Rakuten Mobile is integrating AI across its Open RAN network in Japan, aiming to set a scalable blueprint for AI-driven telecommunications infrastructure worldwide. At MWC25, Anshul Bhatt, Chief Product Officer of the OSS Business Unit, outlined how the company is building autonomous networks to help telcos manage their infrastructure more efficiently while advancing sustainability goals. The approach highlights a deep embedding of artificial intelligence into network operations, site development, and energy-conscious design. As Rakuten Mobile reports EBITDA-positive profitability and growing traction, industry peers are showing renewed interest in how AI can transform operational metrics and expand the capabilities of telecom networks. This article delves into Rakuten Mobile’s AI strategy, the technologies involved, and the implications for telcos seeking to modernize operations, cut costs, and monetize AI-driven efficiencies.
Section 1: Overview of Rakuten Mobile’s AI-Enabled Open RAN Deployment
Rakuten Mobile’s strategic embrace of AI across its Open RAN network represents a holistic effort to modernize telecommunications infrastructure through intelligent automation and data-driven decision making. The company positions its Open RAN deployment as a globally scalable model designed to improve deployment speed, operational efficiency, and network performance while maintaining cost discipline. The leadership publically emphasizes that the initiative is not merely a pilot program but a mature, commercially viable operation that has reached profitability and is generating tangible returns. This dual focus on scale and sustainability is a cornerstone of the company’s narrative, signaling to the market that AI-enabled telco infrastructure can be both financially sound and technically advanced.
From the outset, Rakuten Mobile has framed AI as a unifying layer across multiple domains of network operations. The blend of AI, automation, and open interfaces underpins a new operating paradigm where autonomous decisions support engineers rather than replace them. A key thread in Bhatt’s briefing is the emphasis on monetization—how AI-driven improvements translate into new business models, cost savings, faster rollout cycles, and potential revenue streams from enhanced service agility and network-as-a-service offerings. The company’s message resonates with operators around the world who are evaluating how AI can transform core metrics such as capital expenditure efficiency, energy consumption per traffic unit, and time-to-market for new services.
One of the central commitments of Rakuten Mobile’s approach is to modernize the lifecycle of network deployment and maintenance through a unified AI-enabled workflow. The initiative aims to reduce repetitive manual tasks, cut cycle times for planning, procurement, and installation, and increase visibility across the end-to-end process. The emphasis on autonomy is not simply about replacing human labor with machines; it is about designing systems that anticipate needs, verify compliance with design specifications, and provide actionable insights at the point of work. This philosophy aligns with a broader industry trend toward agentic AI—systems that act with purpose across discrete tasks, coordinate with human operators, and continuously improve through feedback loops.
Industry interest in Rakuten Mobile’s journey is high. Operators are looking to glean practical guidance on how to implement AI across complex telecom ecosystems, where legacy processes, diverse vendor ecosystems, and regulatory considerations create formidable integration challenges. A recurring theme in Bhatt’s commentary is the demand for clarity around monetization—how AI can unlock new value by accelerating deployments, reducing waste, and enabling new pricing or service models tied to automation-based efficiencies. In essence, Rakuten Mobile is presenting AI not only as a technical upgrade but as a strategic business capability with measurable ROI and scalable potential for telcos seeking to compete in a rapidly evolving landscape.
Rakuten Mobile’s AI strategy spans both on-premises and field deployments, with a strong emphasis on practical, near-term benefits. The company argues that AI can flatten complex, multi-stakeholder workflows by providing predictive insights, reducing errors, and enabling smarter decision-making. This combination of operational leverage and strategic foresight is positioned as critical to unlocking sustained performance improvements across the network lifecycle—from site planning to ongoing optimization. The broader implication for the telecom sector is that AI-enabled Open RAN could serve as a replicable blueprint for other operators aiming to modernize while sustaining profitability in an increasingly competitive market.
