《麥肯錫：2024生成式人工智能如何重振電信公司的盈利能力研究報告（英文版）（11頁）.pdf》由會員分享，可在線閱讀，更多相關《麥肯錫：2024生成式人工智能如何重振電信公司的盈利能力研究報告（英文版）（11頁）.pdf（11頁珍藏版）》請在三個皮匠報告上搜索。

1、Technology,Media&Telecommunications Practice How generative AI could revitalize profitability for telcos The new technology offers the sector a real opportunity to reverse its stagnant fortunes.But seizing it will require embracing innovation and agility to an unprecedented degree.February 2024 Gett

2、y ImagesThis article is a collaborative effort by Stephen Creasy,Ignacio Ferrero,Toms Lajous,Vctor Trigo,and Benjamim Vieira,representing views from McKinseys Technology,Media&Telecommunications Practice.49Amid the intense competition and cost-cutting confronting telcos,early evidence suggests that

3、generative AI(gen AI)could be the catalyst to reignite growth after a decade of stagnation.But will it be a groundbreaking differentiator or simply table stakes?Already the technology is on track to become a new norm in the industry.Most telco leaders we surveyed1 say they are developing gen AI solu

4、tions that range from pilots to full-scale deployments,and leading telcos such as AT&T,SK Telecom,and Vodafone have made much-publicized early gen AI commitments and launched trials.Some telcos around the world have started to experience significant double-digit percentage impact from this technolog

5、y.One European telco recently increased conversion rates for marketing campaigns by 40 percent while reducing costs by using gen AI to personalize content.A Latin American telco increased call center agent productivity by 25 percent and improved the quality of its customer experience by enhancing ag

6、ent skills and knowledge with gen-AI-driven recommendations.Most impressive is that these telcos deployed the models in just weeksthe first went live in two weeks,and the second in five.For an industry with a mixed track record for capitalizing on new technologies and legacy systems that slow innova

7、tion,these early results and deployment times illustrate the potentially transformative power of gen AI.These arent one-offs.Pretrained models that can be fine-tuned in days for use cases are readily available,enabling organizations to bring proofs-of-concept to life with minimal up-front investment

8、,achieve impact out of the gate,and scale their efforts.Our experience working with clients indicates the potential for telcos to achieve significant EBITDA impact with gen AI.In some cases,estimates indicate returns on incremental margins increasing 3 to 4 percentage points in two years,and as much

9、 as 8 to 10 percentage points in five years,by 1 The online survey was in the field from November 9,2023,to December 6,2023,and garnered responses from 130 telco operators in North America,Latin America,Europe,Africa,Asia,and the Middle East.2 See“The AI-native telco:Radical transformation to thrive

10、 in turbulent times,”McKinsey,Feb.27,2023.enhancing customer revenue through improved customer life cycle management and decisively reducing costs across all domains.However,while nearly all of the 130 telcos we surveyed are doing something with gen AI,our survey findings suggest a palpable sense of

11、 caution and skepticism in the industry.More than 85 percent of the executives surveyed are cautious to attribute more than 20 percent revenue or cost savings impact by domain,with the greatest enthusiasm for a radical transformation in customer service(Exhibit 1).This blend of optimism and restrain

12、t highlights the critical juncture the industry faces.Seizing the gen AI opportunity to differentiate services and achieve sustainable growth will require the hidebound industry to embrace innovation,exploration,and agility at an unprecedented level and move from decoupled AI efforts to a holistic,A

13、I-native telco.2 The chance for telcos to make this change has never been more accessible.The industry has struggled these last ten-plus years to achieve the potential of“traditional”AI,given the complexity and legacy processes involved.In addition to the significant impact gen AI can bring to bear

14、with entirely new use cases and applications,its ability to learn from vast amounts of diverse data and interact in near-human-like ways may be the tipping point for accelerating broader AI programs and the building blocks that enable them,fueling company-wide transformations.Furthermore,the imperat

15、ive for such change has never been greater.Because gen AI democratizes access to powerful capabilities,any telcoa small operator or large incumbentcan reshape customer expectations and its organizational efficiency.In doing so,they can potentially narrow previously unassailable competitive advantage

16、s and overturn long-standing barriers to growth.Those at the forefront of this movement stand to position themselves to regain growth faster and capture a more significant share of the nearly How generative AI could revitalize profitability for telcos 50$100 billion in incremental value(Exhibit 2).T

