AI in Telecom in todays scenario Final.pptx

ai in telecpm

1. T e l e co m A I AI is creating business value in terms of improved performance, higher efficiency, enhanced customer experience as well as creating new business models and use cases for 5G, IoT and enterprise. What is telecom AI? Telecom AI revolutionizes communication networks, enhancing efficiency and user experience. Through predictive analytics, it optimizes network performance, predicts failures, and prevents downtime. AI-driven chatbots and virtual assistants streamline customer service, offering personalized experiences. Automation of tasks like network maintenance and resource allocation reduces costs and boosts scalability, powering digital transformation.

2. I n dustries and enterprises are currently shifting gears and accelerating their digital transformations as 5G , IoT and edge computing gain traction. But with these new opportunities also come new complexities for network operations – a coexistence of new and legacy technologies, hybrid networks, a variety of frequency bands and spectrums, and an abundance of connected devices. At the same time, emerging requirements from industrial use cases need further performance enhancements and network optimization. W h y i s A I u s ef u l i n t e l e c o mm u n i c a t i o n s ?

3. K e y e l e m e n t s f o r t e l e c o m A I Z e r o - t o u c h op e r a ti o n s By using a combination of currently available and well-understood AI techniques within a flexible architecture, we can reach a high degree of practical autonomous operation. This will lead us into an era of intelligent, autonomous, zero-touch network operations . The real value of AI is not limited to applications which connect to the network but will ultimately be realized in the networks. T r u s t w o r t h y A I To realize the full potential of AI, trust needs to be established in the development, deployment and use of AI. This is why it’s critical for us to build human trust in AI , addressing aspects spanning from explainability and human oversight to security and built-in safety mechanisms. Trustworthiness is a prerequisite for AI, and we are building it into the system by design. A I i n N e t w o r k s 5G network evolution presents new complexities for telecom operators. Transforming these complexities into opportunities, Ericsson’s artificial intelligence (AI) technologies bring together big data with unique network domain expertise to deliver unprecedented benefits for network operations and more. Predictive Analytics: Leveraging data to forecast network performance, identify potential issues, and optimize resource allocation. Automation: Streamlining tasks such as network maintenance, customer service, and resource management to improve efficiency and reduce costs. Virtual Assistants: AI-powered chatbots and virtual agents enhance customer support by providing personalized assistance and resolving queries. Network Optimization: Utilizing AI algorithms to optimize network traffic, enhance bandwidth management, and ensure seamless connectivity. Predictive Maintenance: Proactively identifying and addressing potential failures or issues in network infrastructure to prevent downtime and maintain service quality. Security: Implementing AI-driven solutions to detect and mitigate cybersecurity threats, safeguarding sensitive data and ensuring network integrity. Scalability: Enabling telecom infrastructure to adapt and scale dynamically to accommodate fluctuating demands and evolving technologies. Data-driven Insights: Extracting actionable insights from large volumes of data to inform strategic decision-making and drive business growth.

4. Future autonomous networks - a t o m o r r o w m ad e p o ssib l e b y t h e A I j o u r n e ys s t a r t i n g t o d a y Learn more about what the future holds for intelligent, autonomous networks. F i n d ou t m o r e B e n e fi t s o f T e l e c o m A I AI is transforming industries across the globe, and telecom is no exception. So, what benefits does this technology offer for networks? Where are we already seeing the value of AI being proven? What new opportunities can we expect to emerge, and what ingredients are key to successfully navigating your AI journey? Join our experts and find out, in our new blog series – sign up and receive each post as it’s released! S i g n u p n o w Transforming telec om with generative AI Generative AI has taken the world by storm, and exciting use cases are already emerging in the telecom industry. Join us as we explore four main avenues through which this technology could have the most impact, including real-world applications for RAN, network management and more. R e a d t o b l o g p o s t

5. E x p l o r e ou r o f f e r i n g N e t w o r k a u t om a ti o n a n d A I Discover how automation and AI are transforming mobile networks – and why Ericsson is leading the industry in AI technology. A I i n op e r a ti o n s How we are taking network and IT operations from manual, reactive, and incident-driven, to proactive and data-driven operations – all based on AI and automation. A I i n r a d i o a cce ss n e t w o r k s How do you improve your network performance and user experience in today’s densified networks? AI is one of the keys in conquering these challenges, for LTE as well as 5G. H e a r f r o m ou r e x p er t s i n A I

