The AI-native telco is defined by McKinsey & Co as an organization where “AI is viewed as a core competency that powers decision making across all departments and organization layers.” In a survey of C-suite leaders, the consulting firm found that only about 50 percent are “currently capturing impact from AI/gen AI.”
That means there’s a lot of room for operators to embrace AI and to use it more effectively. But that doesn’t just happen overnight. There is an AI maturity model that describes the changes required for an organization to transition from cloud-native to AI-native.
Rakuten Cloud has the experience and solutions to facilitate this transition, which were the focus of a presentation by Vivek Chadha, SVP and Global Head of Telco Cloud, at the AI-Native Telco Forum, hosted by Telecom TV.
Chadha’s presentation covered the cloud-native to AI-native maturity model, how Rakuten Cloud has helped operators with that transition, including a real-world use case. This post highlights the key points of his talk, but you can watch the entire video on the Telecom TV website.
Chadha said the main impact of AI adoption is in decision making, what decisions are made by people versus by AI, how they are made, and soon, how we will control these decisions.
Ultimately, AI can make an increasing number of network deployment and management decisions in an automated, scaled and better way. However, before this decision-making can change, the operator must change and adopt AI.
A significant part of Chadha’s presentation was a discussion of the Indicative Maturity Model, which identifies the steps needed to transition from a cloud-native focus to an AI-native focus. He identified four phases that most operators typically pass through, ranging from cloud-native to AI-assisted, to AI-enabled, and finally to AI-driven, culminating in the desired end point of AI-native.
Chadha discussed all of the impacts that go with each of these stages, including the architectural reality of each stage; who owns decision-making, AI or human; what is the business model shift and objective; and what is the threat to the business if it is stuck at any of the phases.
As operators progress through these stages, intelligence should be embedded throughout the business. That was what happened with Rakuten Mobile and also with Orange Ivory Coast.
Chadha described how Rakuten Cloud developed the Rakuten Data and AI Cloud Platform to help its customers make the transition to AI.
This platform was built on the company’s Kubernetes-based private cloud, featuring an orchestrator and the fastest storage driver available on the market. Running in that private cloud is the Rakuten Data and AI Framework, an AI and ML pipeline featuring a tested open-source software stack that meets the needs of developers, data scientists, and data engineers.
The result of this integration is a complete pre-integrated AI/ML solution that can be installed and be up and running in a very short time period. Chadha said that the Rakuten Data and AI Cloud Platform reduced setup time by 50% and data acquisition time by 70%.
One impressed customer of this solution is Orange Ivory Coast, which uses the Rakuten Data and AI Cloud Platform to support its “data democracy” program. The operator achieved major improvements in provisioning time, automation and process accuracy while maintaining user performance equivalent to bare-metal systems. The solution has been running for more than two years, and Chadha stated that the customer has experienced a significant reduction in provisioning time, benefiting from the automation of manual operations.
Chadha made a strong case that the transition from cloud-native to AI-native is not a leap but a series of managed, data-driven steps. These steps combine building an open cloud infrastructure with AI models trained on real-world data, combined with automation and experimentation. Operators who modernize quickly, embrace openness and treat data as fuel will be the ones to unlock true AI-native value.
Watch the entire presentation at Telecom TV.
.svg)