17、hat is in addition to the$140 billion to$180 billion in productivity gains that gen AI will create in the industry above what could be unlocked by traditional AI.How can telco leaders use the technology to drive AI transformations and unlock new value?What challenges do they face?And what will it ta

18、ke to succeed?This article offers insights into these critical questions,drawing extensively from our research,industry survey,and firsthand experience implementing these technologies.Gen AI today in the telco industryGen AI represents the latest advance in AI,and it may arguably be one of the most

19、important.The technologys ability to analyze more and different types of data such as code,images,and text,and to create new content,enables new levels of personalization,performance,and customer engagement.With todays capabilities,many use cases are already possible across network operations,custom

20、er service,marketing and sales,IT,and support functions.Exhibit 1 A large majority of telcos have already cut costs with generative AI use cases in customer service and networks.Cost reduction attributed to generative AIin diferent domains,%1Gen AI CxO Survey 2023,n=130,Q:What is the impact(%cost re

21、duction)attributed to generative AI in the diferent domains?Percentages consider answers only from respondents claiming to have achieved impact and to have at least some use cases in execution;fgures may not sum to 100%becauseof rounding.Source:McKinsey analysiscases in customer service and networks

22、.McKinsey&CompanyCustomerserviceNetwork22102ITMarketingand salesSupportfunctions1634475087663015335393276401583131115620215050+Dont knowHow generative AI could revitalize profitability for telcos 51These use cases can both enhance existing AI capabilities(through the inclusion of new unstructured da

23、ta sources)and provide new sources of value(through gen AI and in combination with traditional AI solutions)to deliver significant impact across all key domains.Customer service and marketing and sales currently make up the largest share of total impact(Exhibit 3).Examples of such use cases based on

24、 early pilots include the following:In customer service,where the technology can vastly improve customer experience,increase agent productivity,and enable fully digital interactions.A Latin American telco is enhancing its customer service AI chatbots to improve agent support,a move it anticipates wi

25、ll reduce costs by 15 to 20 percent.The telco also is using gen AI to summarize voice and written client interactions in nearly a dozen use cases,with the expectation that it can reduce associated costs by up to 80 percent.In marketing and sales,where gen AI enables hyperpersonalization,deeper custo

26、mer insights,and faster content generation.A European telco is using the technology to identify new sales leads from customer calls,with its pilot project achieving a more than 10 percent conversion rate.The company can now also create personalized messages and visual media to target individual cust

27、omer microsegments.To do this,the telco feeds a gen AI model standard marketing messages,customer data(including household details,type of phone they use,and where they live),and cognitive biases(for example,whether the customer would be more receptive to messaging that evokes scarcity,such as a lim

28、ited-time offer,or emphasizes authority,such as endorsements,awards,and industry experience).In network operations,where gen AI can optimize technology configurations,enhance labor productivity,extract customer insights from social media,and improve inventory and network planning and management thro

29、ugh the ability to Exhibit 2 Generative AI has the potential to unlock value beyond that previously offered by advance analytics and traditional AI.Generative AIs potential impact on global telecommunications industry,$billion1Updated use case estimates from“Notes from the AI frontier:Applications a

30、nd value of deep learning,”McKinsey Global Institute,April 17,2018.Source:McKinsey Global Institute,The economic potential of generative AI,June 14,2023ofered by advanced analytics and traditional AI.McKinsey&CompanyAdvanced analytics,traditional machine learningand deep learning 2504006010031050014

31、01801540%3570%450680NewgenerativeAI use casesTotaluse-case-drivenpotentialAll worker productivityenabled by generative AI,including in use casesTotal AIeconomicpotentialincrementaleconomicimpactincrementaleconomicimpactHow generative AI could revitalize profitability for telcos 52mine unstructured d

32、ata.One large telco is using the technology to accelerate network mapping by analyzing and structuring data about network components,including specifications and maintenance information,from supplier contracts.This will enable the telco to more accurately assess component compatibility,maintenance r

33、equirements,and morean effort anticipated to improve operational planning and optimize capital productivity.In IT,where the technology can accelerate software migrations and development.Gen AI offers telcos a path to reduce their mounting technical debt and enable capabilities previously deferred be

