Source URL: https://cloud.google.com/blog/topics/telecommunications/the-ai-driven-telecom-a-new-era-of-network-transformation/
Source: Cloud Blog
Title: The AI-driven telecom: A new era of network transformation
Feedly Summary: The telecommunications industry is undergoing a profound transformation, with AI and generative AI emerging as key catalysts. Communication service providers (CSPs) are increasingly recognizing that these technologies are not merely incremental improvements but fundamental drivers for achieving strategic business and operational objectives. This includes enabling digital transformation, fostering service innovation, optimizing monetization strategies, and enhancing customer retention.
To provide a comprehensive and data-driven analysis of this evolving landscape, Google Cloud partnered with Analysys Mason to conduct an in-depth study “ Gen AI in the network: CSP progress in adopting gen AI for network operations. This research examines CSPs’ progress, priorities, challenges, and best practices in leveraging gen AI to reshape their networks, offering quantifiable insights into this critical transformation.
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Key findings: A data-driven roadmap
The Analysys Mason study offers valuable insights into the current state of gen AI adoption in telecom, providing a data-driven roadmap for CSPs seeking to navigate this transformative journey:
1. Widespread gen AI adoption and future intentions
Demonstrating the strong momentum behind gen AI, 82% of CSPs surveyed are currently trialing or using it in at least one network operations area, and this adoption is set to expand further, with an additional 9% planning to implement it within the next 2 years.
2. Strategic importance of gen AI
Gen AI empowers CSPs to achieve strategic goals within the network: 57% surveyed see it as a key enabler of autonomous, cloud-based network transformation initiatives, 52% for the transition to new business models like NetCo/ServCo and more digitally driven organizations, and all with the aim of enhancing customer experience and driving broader transformation.
3. Key drivers for gen AI investment
CSPs are strategically prioritizing gen AI investments to achieve a range of network objectives, including optimizing network performance and reliability, enhancing application quality of experience (QoE), and improving network resource utilization, recognizing gen AI’s potential to move beyond a productivity tool and become a cornerstone of future network operations and automation..
4. Challenges in achieving model accuracy
While gen AI offers significant potential, the study found that 80% of CSPs face challenges in achieving the expected accuracy from gen AI models, a hurdle that impacts use case scaling and ROI. These accuracy issues are linked to data-related problems, which many CSPs across different maturity levels are still working to resolve, and the complexity of customizing models for specific network operations.
5. Addressing the skills gap
With over 50% of CSPs citing it as a key concern, employee skillsets represent a major challenge, highlighting the urgent imperative for CSPs to invest in upskilling and reskilling initiatives to cultivate in-house expertise in AI, gen AI, and data science related fields.
6. Gen AI implementation strategies
While many CSPs begin their gen AI implementation by utilizing vendor-provided applications with embedded gen AI capabilities (the most common approach), the study emphasizes that to fully address their diverse network needs, CSPs also seek to customize models using techniques like fine-tuning and prompt engineering; this customization, however, is heavily reliant on a strong data strategy to overcome challenges such as data silos and data quality issues, which significantly impact the accuracy and effectiveness of the resulting gen AI solutions.
7. Deployment preferences
While 51% of CSPs indicated hybrid cloud environments as the predominant deployment choice for gen AI platforms in network operations, reflecting the need for flexibility and control, a significant 39% of CSPs show a strong preference for private cloud-only deployments specifically for their data platforms, driven by the critical importance of data security and control. Public cloud deployments are preferred for AI model deployments.
Recommendations for CSPs
In summary, to secure a competitive edge, CSPs will need to prioritize gen AI use cases with clear ROI by adopting early-win gen AI use cases while developing a long-term strategy, transform their organizational structure and invest in upskilling initiatives, develop and implement a robust data strategy to support all AI initiatives and cultivate strong partnerships with expert vendors to accelerate their gen AI journey.
Google Cloud: Your partner for network transformation
Google Cloud empowers CSPs’ data-driven transformation by providing expertise in operating planetary-scale networks, a unified data platform, AI model optimization, professional services for gen AI, hybrid cloud solutions, and a rich partner ecosystem. This is further strengthened by Google Cloud’s proven success in driving network transformation for major telcos, leveraging infrastructure, platforms, and tools that deliver the required near real-time processing and scale.
To kickstart your AI-powered journey for network transformation visit Google Cloud for Telecommunications.
AI Summary and Description: Yes
Summary: The telecommunications sector is rapidly evolving due to the integration of AI and generative AI. This transformation serves as a key driver for operational improvements and strategic objectives among Communication Service Providers (CSPs). A study conducted by Google Cloud and Analysys Mason evaluates CSPs’ advancements and challenges in adopting generative AI for network operations.
Detailed Description: The collaboration between Google Cloud and Analysys Mason has resulted in an insightful analysis of generative AI adoption in the telecommunications industry, highlighting both the opportunities and challenges faced by CSPs:
– **Significant Adoption**:
– 82% of CSPs are currently using or trialing generative AI in at least one area of network operations.
– An additional 9% plan to implement it within the next two years, indicating a strong momentum for generative AI.
– **Strategic Goals**:
– CSPs view generative AI as a vital tool for achieving various strategic objectives:
– 57% believe it is essential for transitioning to autonomous, cloud-based network initiatives.
– 52% see it as critical for adopting innovative business models.
– The overarching aim is to improve customer experience and drive broader business transformation.
– **Investment Drivers**:
– Generative AI is prioritized for enhancing network performance, reliability, application quality, and resource utilization, positioned as a key element for future operational automation.
– **Model Accuracy Challenges**:
– Despite the benefits, 80% of CSPs struggle with model accuracy, which hampers the scale and return on investment (ROI) of generative AI implementations. This issue is often related to data quality and the customization needed for specific operations.
– **Skills Gap**:
– Over 50% of CSPs identify workforce skills as a major hurdle, emphasizing the need for investment in upskilling and reskilling initiatives in AI, generative AI, and data science.
– **Implementation Strategies**:
– Many CSPs utilize vendor applications with embedded generative AI features but also seek to customize models to cater to their diverse network needs, underscoring the importance of solid data strategies to mitigate data issues.
– **Deployment Preferences**:
– A majority (51%) prefer hybrid cloud environments for deploying generative AI platforms, while 39% favor private clouds for data platforms to maintain security and control.
**Recommendations for CSPs**:
– Focus on generative AI use cases that promise clear ROI and begin with early successes.
– Transform organizational structures and invest in workforce training.
– Develop a robust data strategy that supports AI initiatives and foster partnerships with expert vendors to enhance the generative AI transition.
**Google Cloud’s Role**:
– Google Cloud supports CSPs’ transformations by optimizing AI models, providing a unified data platform, and leveraging infrastructure for real-time processing capabilities, which enhance the effectiveness of their network transformation efforts.