6. R e mo t e r obo ti c s a n d A I Hear from Elena Fersman, Head of Research Area Artificial Intelligence at Ericsson Research, how the relationship between humans and robots is changing. Discover why it’s paving the way towards zero-touch operations and a landscape in which the network will automatically adjust and react based on the needs of the robots. W a t c h h e r e A I a n d i ts n ece ss i ty f o r a b e t t e r 5 G AI adoption is essential to efficient network management and operations. In this video, Jonas Åkeson, Head of Automation & AI, explains how AI and automation will help address the complexity of 5G networks, drive efficiencies and improve customer experience as well as open new revenue streams. W a t c h h e r e T e l e c o m A I t e c hn o l o g ie s a n d a pp r o a c h e s Ma c h i n e r e a s o n i n g R e i n f o r ce m e n t l e a r n i n g I n t e n t- b a s e d n e t w o r k s D i g i t a l twi n s  

7. A I n a ti v e T r u s t w o r t h y A I L e sso n s i n A I - d r i v e n z e r o - t o u c h operations Gain unique insights into how AI is being leveraged to improve business agility and customer satisfaction. R e a d m o r e AI capabilities solving major challenges for service providers We are not building a general-purpose artificial intelligence platform, trying to solve problems in every industry. Instead, we have developed our skills and capabilities in telco AI to address the challenges of service providers. Working closely with our customers’ challenges, data and products, we are providing them with a different approach to getting value from their networks. We have identified and prioritized four AI capabilities that make our AI as effective as possible. T e l ec om a n d A I e x p er ti s e Our AI is anything but artificial. It’s built on a strong foundation of combining deep telecom domain expertise, data science and AI knowledge. These skills always work hand in hand, close to customer problems, collaborating to solve them. This means that we know the right problems to solve and how to solve them with the right AI solutions.

8. A I - r e a d y t ec h n o l o g y Ericsson’s technology is AI-ready. We have made architectural decisions that make AI easy to deploy and use and that works together holistically across the network. Combined with our telecom domain expertise, this means that we can use AI where it makes sense, solving the right problems for our customers. A I u s e c a s e c o - c r e a ti o n We have learned through experience that co-creation in developing solutions together is critical. We develop our AI in the field together with our customers, using real network data, our domain expertise. Together with a portfolio of ready-made use cases and pre-trained algorithms, we adapt to various customer contexts and problems. In this way, we can solve the right problems for our customers instead of providing an untrained general platform or solutions to only address very specific challenges. A I - p o w e r e d po r t f o li o We are not a niche player, focusing on one use case. Instead, we build AI into every part of our portfolio where it makes sense, from baseband to centralized clouds. With AI spread out through all our products, we can see the whole end-to-end context and how we make every part of the network work together. U n i q u e a cce ss t o d a t a Through access to multiple and diverse data sets from CSPs that we collaborate with, we can train our algorithms. This valuable data

9. creates a unique foundation to build powerful algorithms, benefitting CSPs and their end-users. O u r A I s o l u t i o n s i n a c t i o n Our artificial intelligence solutions target service providers to address their challenges to maximize efficiency and end-customer experiences and create new revenue streams. They are embedded throughout the network, built by people with extensive AI and telecom expertise, and with an AI-first mindset and technology in every product or service. I n t e lli g e n t R A N A u t om a ti o n A series of technologies, solutions and services that use intelligent, machine learning to improve network performance, enhance customer experience and reduce operational costs and energy consumption. 40 p e r c e n t r e d u c t i on on b a d q u a l i t y c e ll s . A u g m e n t e d MIMO s l ee p In Ericsson Radio System, AI algorithms run on the baseband to predict traffic patterns and autonomously turn off antennas as required to reduce energy usage. It’s also possible to combine with Cell sleep and the Low Energy Scheduler solutions for even better savings. 14 percent - average saving for a proof-of-concept cluster I n t e lli g e n t D e p l o y m e n t Ericsson has reshaped the way we deploy networks from the ground up with a digital, agile, and modular suite of tools and services that

