustain their infrastructure, with a clear trajectory toward more autonomous, energy-efficient, and economically viable networks that meet the demands of a rapidly digital world.