In summary, Rakuten Mobile’s AI-enabled Open RAN deployment embodies a multifaceted strategy: open, scalable network architecture; autonomous operations supported by machine learning and advanced analytics; and a clear focus on sustainability and monetization. The company contends that this integrated approach delivers tangible benefits in deployment speed, network efficiency, and energy management, all while providing a blueprint that other operators can adapt to their unique regulatory and market environments. As more operators explore AI-led modernization, Rakuten Mobile’s example offers both a practical roadmap and a strategic narrative about the future of telecom infrastructure in a data-rich, energy-conscious era.
Section 2: Modernising Site Management with the Site Manager Platform
A central pillar of Rakuten Mobile’s AI-driven strategy is the modernization of site management through a dedicated platform known as Site Manager. This platform harnesses machine learning to streamline deployment workflows, reduce delays, and improve visibility across the entire site development lifecycle. The goal is to transform what has traditionally been a lengthy, manually intensive process into a streamlined, data-driven sequence that supports autonomous network operations while maintaining rigorous quality and compliance standards.
Site Manager acts as a backbone for orchestrating the many moving parts involved in site development. From initial planning and nominal planning to the execution of site installation, the platform consolidates disparate activities into a cohesive, auditable workflow. The conventional processes in telco site development—often reliant on spreadsheets, email rounds, and scattered documentation—are replaced with an integrated system that harmonizes data, permits real-time tracking, and supports AI-driven decision making. This, in turn, reduces the time and effort required to complete site builds and upgrades, enabling faster adoption of 5G capabilities and future satellite-related enhancements.
Anshul Bhatt emphasizes that site management is not a peripheral capability but a fundamental building block for enabling autonomous network operations. He notes that the typical telco workflow involves extensive back-and-forth communications, varying data formats, and multiple stakeholders, all of which can lead to inconsistencies and delays. By integrating AI into the site development process, Rakuten Mobile aims to generate greater visibility, standardize processes, and automate routine tasks. This, in turn, minimizes the risk of human error and ensures that installations align with approved designs and regulatory requirements.
The Site Manager platform addresses a range of inefficiencies that have persisted in legacy site development workflows. Bhatt describes how the platform supports a comprehensive end-to-end process: network planning, nominal planning, workflow orchestration, and site installation. Each step benefits from AI-enabled guidance, process automation, and data-driven checks that help ensure accuracy and consistency. The overarching objective is to modernize and digitalize the entire site development lifecycle, reducing cycle times while improving quality and traceability. The result is a more predictable, scalable path to deploying and upgrading network infrastructure across multiple sites and regions.
Rakuten Mobile acknowledges that traditional approaches to site management have often relied on manual methods and siloed information. For many operators, back-office teams manage data in Excel sheets and emails, leading to fragmentation and duplication of effort. The Site Manager platform is designed to unify these activities under a single AI-powered umbrella, creating a source of truth for site-related decisions. This centralization enables more effective coordination among planning, construction, procurement, and compliance teams, while providing a foundation for continuous improvement through data analytics and AI-driven insights.
From a practical standpoint, the platform’s impact can be measured in several dimensions. First, deployment speed increases as AI-assisted workflows streamline planning and execution, reducing delays caused by miscommunication or missing information. Second, accuracy improves as automated checks validate designs against site conditions and regulatory constraints. Third, cost efficiency rises as AI identifies bottlenecks, optimizes resource allocation, and minimizes rework. Fourth, risk management improves due to enhanced traceability and auditability of all site-related activities. Fifth, scalability expands as the same platform can be extended to new sites, new regions, and future technologies, including satellites or additional spectrum bands. The cumulative effect is a more agile, reliable, and repeatable process for site development that supports broader network modernization efforts.