10. enable customers to capture the full benefits of their valuable assets. 99 percent first time right site solution from design to delivery. C o g n iti v e d e s i g n & op ti m i z a ti o n Industry-leading suite of cognitive software solutions for network planning, design, tuning and optimization. 15 m i n e x e c u t i on o f p e r f o r m a n c e d i a g no s t i c s a c r o ss 1 M c e ll s E n e r g y i nf r a s tr u c tu r e op e r a ti o n s AI and data analytics to create energy efficiencies on the radio network. ~15 percent energy OPEX reduction through intelligent energy optimization S e r v i ce C o n ti n u i ty A I a pp s u i t e Implementations of the AI app suite in real-life networks has revealed results,, for example, making the network including radio and core more robust and stable. 35 p e r c e n t r e d u c t i on i n cr i t i c a l i n c i d e n t s . U p t o 60 p e r c e n t r e d u c t i on s i n n e t w o r k p e r f o r m a n c e i ss u e s . Mo r e a bo u t th e m a i n a r e a s w e a dd r e ss wi t h o u r A I s o l u ti o n s E n s u r e n e t w o r k p e r f o r m a n ce The world becomes increasingly dependent on superior network performance, both from a consumer and a business perspective. It’s the main driver of consumer satisfaction, and absolutely necessary when deeply integrated into industrial production processes. It needs to be

11. always up, always on, always performing. We use AI in our networks to secure top-class performance. Realize the value of network slicing in a cloud-native core Virtualization and cloudification promise improved agility, lower cost and is the foundation for new service creation. A cloud-powered network with a virtualized core is a competent tool for service provider transformation, but it also comes with challenges. We realize the value of the virtualization of core and cloud technologies with AI. Improve energy efficiency and meet the demands of sustainability Reducing network energy consumption benefits everyone – the consumer, the environment and the bottom line. We use AI to plan, manage and run networks more efficiently from an energy perspective. Create an outstanding, future-proof customer experience Future customer experience is based on more than coverage and capacity. With diverse use cases, the definition of customer experience will be equally diverse. To meet these requirements, manual activities are not enough, and AI will enable service providers to create and assure experience no matter what those services are. T u r n op e r a ti o n s i n t o a n e ffi c i e n t b u s i n e ss e n a b l e r Operations are key for service providers to realize business opportunities. Service providers need to create relentless efficiency in operations by becoming data-driven and predictive. We use AI to increase efficiency in operations while transforming to deliver on business KPIs rather than network KPIs. Bring out the most from your existing infrastructure AI is not only about 5G and the future. It’s a powerful tool to bring out most of the infrastructure already in place, making sure that you can keep the cost down and customer experience high. I n s i g h t s o n T e l e c o m A I To deliver cognitive networks, we build human trust in AI To deliver cognitive networks by 2030, that learn, reason and act on business intent almost autonomously, we must build trustworthy AI. Hear from our CTO Erik Ekudden how our networks will become the platform of choice for future digital enterprise. R e a d t h e b l o g p o s t

12. Z e r o T o u c h O p e r a ti o n s : I n s i g ht s f r om C S P e x ec u ti v e s Zero-touch makes network operations more data-driven, predictive and proactive. Technologies like artificial intelligence reduces the need for manual activities and enables greater business agility – but it requires preparation. Read about the journey to Zero Touch Operations from a CSP operator executives’ perspective. R e a d t h e a r t i cl e S t a y u pd a t e d R e a d t h e r e s e a r c h a r t i cl e C o g n i t i v e p r o c e ss e s f o r a d a p t i v e i n t e n t - b as e d n e t w o r k i n g A u t ono m o u s l y o p e r a t e d a n d s e l f - a d a p t i n g n e t w o r k s w i ll make it possible to utilize the capabilities of 5G networks in ne  w business models and achieve an unprecedented level of efficiency in service delivery. Intents will play a critical role in achieving this zero-touch vision, serving as ... 

13. W h i t e p a p e r s a n d r e po r ts E n s u r i n g e n e r g y e f fi c i e n t n e t w o r k s w it h A I E x p l a i n a b l e A I – ho w h u m a n s c a n t r u s t A I U X d e s i g n i n A I  PDF A d o p ti n g A I i n o r g a n iz a ti o n s Artificial intelligence in RAN - a software framework for AI-driven RAN automation Cognitive processes for adaptive intent-based networking P r i v a c y - a w a r e m a c h i n e l e a r n i n g w it h l o w n e t w o r k f oo t p r i n t Employing AI techniques to enhance returns on 5G network investments  PDF AI and automation – trends shaping the digital economy  PDF B l o g po s ts AP R 02 , 2024 | B l o g p o s t AI/ML security in mobile telecommunication networks M A R 14 , 2024 | B l o g p o s t Intent-Based Automation: The next evolution of network orchestration M A R 06 , 2024 | B l o g p o s t Corporate sustainability management - the step from regulation to business value F E B 29 , 2024 | B l o g p o s t AI, APIs and Mobile World Congress 2024: insights from Ericsson CMO Stella Medlicott F E B 16 , 2024 | B l o g p o s t From data transmission to value creation: optical fiber's role in the digital revolution R e a d m o r e News M A R 17 , 2024 | P r e ss r e l e as e