34、cause of time and 3 “Unleashing developer productivity with generative AI,”McKinsey,June 27,2023.resource constraints.Organizations are applying gen AI to streamline the entire software life cycle,from documenting how a new product,feature,or service will be perceived by end users to generating and

35、scanning code for vulnerabilities before launch.One McKinsey study found that software developers can complete coding tasks up to twice as fast with gen AI.3 In support functions,where gen AI will reduce the costs associated with back-office operations and improve employee productivity.A European te

36、lco uses the technology in a number of ways,including:shortening procurement analysis and negotiation strategy insights from weeks Exhibit 3 Gen AI is expected to enable a long list of use cases and deliver significant value to telcos,with customer service and marketing and sales accounting for the

37、largest share of total impact.Distribution of impact by business domain1The distribution of impact by business domain is based on our experience working with telco companies to deploy gen AI and includes impacts on both capital expenditure and EBITDA.value to telcos,with customer service and marketi

38、ng and sales accounting for the largest share of total impact.McKinsey&CompanySupportfunctionsCustomerserviceMarketingand salesITCustomer-facingchatbots,call-routingperformance,agentcopilots,bespokeinvoice creation3585Copilots for softwaredevelopment,synthetic datageneration,codemigration,ITsupport

39、chatbotsContent generation,hyperpersonalization,copilots for storepersonnel,customersentiment analysisand synthesisProcurementoptimization,workplace productivity,internal knowledgemanagement,contentgeneration,HR Q&ANetworkNetwork inventorymapping,networkoptimization viacustomer sentimentanalysis,ena

40、blingself-healing viacustomer sentimentanalysis on networkproblems156210553545510Share of total impact,%Share of surveyed businessleaders focused on domain,%Example use casesHow generative AI could revitalize profitability for telcos 53to a few hours,reducing recruiting costs with automated screenin

41、g and recommendations,improving employee productivity using internal gen AI chatbots and copilots,and automating most internal content generation.Combined,the company anticipates these efforts will improve employee productivity by 30 percent.New sources of value may also emerge from turning internal

42、 uses cases into new products for their customers.For example,a customer care solution may be offered on demand to small business customers seeking ways to improve their own call centers productivity and service.Telco leaders view ahead:Real change and real challenges In the wake of their initial su

43、ccesses,business leaders we surveyed say they plan to maintain or double their budgets for gen AI in the next year and invest in more than 50 dedicated full-time employees to pursue their gen AI ambitions effectively.More than half are already scaling up use cases.Moreover,survey findings indicate t

44、hat the technology also had a knock-on effect across all AI initiatives.Compared to responses from McKinseys 2022 digital twin survey,we see a 30-percentage-point increase in business leaders who want to invest in and focus more on data and analytics.However,despite the magnitude of the opportunity

45、and the level of interest(and need),our survey found few who follow the kind of holistic approach required to succeed at scale.Only about one-third of telco leaders said they have a capability-building plan for employees on gen AI or are investing in change management effortstwo core building blocks

46、 for building a culture of innovation and the test-and-learn mindset.A similar share said gen AI has yet to be treated as an organizational priority,and that proponents of the technology often 4 See“Rewired to outcompete,”McKinsey Quarterly,June 20,2023.encounter difficulties in justifying use cases

47、a clear signal that much of the push has come from the bottom,not the top,and that more work is needed to elevate gen AI to a CEO-led priority.Moreover,finding appropriate talent and obtaining quality data remain significant challenges for telcos,although confidence about solving these rose among su

48、rveyed leaders this year as compared to last.Finally,the survey findings suggest that gen AI has already begun to influence long-standing market dynamics.While European telecom operators have traditionally lagged in AI and technology transformations,survey findings indicate that they are pulling ahe

49、ad of those in North America in their adoption of gen AI,especially in areas such as network operations(71 percent compared to 58 percent)and IT(67 percent compared to 55 percent).This shift may be a result of greater maturity in managing data privacy.Small and large operators report similar views o

50、n where to prioritize,focusing on customer service and IT in similar measure,suggesting the possibility of new competitive pressures emerging for incumbents(Exhibit 4).The building blocks for a successful gen AI journey In order to achieve the above-mentioned impact,organizations will need to move a

51、way from the labyrinth of proofs-of-concept and scale the technology.As with any digital or AI initiative,we find there are no shortcuts in doing this.The same core building blocks are necessary,namely(1)a business-led roadmap,(2)the right talent,(3)an operating model for scale,(4)technology built f