Rakuten Mobile’s approach to site management also opens doors for broader industry adoption. If the Site Manager demonstrates sustained benefits in terms of deployment speed, cost savings, and quality, other telcos may adopt a similar AI-enabled workflow to standardize site development across diverse environments. The potential for interoperability with other AI tools, data sources, and network management systems could accelerate the transition to autonomous networks across the telecom landscape. As telcos seek greater efficiency and resilience in their networks, a standardized, AI-augmented site management framework could serve as a critical enabler of scalable, sustainable growth.
In summary, Site Manager represents a strategic core of Rakuten Mobile’s AI-driven modernization. By digitalizing and automating site development workflows, the platform aims to shorten deployment cycles, reduce manual effort, and improve overall quality and visibility. The result is a more efficient path to building and upgrading network infrastructure, with a focus on accuracy, consistency, and future readiness for autonomous network operations. The broader industry implications point to a potential paradigm shift in how telcos manage site development, data, and execution through AI-enabled workflows that drive measurable value.
Section 3: Developing an AI Site Companion for Field Engineers
Beyond the back-office optimization of site development, Rakuten Mobile is advancing an “AI site companion” designed to assist field engineers during construction and installation activities. This initiative centers on deploying computer vision and large language models (LLMs) to augment the capabilities of engineers in the field, helping them perform tasks more efficiently, accurately, and safely. The concept positions AI as a practical partner—an assistant that supports decision making, expedites routine tasks, and reduces the cognitive and administrative load that engineers carry on a daily basis.
The core idea is to embed AI capabilities directly into the tools and devices used by field crews. One application area involves integrating AI into cameras used by engineers during construction and installation. By processing images on-device or at the edge, AI can provide real-time feedback and auditing, indicating whether construction work aligns with the approved design. This intelligent auditing capability helps ensure that site development adheres to specifications, reducing the risk of rework and non-compliance after inspection. The system’s on-device intelligence minimizes latency, preserves data privacy, and enables immediate corrective actions in the field without requiring round-trips to centralized servers.
In addition to image analysis, Rakuten Mobile’s AI site companion leverages the capabilities of large language models to interpret the vast array of documentation encountered during site development. Engineers frequently encounter documents such as permits, blueprints, commissioning checklists, and regulatory forms, each with its own structure and terminology. The LLM-powered chatbot serves as an on-site assistant, answering questions by drawing from the platform’s document corpus and the broader site data. This capability helps engineers quickly resolve doubts, obtain clarifications, and access relevant information without leaving the field or delaying critical operations.
A key efficiency metric cited by Rakuten Mobile is the potential time saved per site through AI-enabled document processing and automation. By reading, understanding, and autofilling forms, AI can eliminate substantial manual effort—an estimated saving of 30 to 40 minutes per site in some workflows. This time savings compounds across multiple sites, significantly accelerating deployment schedules and allowing engineers to reallocate time toward more value-added activities, such as verification, quality assurance, and complex problem solving. The AI site companion thus acts as a practical productivity multiplier, enabling field teams to accomplish more with the same resources and improving overall project throughput.
The companion’s design emphasizes accessibility and user experience. Engineers can pose questions to the on-site chatbot, which uses the underlying LLM and a domain-specific knowledge base to generate relevant, contextual responses. The objective is to provide reliable, accurate answers that support on-the-ground decision making while ensuring that responses are grounded in the site’s documentation and approved designs. The vision extends beyond a single chat interaction; it envisions an evolving agentic system in which numerous tasks—ranging from data collection and validation to procurement checks and compliance verifications—are automated or semi-automated through agent-like behavior.
From a technical perspective, the AI site companion integrates a multi-layered architecture. It combines perception (computer vision), reasoning (LLMs and domain models), and action (edge-driven automation and robotic process automation for field tasks). This architecture enables real-time feedback during construction, audit trails for compliance, and rapid adaptation to variations in site conditions or design changes. The AI’s ability to process visual data, interpret textual documents, and generate actionable insights makes it a versatile tool that can be applied across diverse site types—from urban macro sites to rural backhaul facilities and beyond.