14. Ericsson and Umniah announce strategic alliance to elevate network performance with cutting-edge AI F E B 29 , 2024 | P r e ss r e l e as e Ericsson and Chunghwa Telecom boost collaboration in 5G Advanced Technology F E B 22 , 2024 | N e w s SoftBank Corp. and Ericsson Japan K.K. successfully demonstrate high-speed automatic optimization of 5G networks through external control of base stations F E B 15 , 2024 | N e w s Ericsson launches Explainable AI in Cognitive Software to accelerate AI adoption in network optimization F E B 15 , 2024 | P r e ss r e l e as e Ericsson launches Service Orchestration and Assurance to fuel CSP innovation R e a d m o r e

15. Solutions that enable advanced retail experiences, smarter scheduling, self-healing, and better coaching can reduce complexity, lower costs, and ma k e b o t h c u s t o m e r s a n d e m p l o y ee s h a pp i e r .  T e chn o lo g y , M e d ia & T e le c o mm u n ic a t ion s Sign In | Subscribe  How AI is helping revolutionize telco service operations

16. O Why now is the time to deploy AI Field and service operations account for 60 to 70 percent of most telcos’ operating budgets, so applying AI can offer real and rapid benefits. The industry has already faced a decade-plus of increasing cost pressure, and the returns on necessary infrastructure investments are barely outpacing the cost of capital. Now the sector must cope with the pandemic-related changes to how people work and shop, which have caused demand to surpass all expectations. At the same time, staffing telco operations functions has become increasingly difficult, with labor shortages and new coronavirus variants further complicating the process. Holding on to workers is also harder than ever, especially in the United States, where 40 percent of employees say they’re likely to leave their current jobs within the next three to six months. To stay ahead, operators will need to make critical investment decisions around customer and employee experience. At the same time, they need to offer efficient and effective processes to keep costs down while increasing retention of both customers and employees. These are the very areas where front-runner telcos are deploying AI solutions and finding success. As the following use cases illustrate, those solutions fall into several categories: smart scheduling and forecasting; store-of-the-future experiences enabled by machine learning–driven personalization and other basic operational efficiency; self-healing in which problems are either preempted or solved automatically; and smart coaching.

17. Enhancing the retail customer experience A critical area in which AI tools can help enhance operations is the retail setting, where store-of- the-future technologies and tools along with smart scheduling and forecasting can assist in breaking through the bottlenecks that plague the current retail experience. Getting a phone line activated can take up to an hour on average, making the retail setting a prime opportunity for upselling. for example, some 40 to 50 percent of phone sales happen in a retail setting, and 70 percent of those transactions involve the purchase of an accessory such as a protective screen cover, phone case, or headphones. Yet customers are left to sit idly while their phone line is set up and their purchase completed.

18. “ The future of shopping: Technology everywhere ” on McKinsey.com

19. Improving operations in the contact center As AI applications become increasingly sophisticated, leading telcos look not only to reduce customer need to call or message regarding problems that could be prevented or solved in other ways. They also want to ensure upsell opportunities that could result from a contact are maximized.

20. For example, billing inquiries are a major source of customer calls. A self-healing solution would consider the primary driver of the billing issue at hand, along with the customer’s billing history, lifetime value, and propensity to call based on a bill change, and then take any number of different actions. One customer might just need an explanation included with their bill to be satisfied, while another customer might need a retroactive data package applied. And still another customer might be likely to choose an upgrade or take some other revenue-enhancing action, in which case it might be better for them to call. Such a self-healing solution would involve clustering different customer profiles to identify their propensity to call and the likely revenue and customer lifetime value impact of their call. At the same time it would predict what impact different identified self-healing actions would have and pinpoint the best action to grow customer lifetime value. Once in place, the self-healing solution could be augmented with a machine-learning feedback loop to reflect the effectiveness of the actions taken, thus enabling the solution to become increasingly precise in its decisions. A self-healing AI could also help reduce call center demand by troubleshooting issues with wireline devices (for example, a router that is slowing down could be identified and repaired before the customer even notices). A solution that runs continuous checks on device speed and performance could triangulate one device’s performance against that of nearby devices to determine the best course of action to take. If the problem is that a customer’s router needs to be reset or configuration changes downloaded, this could be done remotely at a time when the customer isn’t actively using the device and without their knowing a problem had arisen.