52、or speed and innovation,(5)quality data that is easily accessible and managed in a responsible and accountable way,and(6)change management to ensure adoption and scaling.4 These are fundamental pillars in effectively scaling use cases and capturing sustainable impact from gen AI in the journey towar

53、d an AI-native telco.How generative AI could revitalize profitability for telcos 54Exhibit 4 Small and large operators are focused on generative AI use cases for customer service and IT in similar measure.Focus on gen AI for customerservice and IT use cases,%1GenAI CxO Survey 2023,n=130,Q:Focus doma

54、ins in gen AISelect the dimensions that apply depending on how you defnegenerative AIin yourorganization.Percentages may not sum to 100%as this question contained multiple selections.Source:McKinsey analysiscustomer service and IT in similar measure.McKinsey&Company$10 billionHowever,while the same

55、holistic approach is required,gen AIs unique capabilitiesits ability to surface new insights from seemingly unrelated data,its reliance on large language models from third-party vendors,and its transformative impact on roles and workpresent new challenges that will require greater agility and additi

56、onal oversight.Next,we outline key differences and provide recommendations on how telcos can best tackle them.Strategy:Determine when to build,buy,or fine-tune solutionsAs with any AI initiative,leaders will need to align on vision,value,and road map,assessing both risks and opportunities,and commun

57、icating guidelines for use across the organization.In building a road map,telco leaders will face a choice:use a commercial,off-the-shelf solution if one exists,fine-tune existing large language models with internal data,or build and train a new foundation model from scratch(what we refer to as the“

58、taker,”“shaper,”and“maker”approach,respectively).Each is suitable for different use cases and has its own costs,requiring leaders to develop not only a clear vision and strategy for which use cases to pursue,but also how.One mistake we see some telcos making is building common gen AI solutions from

59、scratcha content generator or call summarization solution for examplewhen there are nearly a dozen out-of-the-box options on the market today from gen AI start-ups or SaaS vendors injecting gen AI capabilities into existing solutions.Only one-third of surveyed telco leaders say they buy products off

60、 the shelf,suggesting that many telcos continue to embrace a do-it-yourself model.This move is likely to slow innovation and distract talent from more differentiating use cases,as it has in the past with other technologies.How generative AI could revitalize profitability for telcos 55Instead,leaders

61、 should strongly consider partnering with gen AI solution providers and enterprise software vendors for solutions that arent very complex or telco specific.This is particularly critical in instances where any delays in implementation will put them at a disadvantage against competitors already levera

62、ging these services.The handful of solutions leaders can concentrate on shaping or making themselves should enable them to differentiate their offerings or address a strategic business priority,such as delivering the best service or network coverage,and drive sustained economic impact.To do this,one

63、 CIO at a large telco is bringing together business leaders across all key domains to assess hundreds of potential use cases and build a road map for determining when to build,buy,or fine-tune models,and for prioritizing resources and building momentum with early successes.Talent:Upskill and expand

64、internal expertise to innovate with gen AIThe speed of innovation that is now possible with gen AI puts new pressure on telcos accustomed to outsourcing tech talent to build in-house AI expertise.Consider the experiences of two telcosone that continued offshoring and outsourcing tech talent and one

65、that created a dedicated AI team of ten data scientists and engineers.In the time the first telco took to draft requirements for outsourcing gen AI use-case development,the second built and deployed four gen AI solutions.While this new technology democratizes AI by requiring fewer highly specialized

66、 data scientists to build the models,it requires new skills,such as gen AI prompt engineering,which may sometimes be a separate skill embedded within traditional roles.It also requires significantly more data engineers and subject matter experts who understand what data to collect and how,and who ca

67、n oversee daily quality reviews as new forms of data are generated and consumed by these systems,including user queries,responses,and feedback.Capturing the full potential will also require significant upskilling of existing staffeveryone from data scientists to business leaderson gen AI,including t

68、he risks of uploading proprietary data into third-party language models.Some telcos are setting up internal certification and university-led training programs to ensure their teams have the right skills and capabilities to innovate and execute with the technology.For instance,a large telco created a

69、 badging system to identify gen-AI-ready employees who have completed the companys sessions on use,risk,and effective prompting techniques given by its AI,legal,and risk experts.Following certification,users participate in weekly discussion groups to stay abreast of changes and discuss their success

