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AI in Telecom Operations: Opportunities & Obstacles Independent market research and competitive analysis of next-generation business and technology solutions for service providers and vendors AI in Telecom Operations: Opportunities & Obstacles AUTHOR: JAMES CRAWSHAW, SENIOR ANALYST, HEAVY READING TABLE OF CONTENTS 1. EXECUTIVE SUMMARY .................................................................................. 4 1.1 Key Findings ............................................................................................. 4 1.2 Companies Covered ................................................................................... 5 2. INTRODUCTION ............................................................................................ 6 2.1 Machine Learning Categories....................................................................... 6 2.2 Why the Resurgence of Interest in AI/ML? .................................................... 8 3. POTENTIAL AI/ML USE CASES IN TELECOM ................................................. 9 3.1 Network Operations Monitoring & Management .............................................. 9 3.2 Predictive Maintenance ............................................................................ 10 3.3 Fraud Mitigation ...................................................................................... 11 3.4 Cybersecurity ......................................................................................... 11 3.5 Customer Service & Marketing Virtual Digital Assistants ................................ 11 3.6 Intelligent CRM Systems .......................................................................... 12 3.7 CEM ...................................................................................................... 12 4. CSP ADOPTION OF AI ................................................................................. 13 4.1 TCS Study Suggests Most CSPs Already Using AI/ML in IT/Networking .............. 13 4.2 TM Forum Study Suggests More Cautious Adoption of AI/ML .......................... 13 4.3 Customer Experience the Key Driver for AI/ML ............................................ 14 4.4 AI/ML in Network Management ................................................................. 15 5. REAL-WORLD CSP EXAMPLES ..................................................................... 16 5.1 AT&T ..................................................................................................... 16 5.2 COLT ..................................................................................................... 17 5.3 Deutsche Telekom ................................................................................... 17 5.4 Globe Telecom ........................................................................................ 18 5.5 KDDI ..................................................................................................... 18 5.6 KT......................................................................................................... 19 5.7 SK Telecom ............................................................................................ 19 5.8 Swisscom ............................................................................................... 20 5.9 Telefónica .............................................................................................. 20 5.10 Vodafone ............................................................................................... 22 6. CHALLENGES OF APPLYING AI/ML TO NETWORKING ................................. 23 6.1 Data That Is Dirty, Unavailable or Difficult to Access .................................... 24 6.2 Lack of Data Science Talent ...................................................................... 24 6.3 Lack of a Clear Question to Answer ............................................................ 25 6.4 Limitations of Tools ................................................................................. 26 © HEAVY READING | SEPTEMBER 2018 | AI IN TELECOM OPERATIONS 2 7. ACADEMIC, SDO, CONSORTIA & OS INITIATIVES ....................................... 28 7.1 Academia – Knowledge-Defined Networking ................................................ 28 7.2 Standards Development Organizations ....................................................... 29 7.3 Industry Consortium – Telecom Infra Project ............................................... 30 7.4 Open Source – Acumos ............................................................................ 32 8. VENDOR PROFILES ..................................................................................... 34 8.1 Afiniti .................................................................................................... 34 8.2 AIBrain .................................................................................................. 35 8.3 Anodot .................................................................................................. 35 8.4 Arago .................................................................................................... 35 8.5 Aria Networks ......................................................................................... 36 8.6 Avaamo ................................................................................................. 37 8.7 B.Yond .................................................................................................. 38 8.8 Cardinality ............................................................................................. 39 8.9 Guavus .................................................................................................. 39 8.10 Intent HQ ............................................................................................... 41 8.11 IPsoft .................................................................................................... 41 8.12 Nuance Communications .......................................................................... 42 8.13 Skymind ................................................................................................ 42 8.14 Subtonomy ............................................................................................ 43 8.15 Tupl ...................................................................................................... 44 8.16 Wise Athena ........................................................................................... 45 9. CONCLUSIONS ............................................................................................ 46 TERMS OF USE ...................................................................................................... 48 Use of this PDF file is governed by the terms and conditions stated in the license agreement included in this file. Any violation of the terms of this agreement, including unauthorized distribution of this file to third parties, is con- sidered a breach of copyright. Heavy Reading will pursue such breaches to the full extent of the law. Such acts are punishable in court by fines of up to $100,000 for each infringement. For questions about subscriptions and account access, please contact [email protected]. For questions and comments about report content, please contact Heavy Reading at [email protected]. © HEAVY READING | SEPTEMBER 2018 | AI IN TELECOM OPERATIONS 3 1. EXECUTIVE SUMMARY The complexity of communications networks seems to increase inexorably with the deploy- ment of new services, such as software-defined wide-area networking (SD-WAN), and new technology paradigms, such as network functions virtualization (NFV). To meet ever-rising customer expectations, communications service providers (CSPs) need to increase the intel- ligence of their network operations, planning and optimization. Heavy Reading believes that artificial intelligence (AI) and machine learning (ML) will be key to automating network operations and enhancing the customer experience. Although "big data" analytics is already widespread in the telecom industry, it is typically conducted in batch, after the fact, and used to manually update rules and policies. In order to move to real-time closed-loop automation, CSPs need systems that are capable of learning autono- mously. That is only possible with AI/ML. Researchers in communication networks are tapping into AI/ML techniques to optimize network architecture, control and management, and to enable more autonomous opera- tions. Meanwhile, practitioners are involved in initiatives such as the Telecom Infra Pro- ject's (TIP) Artificial Intelligence and Applied Machine Learning Group. AI/ML techniques are beginning to emerge in the networking domain to address the challenges of virtualiza- tion and cloud computing. Network automation platforms such as the Open Networking Automation Platform (ONAP) will need to incorporate AI techniques to deliver efficient, timely and reliable operations. However, we must not let ourselves get carried away by the breathless hype surrounding AI/ ML. Many so-called AI/ML systems today are mainly composed of "big data" tools, statistical analysis and a healthy dose of marketing. As our sister market intelligence firm Tractica sur- mises in its report Artificial Intelligence for Telecommunications Applications: "An immature ecosystem for telecom AI use cases has formed, made up of legacy telecom network and business support system (BSS)/operations support system (OSS) vendors; broad-based auto- mated customer service specialists; CRM providers; open-source communities and organiza- tions; established cybersecurity vendors; and a small but impressive
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