21. If the problem required customer intervention, the solution would predict the customer’s propensity to call about the issue before either sending them an alert or prepping the necessary information to reduce the length of the eventual call. For an issue that requires on-site resolution, a truck and crew could be dispatched before customers notice the slower network speed and call to complain. These kinds of measures can help telcos drastically reduce call volumes, which improves the customer experience by enabling agents to dedicate time to truly complex, value-added activities. For example, spending more time on calls that require direct customer interaction to address a critical need or offer education on products and services can provide a better experience and lead to improved customer satisfaction. This also improves the employee experience, as workers’ capabilities are put to better use and the number of dissatisfied customers they have to handle is reduced. Over time, this can help strengthen operational efficiency and build brand loyalty. As with retail outlet staffing, call center staffing can benefit greatly from AI-driven smart scheduling to ensure the right call center employees are on duty at the right time (see Exhibit 2). Better information on what types of customers call and why can be combined with workforce scheduling systems to optimize staffing levels and timing. Combining AI-powered forecasting with a multichannel schedule optimizer that can assign agents across functions, including the call center, message center, and even retail stores, creates a feedback loop that allows the system to grow more intelligent.

22. One telco with several thousand call center agents built core AI models for forecasting and schedule optimization, with the resulting dashboards enabling 10 to 20 percent improvement in o v e r t i m e c o s t s a n d m o r e e ffi c i en t u s e o f s t a f f , a s we ll a s e nh a n c e d c u s t o m e r ex p e r i e n c e . Additionally, the time required by workforce management to manage forecasting and scheduling

23. was cut in half, and the company saw 30 percent greater flexibility in worker allocations across locations and job types through centralized scheduling that spanned multiple business units. Improving the fi eld force capabilities On the field force journey, telcos have to perform a balancing act between customers, employees, and external forces over which they have little control. Smart AI coaching solutions can help improve the performance and service levels of frontline workers and their supervisors, as well as the experience of customers and employees. These sophisticated tools use machine-learning algorithms to generate performance insights along with coaching resources that rely on employees’ normalized performance metrics as inputs. The result is timely and situationally relevant digital instruction, as well as celebratory nudges, to help encourage desired behaviors

24. One telco that piloted AI-based smart coaching with its distributed workforce of more than a few thousand employees found that it was able to solve the problem of not having an effective way to differentiate coaching based on individual employees’ needs. The company knew it needed to improve key metrics across productivity, quality, learning effectiveness, and level of engagement, and built an AI-driven coaching program that would address all four areas.

25. The program was able to identify personalized coaching opportunities based on past performance and deliver targeted nudges and best practices directly to employees’ handheld devices. Not only did this approach help to increase employee performance, but it ultimately boosted job satisfaction as well. Field force operations can also benefit from smart scheduling, particularly when it comes to on- time arrival of technicians. Weather, traffic, and other external forces can all have a major impact on scheduling, which in turn affects customer and employee experience, especially when both t e c hn i c i a n a n d c u s t o m e r e n d u p c a lli n g i n r e s p on s e t o a l a t e a rr i va l . As with call center and retail scheduling, an ML-based AI can use historical data to reveal causes of delays that are otherwise unclear and then combine that data with weather and traffic data to dynamically reschedule technicians in the field. The solution could even assess the likelihood of technical hitches arising based on historical and customer data, and alert the technicians to which parts are likely to be needed for that day’s visits. One telco that built a solution using historical data on seasonality, routing of technicians, and other external factors such as traffic and weather created up to 80 to 90 percent improved accuracy in its forecasting and workforce management. Getting started

26. Telcos that are just getting started with AI to support their service operations or are thinking about doing so would benefit from considering some best practices already battle-tested by the front- runners, including the following actions: Identify the top use cases for AI for each business unit and its relevant service operations journeys—call centers, retail, and in-store uses, or field operations, for example—based on the most critical gaps or pain points. Then run a prioritization exercise to rank the opportunities and use cases according to feasibility, impact, and ease of implementation. Determine the availability of data for each use case being considered and create a road map for the data asset build that will be needed to enable it. Start with descriptive analytics and use an agile approach in the early phases of the AI- driven service ops journey, layering on predictive and prescriptive analytics to construct a strong foundation. Building minimum viable products through dedicated sprints and scaling up based on a continuous learning approach will help ensure strong outcomes. Set up teams in AI pods that incorporate both technical talent as well as business leads and subject-matter experts, depending on the use case. Working jointly, these cross-functional experts develop and test the AI use cases and solutions.