The broader implications of the AI site companion framework are significant. First, it can reduce human error by providing immediate checks during critical tasks, such as verifying that installations conform to design specifications. Second, it can standardize field practices by delivering consistent guidance and documentation across different crews and sites. Third, it enhances knowledge transfer by capturing tacit field experience in an accessible, machine-readable form that others can leverage for training and continuous improvement. Fourth, the on-device AI capabilities reassure stakeholders concerned about data privacy and regulatory compliance by keeping sensitive information processing close to the source.
In sum, the AI site companion represents a practical realization of field-level intelligence, merging computer vision, LLMs, and edge computing to support field engineers in construction and installation activities. It embodies Rakuten Mobile’s broader objective of creating agentic, autonomous capabilities that extend beyond centralized data centers into the hands-on operational environment. By enabling real-time auditing, faster form processing, and intuitive on-site assistance, the AI site companion has the potential to transform field operations, reduce lead times, and deliver measurable efficiency gains across telco deployment programs.
Section 4: On-Device AI for Image Processing and Real-Time Site Auditing
A distinctive aspect of Rakuten Mobile’s AI strategy is the deployment of on-device AI to empower image processing and real-time site auditing during construction and deployment activities. This approach centers on embedding intelligence directly into field equipment—such as cameras and other sensing devices—so that critical decisions and quality checks can occur at the edge, without dependence on centralized cloud processing. On-device AI reduces latency, preserves data privacy, and enables rapid feedback that keeps projects on track.
During site development phases, AI-enabled cameras can perform intelligent audits as soon as images are captured. The system can compare captured images against approved design specifications, identify deviations, and prompt on-site personnel with corrective actions. This capability creates a continuous quality loop that accelerates the verification process and reduces the likelihood of costly rework at later stages. Real-time auditing also enhances accountability, providing an auditable trail of site conditions, actions taken, and outcomes achieved, which is essential for regulatory compliance and post-deployment maintenance.
The on-device AI approach also supports image processing tasks that would otherwise be time-consuming or error-prone if performed manually. For example, AI can automatically annotate images, categorize construction elements, detect misalignments, and flag discrepancies between as-built conditions and design documents. By processing data locally, the system minimizes the need to transmit large image datasets to remote servers, thereby reducing bandwidth usage and improving responsiveness in environments with limited connectivity. This is particularly relevant for rural or remote deployment sites, where network reliability may be constrained.
In addition to visual data, on-device AI can assist with other sensor streams and metadata associated with site construction. AI-enabled devices can monitor environmental conditions, equipment status, and energy usage, providing operators with timely alerts about anomalies or potential safety concerns. The collected data can then be integrated into the Site Manager or other orchestration platforms, enriching the decision-making process with a multidimensional view of site health and progress. The combination of real-time perception, immediate feedback, and secure, edge-based processing contributes to a more efficient, resilient deployment workflow.
Rakuten Mobile emphasizes the value of on-device AI in enabling autonomous site operations while maintaining human oversight where appropriate. The edge-based intelligence acts as a supervisory layer that accelerates routine checks and ensures adherence to design standards, yet human professionals remain engaged for interpretation, critical decision making in ambiguous situations, and complex problem solving. This balanced approach aligns with the broader objective of achieving autonomous network management without sacrificing safety, accountability, and governance.
From a strategic perspective, the on-device AI implementation supports scalability and resilience across the company’s network expansion plans. As operators scale deployments—whether across 5G expansions, upgrades to higher-frequency bands, or future satellites—the need for rapid, reliable, local decision-making becomes increasingly important. On-device AI reduces dependence on backhaul connectivity, lowers operational risk, and enhances the ability to deliver consistent results across diverse environments. In addition, by keeping sensitive data on-site, the approach can help address data privacy concerns and regulatory constraints that vary across jurisdictions.
