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Traditional Telcos At Inflection Point Need To Rewire Their Future With Gen AI

Traditional Telcos At Inflection Point Need To Rewire Their Future With Gen AI

傳統電信公司正處於轉折點,需要藉助人工智能重新規劃未來。
Business Today ·  08/08 04:20

The telecommunications (telco) sector is rapidly evolving as businesses and consumers seek new applications and experiences that 5G has enabled. This new connectivity is fueling exciting progress in autonomous vehicles, driving innovative new applications for industrial internet of things (IoT), and helping us design and build futuristic smart cities. However, the recent emergence of generative artificial intelligence (AI) has the power to completely transform the industry.

隨着企業和消費者尋求5G支持的新應用和體驗,電信(電信)行業正在迅速發展。這種新的連接推動了自動駕駛汽車的激動人心的進步,推動了工業物聯網(IoT)的創新新應用,並幫助我們設計和建設未來的智慧城市。但是,最近出現的生成式人工智能(AI)有能力徹底改變行業。

It is pushing telcos to rethink where to place investments supporting transformation – in particular with dwindling net promoter scores (NPS) – a key customer-satisfaction metric. New industry entrants in the form of challenger brands are offering feature-rich self-service experiences that favor higher data caps over traditional voice minutes are also adding to the disruption.

它促使電信公司重新考慮將支持轉型的投資投向何處,尤其是在淨推薦值(NPS)不斷下降的情況下,這是一項關鍵的客戶滿意度指標。以挑戰者品牌爲形式的新行業進入者正在提供功能豐富的自助服務體驗,這些體驗比傳統語音時長更高的數據上限,這也加劇了顛覆性。

The AI-Powered Telco
Pivoting to new business models has typically been an arduous process, often taking years of re- platforming and complex systems integration projects. However, imagine a world where legacy telcos could rebuild from the ground up using generative AI?

人工智能驅動的電信公司
轉向新的業務模式通常是一個艱鉅的過程,通常需要多年的平台重組和複雜的系統集成項目。但是,想象一下傳統電信公司可以使用生成式人工智能從頭開始重建的世界嗎?

The availability of scalable compute, a massive increase in data, and rapid advancement in the areas of large language models (LLMs), all make the idea of an AI-powered telco – once a far-fetched idea – a real possibility. For many regulated companies such as telcos with privately held data, their extensive proprietary databases present an untapped opportunity. The unique data they've amassed over years of operations has never been opened up to generative AI models before – till now. Generative AI is able to understand, read, connect the dots, find similarities and differences across these very large datasets. For example, agents are now empowered to craft intelligent solutions tailored to a telco's subscriber demographics, and run their operations more efficiently in service of the customers.

可擴展計算的可用性、數據的大量增加以及大型語言模型(LLM)領域的快速發展,都使人工智能驅動的電信公司的想法——曾經是一個牽強附會的想法——成爲了真正的可能性。對於許多受監管的公司,例如擁有私人數據的電信公司來說,他們龐大的專有數據庫提供了一個尚未開發的機會。到目前爲止,他們在多年的運營中積累的獨特數據從未向生成式人工智能模型開放過。生成式 AI 能夠理解、讀取、連通這些超大型數據集之間的相似之處和不同之處。例如,代理商現在有權根據電信公司的訂戶人口統計數據量身定製智能解決方案,並更有效地運營業務以服務客戶。

Here are my top tips for telco leaders who are looking to start transforming with AI:
Tailor-made or Off-the-Rack AI? Telcos Can Have It Both Ways. By deploying off-the-shelf, ready-to-use generative AI models available through platforms like Amazon Bedrock, telcos can expedite transformation initiatives that previously took years to execute. Customer service can be reinvigorated with multilingual customer service chatbots, network teams can now conduct fault analysis, Root Cause Analysis, and optimization quicker; marketing teams can run personalized campaigns at scale, and developers can leverage secure coding assistants to significantly reduce prototyping timelines.

