AI & Analytics Research
How can telcos apply the latest practices in AI and analytics? And how are they doing?
A3 for enterprise: Where should telcos focus?
As analytics, AI and automation (A3) technologies mature, we explore nine potential A3 capabilities telcos could offer to their enterprise customers. We identify the sweet spots for telcos by assessing the importance of each of the nine capabilities across 14 industry verticals and mapping them against telcos’ existing levels of expertise.
Telco A3: Skilling up for the long term
Over the next 10 years, advances in A3 are going to drastically change the way telcos manage their core businesses, how their businesses are organised, and the demands of their customers. We outline the core capabilities A3 capabilities telcos will need to remain competitive in their core business and to be effective ecosystem players over the next ten years.
DataSpark: Lessons on building a new telco (data) business
Singtel’s data analytics business, DataSpark, has achieved some impressive results, but scaling is hard. Its path highlights lessons on dealing with the challenges facing all telcos building new businesses, e.g. how to govern and manage relationships with the broader organisation, measuring success, and finding the right skills and partners.
AI is starting to pay: Time to scale adoption
AI, coupled with a data-centric approach and automation, looks like it is starting to pay back the operators who have led in this field. Where can industry leaders go next, and what are the key lessons for others on how to ‘jump the curve’?
The future of assurance: How to deliver QoS at the edge
Assuring networks, services and devices in the world of 5G, edge and IoT demands new capabilities in automation, AI and analytics (A3) at the edge of networks. This report sets out a roadmap for telco decision making around assurance tool creation, deployment and possible monetisation.
Telco data monetisation: What’s it worth?
It has been six years since telcos began introducing data and analytics products into their portfolio of enterprise services. This report assesses the potential value of data monetisation across 13 verticals, and by type of data analytics product.
End-to-end network automation: Why and how to do it
True E2E automation has not yet been achieved, but network automation is a reality now, and one which telcos must master to survive. What steps are telcos taking to implement network automation, what challenges must be overcome and what benefits can be expected?
A3 for telcos: Mapping the financial value
We consider the potential financial value of adding analytics, AI and automation (A3) into a telco’s processes. Our modelling assesses the value of A3 in more than 150 processes across core network operations, customer care channels, and sales and marketing.
The value of automation, analytics and AI for telcos – Part 1: The telco A3 application map
This report maps the application of analytics, automation and AI (A3) across the telco organisation, identifying where each technology is most effective, and providing a foundation for understanding and planning the use of A3 to improve core business performance. Part 2 will estimate the potential value of applications.
Elisa Automate: Growing value with sisu
Finnish telco, Elisa, repeatedly achieves surprising wins with innovative new propositions. For example, it now sells Elisa Automate, its fully automated Network Operations Centre (NOC), to other telcos. Most telcos buy their NOCs from vendors. How does this relatively small telco punch so much above its weight? At the heart of the answer is a Finnish word which cannot directly be translated to English: sisu.
Telco AI: How to organise and partner for maximum success
Our latest research shows that only one in five telco AI projects has made the leap from proof of concept (PoC) to live deployment. How can telcos improve the hit rate and achieve real performance improvements?
Telecoms data analytics – Where’s the real value?
Although nearly all operators aspire to deploy autonomous networks and personalised customer services, few have actually implemented advanced analytics at scale across their organisations. Almost universally, telcos are hampered by incomplete and siloed data sets and cultural resistance. What have the industry’s leaders done to overcome these challenges?
Network AI: The state of the art
Autonomous networks are still many years away, but AI-supported automation is a reality now, which all telcos must master to survive. What steps must telcos take to implement AI in network maintenance, optimisation and planning, and what is it worth?
Personal data: Treasure or trash?
Telefónica’s systematic and sustained push into personal data management holds valuable lessons for other telcos about building trust and credibility. The report also covers personal cloud / data plays by NTT DOCOMO and financial services company Mint.
AI in customer services: It’s not all about chatbots
There’s no doubt that AI is a transformative technology, and chatbots are a focus for many telcos looking to benefit from it. We look at case studies from Telenor, Deutsche Telekom and AT&T to find telcos’ best path toward employing AI. Despite the hype, chatbots may not be the best first step.
Big data analytics – Time to up the ante
Big data analytics could help telcos improve performance, rebuild revenues and regain relevance with consumers, but it is not a quick fix. This report looks at what telcos have done in this area so far and what they need to do next.
AI on the Smartphone: What telcos should do
Artificial intelligence (AI) is more powerful and affordable than ever, and the leading consumer-facing AI platforms – Google, Apple, Facebook and Amazon – are in an arms race to bring the technology to smartphones. AI will radically change the way people use smartphones, but what are the implications for data traffic and consumer expectations, and what role should telcos play in this evolution?
AI: How telcos can profit from deep learning
Artificial intelligence (AI) is improving rapidly thanks to the growing use of deep neural networks to teach computers how to interpret the real world (deep learning). These networks use vast amounts of detailed data to enable machines to learn. What are the potential benefits for telcos, and what do they need to do to make this happen?