In summary, on-device AI for image processing and real-time site auditing is a core capability in Rakuten Mobile’s AI-driven site development framework. By embedding intelligence directly into field devices, the company can deliver fast feedback, improve inspection accuracy, and reduce the time and cost associated with site audits. This edge-centric approach complements the broader Site Manager platform and AI site companion, creating a cohesive ecosystem that supports efficient, high-quality deployment and continuous improvement across telco sites.
Section 5: Document Management and AI-Driven Automation
Effective document management is a critical challenge in telecom site development, where a torrent of permits, drawings, contracts, and regulatory forms must be processed accurately and efficiently. Rakuten Mobile’s AI strategy addresses this pain point by leveraging large language models (LLMs) and related AI technologies to read, understand, and auto-fill complex documents. This capability reduces manual data entry, accelerates workflows, and helps ensure consistency across diverse documentation types and real estate arrangements.
The LLM-driven document understanding system works by ingesting a broad corpus of site-related documents and aligning them with the specific tasks at hand. Engineers and project managers can query the system in natural language to retrieve relevant clauses, verify compliance requirements, or extract key data points for further processing. The automation extends to auto-filling fields within forms and templates that are common in site development, permitting, and leasing agreements. The time savings reported by Rakuten Mobile, in the context of document processing, underscore the potential for substantial efficiency gains when AI reduces repetitive administrative tasks.
A notable advantage of this approach is the ability to handle the variability inherent in telecom documentation. Different leasing companies, real estate projects, and regulatory regimes yield disparate forms and processes. An intelligent system that can map these documents to standardized templates and autofill them accurately helps to reduce errors and rework. The resulting consistency can improve the speed and reliability of site approvals, lease negotiations, and other critical steps in the deployment process. The AI-driven document management capability thus serves as a central lever for accelerating site development while maintaining governance and compliance standards.
The AI-based document handling also supports knowledge capture and organizational learning. As the system processes a wide range of documents, it can identify common patterns, extract best practices, and surface insights that can inform future site development projects. This creates a feedback loop that enhances both the technology and the process over time. The ability to store, organize, and retrieve document-related information in a structured, machine-readable format fosters a more proactive and data-driven culture within the organization.
From an operational standpoint, the integration of AI into document management complements the Site Manager platform, the AI site companion, and the on-device AI capabilities. Together, these technologies create an end-to-end AI-enabled workflow that spans planning, execution, auditing, and compliance. The convergence of perception, reasoning, and automation across documents and site workflows reinforces Rakuten Mobile’s broader objective of achieving autonomous network operations with robust governance and traceability.
In summary, AI-driven document management represents a powerful enabler of faster, more accurate, and more scalable site development. By reading and understanding documents, autofilling forms, and enabling natural-language queries to extract actionable information, Rakuten Mobile is reducing administrative overhead and shortening the deployment cycle. The integration of document intelligence with field operations and site management reinforces the company’s vision of a unified, AI-enabled telco ecosystem that can scale across markets and technologies.
Section 6: Green Slicing and Sustainable Network Solutions
Rakuten Mobile’s AI strategy extends into network sustainability through innovative approaches to network slicing, including the concept of green slicing. Green slicing refers to energy-efficient network slices designed to meet enterprise or consumer requirements while minimizing power consumption and emissions. The initiative reflects a growing industry emphasis on balancing performance with environmental stewardship as telcos scale their networks and deploy higher-density technologies.
Anshul Bhatt explains that many operators are seeking to avoid trade-offs between cost, performance, and sustainability. The green slicing concept addresses this by creating energy-aware slices that optimize the allocation of resources—such as computational power, radio spectrum, and network routing paths—for tasks that demand different levels of performance. This approach aligns with corporate sustainability goals and regulatory expectations while enabling operators to deliver cost-effective, energy-conscious services.