70、es and challenges.McKinsey research has found that such efforts improve the quality of prompts.Operating model:Orchestrate efforts enterprise-wideA significant portion of implemented gen AI solutions can be adapted and reused in multiple use cases.A gen AI chatbot developed to improve agent producti

71、vity,for example,can be repurposed with additional fine-tuning or data to answer frequently asked questions by new employees or provide IT support.An off-the-shelf content generation system for drafting sales proposals may also streamline the development of marketing and business plans.As a result,w

72、ere beginning to see telcos adopt more centralized decision making around gen AI development.This shift includes a greater emphasis on adopting reusable services and self-service components,an evolution of key functions,such as risk,FinOps,and transformation offices,to be more focused on gen AI,and

73、the creation of“control towers”that can oversee all gen AI investments and development efforts.In practice,that can mean,for example,prioritizing the use-case pipeline,identifying opportunities for reusability,setting key performance indicators to measure and track impact at the level of both use Ho

74、w generative AI could revitalize profitability for telcos 56case and enterprise,and managing suppliers and risk.A European telcos control tower evaluates the effects of its gen AI transformation based on three dimensionsfinancial impact,user adoption,and model performanceand aggregates the data in d

75、ashboards that enable the companys top executives to keep tabs on the organizations progress.Similarly,a Latin American telco uses a control tower to consolidate and standardize supplier contracts,tracking key metrics such as scope,duration,and renewal to compare providers more easily,identify poten

76、tial redundancies,and reduce the manual work of digitizing content.Technology:Create a blueprint for reusability,innovation,and excellence Organizations will also need a technology blueprint that enables reusability.For instance,the blueprint should include a framework for determining which large la

77、nguage models to use and when(commercial or open-source models,for example,or those that support hybrid workloads).And it should lay out how to scale a pilotfor example,to extend a pilot that serves 100 call agents to serve more than 10,000 agents with the same latency and cost profile.The blueprint

78、 should also have a framework for determining which gen AI capabilities can be turned into ready-to-use modules to be plugged into different use cases.One large telco,for example,has begun to identify and develop components designed to fetch product data from a large dataset and generate content fro

79、m it that could be reused by data science teams across domains such as customer service,network operations,and sales and marketing.With new gen AI research and capabilities being announced weekly and sometimes daily,technology teams will also need a dedicated gen AI innovation lab to keep abreast of

80、 industry changes and test emerging solutions.For example,one large telcos chief data and analytics officer recruited PhD graduates from universities to staff a gen AI innovation lab and build bespoke solutions ahead of the market to gain a competitive edge.Once new models are deployed,telcos will n

81、eed to monitor model outputs daily to ensure quality and accuracy do not waver as models learn and adapt their responses based on user queries and feedback.Large language model operations(LLMOps)is an emerging practice that aims to streamline the daily management and monitoring of gen AI models.A ke

82、y component of LLMOps is a dedicated operations team to oversee all deployed gen AI models,continuously monitoring for issues and rapidly adapting solutions when needed,just as a network operations team might do for network performance.Organizations can start small now and build capability in this a

83、rea as the field of LLMOps develops.For example,a European telco started by assigning three data scientists to monitor its handful of deployed models and plans to expand the team as more models are deployed.Data:Capture everything,especially unstructured data,and ensure responsible useOne of gen AIs

84、 superpowers is its ability to uncover connections in seemingly unrelated datasets,which has implications for how organizations choose to collect and measure data,and how they manage it to ensure responsible use.Data collection:Telcos will need to think more broadly about data collection,mapping mor

85、e data,setting up pipelines for unstructured data,and creating synthetic data to evaluate outputs.A US telco,for instance,has reached beyond its traditional datasets in its work to develop a customer service agent copilot that will reduce average resolution times by 40 percent across more than one m

86、illion annual chats.As part of their work,the companys data scientists gather institutional knowledge from agent emails and interactions to enable the chatbot to learn from real situations and challenges,and offer detailed descriptions of how to resolve specific issues.The team also creates syntheti

87、c data using a large language model to create sample customer questions and answers,with agents reviewing the outputs for accuracy.How generative AI could revitalize profitability for telcos 57Responsible use:These types of more sophisticated data strategies and tactics come with new regulatory,inte

