Artificial Intelligence Insight Series - Artificial Intelligence in Telecom

Growing complexities in the communication networks today calls for an intelligent approach to network planning and optimization. With the rise of Artificial Intelligence (AI) techniques, new technology paradigms such as network virtualization, self-organizing networks (SONs), intelligent antennas, AI-powered radio-frequency (RF) front end and intelligent chipsets can be easily embedded into the communication networks.
Telecom companies are therefore leveraging AI solutions to achieve hyper-automation of telecom networks and usher in an era of self-healing and self-configuring networks. Inclusion of network intelligence allows mobile network operators (MNOs) to achieve efficient network management and cross spectrum protection.
This report includes a comprehensive analysis of the adoption of AI in telecom, highlighting the major technology trends and opportunities available across the ecosystem.

Competitive Analysis
This section includes a study of key telecom companies and other emerging entities in the AI in telecom space. We assess the major telecom entities based on their partnerships, implementation strategies, and recent AI solutions and services. The analysis also highlights the transformation of current telecom business models with the integration of AI/ML techniques for driving new revenue streams and maximizing benefits. Some of the key entities included in the report are AT&T, Verizon, Nokia, Huawei, Ciena, Rakuten, and Orange.
Further, the report includes approximately 40 startups that are concentrating on various technological aspects like SON, cloud-native, network analytics, AI hardware, and interference cancellation. Most of these emerging players are solving these telecom industry challenges by leveraging innovative AI solutions and services. We have analyzed them closely to get a clear picture of their product and technology offerings, partnerships, customers, funding details, and future outlook. Some of the startups reported include Cellwize, Altiostar, CujoAI, Kumu Networks, GenXcomm, Cambricon, Pivotal Commware, Metawave, Senseon, Parallel Wireless, Galgus, and Affirmed Networks.

M&A Analysis
An assessment of the acquisition trends since 2014 provides insights about the technology drivers in the AI-driven telecom market. The key technologies include cloud-native solutions, security, virtualized RAN, SON, and network analytics.
Distribution of these deals over the years provides an overview of the roadmap that the acquirers are following and highlights the unique value proposition behind the mergers and acquisitions.
The prominent participants in the space include telecom solution providers, MNOs, startups and investors. Some of the major acquirers include Nokia, Ericsson, Cisco and Zephyrtel.

Key Insights:
• Network intelligence-related solutions are becoming the new norm for deriving insights from massive network data.
• The growing need for hyper-automation in communication networks is setting the stage for SON technologies that will help drive autonomous networks of the future.
• Virtualization of networks is fueling the need for cloud-native solutions, ranging from the ones that provide security, to the one’s constituting the network core.
• The telecom solution providers, including names like Ericsson, Nokia, and Cisco, led the charts with the highest number of AI-based acquisitions.
• Emerging companies took a lead role in solving the problem of RF spectrum interference.

Key questions addressed in the report:
• How innovative AI solutions are making an impact in the telecom value chain?
• What are the challenges and issues addressed by the implementation of AI algorithms in the telecom industry?
• What are the emerging use cases and business models that are driving the adoption of AI?
• Who are the key enablers for AI technology in the industry?
• Who are the frontrunners, and what are their implementations strategies?
• What is the current industry adoption status of AI in telecom?
• Which are the disruptive entities that can be potential targets for driving AI in telecom?
• Since 2014, what are the acquisition trends for AI in telecom?
• What are opportunities for the telecom companies to build a strong roadmap for the future of autonomous networks?
• How can companies from other sectors tap into the AI in telecom market?


1. Impact of AI in the Telecom Ecosystem
1.1. Overview of the Telecom Value Chain Participants
1.2. Role of AI in the Telecom Ecosystem
2. Use Cases of AI in the Telecom Industry
2.1. Use Case 1: AI in Network Optimization
2.2. Use Case 2: AI for 5G Networks
2.3. Use Case 3: AI in Cloud
2.4. Use Case 4: AI Security
2.5. Use Case 5: Other AI Implementations
3. How is AI Transforming Telecom Business Models?
3.1. Business Model 1: AI Managed Services
3.2. Business Model 2: AI Powered Chipsets
3.3. Business Model 3: Data Monetization
3.4. Business Model 4: Open Source Platforms
3.5. Business Model 5: AI-Enabled Cloud Services
4. Overview of the AI Adoption by Key Telecom Companies
4.1. Adoption Status of Telecom Companies
5. How is AI Driving M&A Activity in Telecom Sector?
5.1. Technology Drivers of the Deals
5.2. Technology Distribution
5.3. Overview of the Acquired Technologies
5.4. Prominent Acquirers in the Space
5.5. Post-deal Integration Examples
5.6. Netscribes Analysis and Insights
5.7. Overview of the Deals
6. How are Startups Innovating in the AI Telecom Space?
6.1. Key Challenges Targeted by Startups
6.2. Startups Focusing on:
6.2.1. SON
6.2.2. Cloud Native Networks
6.2.3. Network Security and Analytics
6.2.4. Interference Cancellation
6.2.5. AI Hardware
6.2.6. Conversational AI
7. Insights & Recommendations
7.1. Current Adoption Status of AI in Telecom
7.2. Recommendations for Telecom Companies
7.3. Recommendations for Other Industry Players


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