The sustainable dimension of Rakuten Mobile’s AI-enabled strategy encompasses more than energy savings alone. It also involves optimizing the autonomy and efficiency of network operations to reduce waste and improve resilience. By integrating AI into the management of network slices, operators can dynamically adapt to changing load conditions, traffic patterns, and environmental considerations, thereby lowering the total cost of ownership and extending the life of existing assets. In practice, green slicing could enable telcos to offer differentiated services with clear sustainability metrics and improved energy performance credentials, which could appeal to environmentally conscious customers and enterprise buyers.
Operators have expressed interest in both the economic and environmental benefits of green slicing. The ability to lower energy consumption while maintaining or improving service quality resonates with many telcos’ long-term strategic goals. At the same time, the autonomous network journey—supported by AI—offers opportunities to optimize the placement and operation of network functions, further enhancing energy efficiency. Rakuten Mobile positions these capabilities not only as technical accomplishments but as strategic differentiators that can drive customer value, reduce operating costs, and advance sustainability targets across the telecom ecosystem.
The green slicing narrative aligns with ongoing trends in AI for telecoms, including energy-aware orchestration, carbon accounting for network operations, and the pursuit of greener artificial intelligence. Telcos are under increasing scrutiny to demonstrate responsible energy use and efficient resource management as data traffic grows and network infrastructure expands. By embedding green considerations into its AI-driven network design, Rakuten Mobile aims to demonstrate a practical, scalable approach that other operators can replicate to achieve both performance gains and environmental stewardship.
In essence, green slicing represents a pivotal dimension of Rakuten Mobile’s sustainability-focused strategy. It showcases how AI-enabled network orchestration can deliver smarter, more energy-efficient infrastructure while supporting business objectives and environmental commitments. The concept embodies a forward-looking vision of telecom networks that are not only fast and reliable but also responsible stewards of energy resources and natural impact, aligning technical innovation with broader societal goals.
Section 7: Monetization and Industry Impact
A recurring theme in Rakuten Mobile’s MWC25 discussions is monetization—the idea that AI-driven improvements can unlock new revenue streams and enhance profitability for operators. The company highlights opportunities to monetize efficiency gains, faster time-to-market, and higher-quality network performance that translates into improved customer satisfaction and retention. The monetization narrative is linked to the evolving business models surrounding Open RAN, autonomous networks, and AI-enabled services, suggesting a path for telcos to capture value from modernization initiatives beyond traditional capex reductions.
Rakuten Mobile’s approach implies several practical monetization avenues. First, faster rollouts and reduced deployment timelines can shorten time-to-revenue for new services and network capabilities, enabling operators to capture market opportunities more quickly. Second, improved network efficiency and energy savings can translate into lower operating expenses and lower total cost of ownership for mobile networks, enhancing profitability and potentially enabling料金-based incentives or green pricing models for enterprise customers seeking sustainable solutions. Third, AI-enabled analytics and automation can unlock new service offerings, such as AI-assisted network optimization as a managed service, enabling operators to monetize advanced analytics capabilities and automated optimization for other operators or enterprise clients.
The broader industry implications of Rakuten Mobile’s monetization focus are substantial. If AI-enabled site management, autonomous network operations, and green slicing demonstrate consistent ROI, other operators may adopt similar architectures and business cases. This could accelerate the adoption of Open RAN as a platform for AI-driven optimization, interoperability, and innovative service models. The cross-operator interest in Rakuten Mobile’s journey—especially among operators evaluating their AI capabilities—suggests a potential ripple effect in which a successful, scalable model informs procurement strategies, network design choices, and technology roadmaps across markets.
From a strategic perspective, monetization is intimately tied to performance metrics such as deployment cycle time, energy efficiency, and the quality of service. A robust business case for AI-driven automation requires rigorous measurement frameworks that capture improvements in lead times, cost per site, energy consumed per unit of traffic, and customer experience indicators. Rakuten Mobile’s emphasis on profitability and credible market traction signals to industry stakeholders that AI-enabled Open RAN deployments can be attractive not only for technical reasons but also for financial performance and competitive differentiation.
