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Enterprises do not need another collection of individual AI tools. They need a unified platform that connects data, AI development, governance, deployment and operations.
Artificial intelligence is driving a fundamental shift in compute infrastructure, forcing organizations to rethink how they deploy and manage AI workloads. To start with, MNOs are becoming AI infrastructure providers, combining network, compute and data resources to support new AI services.
AI inference is moving closer to the edge to power real-time applications such as drones, robotics, smart cities and other real-time workloads.
At the same time, governments and enterprises are increasingly demanding data sovereignty, creating demand for localized AI infrastructure that keeps sensitive data within defined geographic or organizational boundaries.
One enterprise AI platform for multiple services
These changes are creating a need for an AI platform that provides a common foundation for building and operating AI across different environments. AI Workbench Powered by Rakuten AI meets these needs by connecting data, AI development, governance, deployment and operations, enabling data scientists and engineers to build, govern, deploy and scale AI pipelines without assembling separate tools for each project.
AI Workbench is a cloud-agnostic, enterprise-grade platform from Rakuten Symphony that brings together three core capabilities: the AI Platform, Customer Data Platform (CDP) and Customer Intelligence Engine (CIE). Together, they streamline data discovery, model training, deployment, insights and AI-assisted and autonomous operations.
Creating a common AI platform
Rather than creating separate infrastructure and data environments for individual AI projects, organizations can use AI Workbench as a common foundation for their data and AI initiatives. The AI Platform supports scalable AI pipelines and MLOps lifecycle capabilities; the CDP supports governed data discovery and preparation; and the CIE delivers AI-driven insights for AI-assisted and autonomous operations.
This approach addresses an increasingly important enterprise AI challenge: turning large volumes of distributed operational data into information that AI models and applications can use. AI Workbench can ingest information from sources such as network, subscriber and billing systems into a data foundation where it can be retained, aggregated and made available for analytics and AI workloads. It can do this without a large storage requirement.
From there, organizations can create AI pipelines that extend from data ingestion and transformation through model development, deployment and serving. The result is an enterprise AI platform that supports the AI lifecycle rather than requiring organizations to assemble and manage separate tools for each project.
Three core capabilities for enterprise AI
AI Workbench brings together three core capabilities that connect the enterprise AI lifecycle, from governed data and AI development to insights and operations.
- AI Platform. The AI Platform provides scalable AI pipelines and lifecycle capabilities that help data scientists and engineers build, deploy and manage AI applications using governed enterprise data. This creates a repeatable framework for AI development rather than requiring a new infrastructure stack for every use case.
- Customer Data Platform. Underpinning these AI workloads is the CDP. Enterprise data is frequently fragmented across databases, applications, operational systems and logs. The CDP provides a governed data foundation that brings these sources together and prepares information for analytics and AI.
- Customer Intelligence Engine. Building on this data and AI foundation, the CIE delivers AI-driven insights for applications including root-cause analysis, anomaly detection, log intelligence and demand forecasting, as well as AI-assisted and autonomous operations. In an autonomous operations scenario, for example, the system can detect an issue, determine the appropriate response and remediate it automatically.
Deploy AI where the data is located
Deployment flexibility is particularly important as AI becomes more distributed. Organizations may want public cloud resources for some applications while keeping customer, network, financial or other sensitive data within their own infrastructure for others.
AI Workbench supports on-premises and sovereign deployment, including air-gapped operation inside an organization's DMZ with zero data egress and deployment in controlled data environments. Rakuten AI is also optimized for distributed CPU inference, reducing dependence on GPU infrastructure for supported workloads. The platform uses specialized models for areas such as customer intelligence, network operations, root-cause analysis, churn, fraud and compliance.
Organizations can also use public cloud infrastructure and models for applications where sovereignty is less important or elastic computing is beneficial. This provides a way to match AI infrastructure to workload requirements rather than forcing every application into the same deployment model.
Taking enterprise AI into the real world
These capabilities extend AI Workbench beyond MNOs. The same requirements for governed data, scalable AI pipelines, local inference and controlled deployment exist across industries, including the public sector, healthcare, banking, manufacturing, mines and ports, and retail.
A manufacturer, for example, could use AI operations for anomaly detection, demand forecasting or image recognition. A retailer could combine customer and location information to improve personalization, inventory and store efficiency. Banks and public-sector organizations can benefit from architectures designed to address data residency, privacy and compliance requirements.
For MNOs, the opportunities are particularly broad because they sit on enormous volumes of network and customer data. AI Workbench supports applications ranging from Customer 360 profiles and revenue assurance to fraud detection, personalization, network anomaly detection and AI-assisted and autonomous operations.
Rakuten Mobile is already applying this model to large-scale operational environments. The platform has been validated on more than 20 petabytes of telco data and supports more than 70 use cases. Rakuten has also used AI models to achieve reported improvements in spectral efficiency and energy savings within its ecosystem.
One foundation for the next phase of AI
As AI moves toward enterprise-wide deployment, organizations need more than access to models. They need a foundation that connects data, AI development, governance, deployment and operations.
AI Workbench provides that foundation. By bringing together the AI Platform, CDP and CIE with flexible deployment options, organizations can build, deploy and operate multiple AI workloads across cloud, on-premises and sovereign environments.
For MNOs becoming AI infrastructure providers, enterprises moving inference to the edge and organizations facing stricter sovereignty requirements, a common enterprise AI platform can turn AI from a collection of individual projects into a multi-pipeline infrastructure for ongoing innovation.
Ready to transform your enterprise AI strategy? Connect with our team to see how AI Workbench can be tailored to your specific organizational needs.
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