88、llectual property,and data privacy concerns.Risks abound in this new era,particularly with customer insights,recommendations,and network optimizations being analyzed and generated by third-party large language models and open-source environments.To address the novel risks,telcos need to expand their

89、 data governance programs to address unstructured data.For example,one multinational telco hardware provider created a robust data access process to validate what data can be used in gen AI use cases.Data owners along with legal and security experts work together to validate each use case based on s

90、everal criteria including the criticality of the data to the business(data that is deemed of high importance cannot be input into commercial large language models);the end users(some users cannot access certain data assets);and the risks if the gen AI solution gives an incorrect answer.The team mana

91、ges the process in an agile manner using a simple Microsoft Power App to manage and automate the workflow across teams,and conducts monthly forums to review the process and develop improvements.The organization has reviewed more than 200 use cases,rejecting a number due to intellectual property and

92、other risks,to ensure responsible use for the company.Change management:Ensure adoption and scaling are CEO-led Every role,including everyone from network technicians to HR professionals,will be impacted by gen AI,making vital the need for leaders to begin preparing their employees now to capture th

93、e full value of this transformative technology.With many employees already using the technology in their personal lives,organizations will need to consider how to help them learn to apply the technology in a professional context,upskilling and reskilling staff at scale.Such work can be made easier u

94、sing gen AI,for example to develop and deliver customized and adaptive training programs,and even to onboard employees.For example,another European telco saw firsthand the importance of change management and upskilling when it created a gen-AI-driven knowledge“expert”that helped agents get answers t

95、o customer questions more quickly.The initial pilot,which didnt include any process changes or employee education,realized just a 5 percent improvement in productivity.As the organization prepared to scale the solution,leaders dedicated 90 percent of the budget to agent training and change managemen

96、t processes,which facilitated the adoption of the solution and resulted in more than 30 percent productivity improvement.The telco also used gen AI to create upskilling programs and provide agents with personalized recommendations for improvement once the solution was rolled out.Even though so many

97、companies have already achieved real cost savings and revenue improvements with gen AI,these are still the early days of the technology.In the next five years,emerging capabilitiesincluding significant improvements in natural language understanding,advances in human-like reasoning across multiple to

98、pics,and availability of real-time solutions with increased accuracy and fewer hallucinationsshould unlock even more exciting opportunities beyond the basic improvements seen today.Combined,these gen AI capabilities will enable telcos to redefine industry standards and set themselves apart in the ma

99、rket.For example,network operations could be enhanced and quality standards radically recast with AI copilots that evaluate images from technicians,provide accurate recommendations for remedies,and automatically initiate interventions or work orders.In sales,cognitive copilots could conduct sentimen

100、t analysis on customer calls in real time and guide sales representatives on how best to respond,profoundly altering sales strategies,customer engagement,and overall sales outcomes.Customer service channels How generative AI could revitalize profitability for telcos 58Stephen Creasy is a partner in

101、McKinseys Copenhagen office,Ignacio Ferrero is a partner in the Miami office,Toms Lajous is a senior partner in the New York City office,Vctor Trigo is an associate partner in the Madrid office,and Benjamim Vieira is a senior partner in the Lisbon office.The authors wish to thank Joshan Cherian Abra

102、ham,Eric Buesing,Michael Chui,Guilherme Cruz,Sebastian Cubela,Andrea Faria,Roger Roberts,and Kayvaun Rowshankish for their contributions to this article.Copyright 2024 McKinsey&Company.All rights reserved.using cognitive chatbots could seamlessly answer complex queries in real time while taking into

103、 account privacy and fairness concerns,thereby revolutionizing efficiency while offering customers a human-like experience.Across the enterprise,greater efficiency and productivity could emerge,as domain-specific solutions endowed with an organizations institutional knowledge power an unprecedented

104、wave of automation and AI-driven decision making.The sudden rise of gen AI has brought the dream of the AI-native telco significantly closer to becoming a reality.With it comes the opportunity for telcos to reverse their recent stagnant fortunes and usher in a new era of growth and innovation.The jo

105、urney will not be easy,however.To answer the call of gen AI,telcos will need to quickly adopt a culture of innovation and experimentation enabled by the core building blocks shared in this article,one they have previously struggled to build and maintain.With the technology moving so rapidly,those operators that embrace it now are likeliest to create a significant lead that will be difficult for others to follow.How generative AI could revitalize profitability for telcos 59