In summary, monetization sits at the intersection of technology, operations, and business strategy in Rakuten Mobile’s AI-driven modernization. The company’s narrative suggests that AI-enabled efficiency, rapid rollouts, and sustainable operations can translate into new revenue opportunities and improved profitability for telcos. As operators weigh these possibilities, Rakuten Mobile’s real-world experience offers a concrete reference point for how AI-driven automation and Open RAN can be integrated into a compelling business case that aligns technology with financial and strategic objectives.
Section 8: Future Roadmap—Autonomous Networks, Satellite Prospects, and Agentic Architecture
Looking ahead, Rakuten Mobile envisions a future in which autonomous networks become the mainstream operating paradigm for telecom infrastructure. The roadmap includes continued expansion of Open RAN capabilities, deeper AI integration across planning, deployment, and operations, and broader adoption of agentic architectures—systems that execute tasks autonomously while remaining aligned with human oversight and governance frameworks. The goal is to extend AI-driven autonomy to more components of the network, enabling operators to manage complex, dynamic environments with fewer manual interventions and more data-driven confidence.
One facet of the roadmap involves expanding the types of services and technologies integrated into autonomous networks. Beyond terrestrial 5G and Open RAN, Rakuten Mobile signals an interest in satellite-enabled connectivity, acknowledging the potential for future capabilities to complement ground-based networks. This expansion would require careful orchestration across disparate networks, spectrum, and regulatory regimes, but it also presents opportunities to extend coverage, improve resilience, and support new business models. The company’s perspective is that the autonomous network journey will be iterative, with incremental enhancements that cumulatively yield substantial gains in performance, efficiency, and sustainability.
Agentic architecture is another central theme in the envisioned future. This approach emphasizes architecture that empowers AI agents to autonomously perform a range of tasks—from provisioning and optimization to audits and compliance checks—while maintaining clear lines of responsibility and governance. Anshul Bhatt highlights the importance of designing architectures that facilitate automation while ensuring transparency, security, and human oversight where needed. The agentic model aims to reduce manual toil, accelerate decision-making, and deliver consistent outcomes in a scalable manner.
The satellite dimension, while not yet a primary focus of the current rollout, is acknowledged as a possible frontier for AI-enabled telco infrastructure. The integration of satellite backhaul or user-plane capabilities with AI-driven orchestration could enhance coverage and resilience, particularly in hard-to-reach areas or during network disruption events. Adopting such capabilities would necessitate cross-domain interoperability, robust security, and careful optimization to ensure that satellite links contribute positively to performance and energy efficiency. Rakuten Mobile’s forward-looking stance suggests readiness to explore these possibilities as part of a long-term strategy to create a highly resilient, globally scalable AI-enabled network.
In practice, the roadmap emphasizes a phased approach to AI deployment and autonomy. Early stages focus on improving site development workflows, on-device AI capabilities, and policy-driven automation. Intermediate stages extend AI to more network functions, including dynamic resource allocation, predictive maintenance, and self-healing mechanisms. Later stages could see broader deployment of autonomous decision-making across network planning, optimization, and service assurance, with agentic systems that coordinate across multiple network domains and stakeholders. Throughout, the emphasis remains on maintaining human oversight, governance, and accountability, ensuring that autonomous capabilities augment human expertise rather than supplant it.
The anticipated outcomes of this future path include faster deployments, more efficient networks, lower energy consumption per unit of traffic, and enhanced service reliability. Telcos that adopt similar AI-enabled autonomous architectures could realize bottom-line improvements while presenting customers with higher quality experiences and stronger sustainability credentials. Rakuten Mobile’s roadmap signals a comprehensive and ambitious plan to advance telecom infrastructure toward full autonomy—an evolution that could redefine how operators design, operate, and monetize network ecosystems in the decades ahead.
Section 9: Industry Implications, Challenges, and Opportunities
Rakuten Mobile’s AI-powered Open RAN strategy holds meaningful implications for the telecom industry, including both opportunities and challenges that operators must navigate as they consider wide-scale adoption. The potential benefits are clear: faster deployment cycles, improved network efficiency, enhanced field operations, and stronger alignment with sustainability goals. If the model proves scalable and economically viable, more operators may follow with analogous AI-enabled modernization programs, creating a broader ecosystem of interoperable tools, platforms, and services designed to support autonomous networks.
However, several challenges require careful attention. Data privacy and security are paramount when AI systems process sensitive information across planning, construction, and operations. Ensuring robust access controls, secure data flows, and auditable processes will be essential to maintaining trust and regulatory compliance. Interoperability across vendors, platforms, and network technologies is another critical factor. The AI components must work seamlessly across diverse hardware and software environments, and common data standards will help to prevent fragmentation and enable scalable deployments.
Cost considerations are also central to adoption decisions. While AI-driven automation promises substantial long-term savings, the upfront investment in AI platforms, edge devices, sensors, and integration efforts can be significant. Telcos will need compelling business cases, detailed ROI analyses, and clear execution plans to justify these expenditures. Ongoing operational costs related to model maintenance, monitoring, and updates must also be taken into account to ensure sustained value over the life of the deployments.
From a strategic perspective, telcos should consider governance and risk management as essential components of their AI initiatives. As autonomous capabilities expand, organizations must establish robust governance frameworks that define accountability, ethical guidelines, risk mitigation, and compliance with industry regulations. Operational resilience becomes a priority as AI-driven automation increases exposure to potential system failures or data quality issues. Proactively addressing these concerns can help ensure smoother adoption and long-term success.
The global industry implications of Rakuten Mobile’s journey also relate to workforce implications. AI-enabled automation can shift the skill requirements for telecom professionals, emphasizing data literacy, AI governance, and the ability to manage complex automated processes. Training and change management will be critical to ensuring that teams can effectively design, deploy, and operate AI-enabled networks while maintaining human expertise and oversight.
In conclusion, Rakuten Mobile’s AI-powered Open RAN and site-management initiatives offer valuable lessons for the telecom industry. The potential benefits in efficiency, reliability, and sustainability are compelling, but realizing them requires thoughtful consideration of data governance, interoperability, cost, and people. Operators exploring AI-driven modernization should assess how similar architectures could be adapted to their regulatory contexts, market dynamics, and technical ecosystems. With careful planning, transparent governance, and a focus on measurable ROI, AI-enabled autonomous networks can become a practical, scalable path toward a more resilient and efficient telecom future.
Conclusion
Rakuten Mobile’s ambitious AI-centric strategy for Open RAN, autonomous networks, and sustainable operations represents a bold blueprint for the telecom industry. By integrating AI across site management, field operations, and network orchestration, the company aims to accelerate deployments, improve operational efficiency, and advance environmental stewardship. The Site Manager platform, AI site companion, on-device AI capabilities, and green slicing concept form a cohesive framework that addresses core deployment challenges while unlocking new opportunities for monetization and service innovation. The broader industry implications suggest that Rakuten Mobile’s approach could serve as a replicable model for telcos seeking to modernize their networks through AI-enabled automation and sustainable design.
As operators around the world evaluate how best to transform their networks, Rakuten Mobile’s experiences offer a detailed case study in implementing practical AI solutions that deliver tangible results. The emphasis on profitability, measurable efficiency gains, and scalable architectures provides a compelling narrative for adopting AI-driven Open RAN and autonomous network strategies in diverse markets. While challenges remain—ranging from data governance to interoperability and cost—careful planning, governance, and phased implementation can help telcos realize the benefits of AI-enabled modernization. The road ahead envisions networks that are not only faster and more capable but also more sustainable, with AI guiding intelligent decisions that optimize performance, cost, and environmental impact across the telecommunications landscape.