對於希望通過人工智能開始轉型的電信公司領導者,以下是我的重要建議:
量身定製還是現成的 AI?電信公司可以雙管齊下。通過部署可通過 Amazon Bedrock 等平台提供的現成的、隨時可用的生成式 AI 模型,電信公司可以加快以前需要數年才能執行的轉型計劃。多語言客戶服務聊天機器人可以爲客戶服務注入活力,網絡團隊現在可以更快地進行故障分析、根本原因分析和優化;營銷團隊可以大規模開展個性化活動,開發人員可以利用安全的編碼助手來顯著縮短原型設計時間。

Early movers like Deutsche Telekom have already seen double-digit improvements and answer accuracy by tapping into powerful AI infrastructure they now have access to. To supercharge productivity, the UK's BT Group has deployed a coding assistant that has generated over 200,000 lines of code for its 1,200 developers. And CelcomDigi in Malaysia will develop Bahasa Melayu language algorithms to create solutions like chatbots for its linguistically and culturally diverse customer base.

像德國電信這樣的早期行動者通過利用他們現在可以訪問的強大的人工智能基礎設施,已經實現了兩位數的改進,答案准確性也得到了兩位數的提高。爲了提高生產力,英國電信集團部署了編碼助手,該助手已爲其1,200名開發人員生成了超過20萬行代碼。馬來西亞的CelcomDigi將開發馬來語算法,爲其語言和文化多元化的客戶群創建聊天機器人等解決方案。

Telcos with digital sub-brands can also benefit from generative AI. For example, cloud-native, generative AI-enhanced billing and charging solutions can help telco product managers dynamically generate pricing, discounts, and bundled offers based on fine-grained user segmentation, with the necessary marketing collateral and assets to take these out to their subscribers.
Telcos also have the option of creating their own bespoke foundation models (FMs) tailored to their customers, and the broader industry. South Korea's SK Telecom, for example, created TelClaude to drive AI-powered contact centers, improve existing services like spam detection, and develop personal AI assistants.

擁有數字子品牌的電信公司也可以受益於生成式人工智能。例如,雲原生、生成式 AI 增強型計費和收費解決方案可以幫助電信產品經理根據精細的用戶細分動態生成定價、折扣和捆綁優惠,並提供必要的營銷宣傳材料和資產將其提供給訂閱者。
電信公司還可以選擇創建自己的定製基礎模型(FM),爲其客戶和更廣泛的行業量身定製。例如,韓國Sk Telecom創建了Telclaude,以推動基於人工智能的聯絡中心,改善垃圾郵件檢測等現有服務,並開發個人人工智能助理。

AI Accuracy Starts with Data Accuracy and Security
Regulated companies like telcos already possess extensive data from market research, networks, devices, and customer records, which generative AI can rapidly analyze to uncover valuable insights that help companies evolve or even reinvent themselves. However, understanding and protecting this data is crucial. Regulated companies already have policies to prevent data misuse in place and are adept at addressing these concerns, enabling faster AI adoption. This experience gives telcos an advantage over organizations that haven't yet addressed data management issues or don't see a path forward with AI.

AI 準確性始於數據的準確性和安全性
像電信公司這樣的受監管公司已經擁有來自市場研究、網絡、設備和客戶記錄的大量數據,生成式人工智能可以快速分析這些數據,以發現有價值的見解,幫助公司發展甚至重塑自我。但是,了解和保護這些數據至關重要。受監管的公司已經制定了防止數據濫用的政策,並且善於解決這些問題,從而加快了人工智能的採用。與尚未解決數據管理問題或看不到人工智能前進道路的組織相比,這種經驗使電信公司更具優勢。

"We need to ensure that our data is secure and not used by other actors," the head of IT at a leading telco recently told me.
We know our customers care deeply about the provenance of their data, because in a lot of cases, that data is actually their customers' data. With generative AI, there are justifiable concerns around transparency, bias, and hallucinations (AI-
generated responses containing false or misleading information). These must be carefully governed. Just like all other industries, telcos need to implement rigorous testing and monitoring while having a "human in the loop" to ensure AI systems operate reliably and ethically.

一家領先的電信公司的IT負責人最近告訴我:「我們需要確保我們的數據安全,不被其他行爲者使用。」
我們知道我們的客戶非常關心他們的數據的來源,因爲在很多情況下,這些數據實際上是他們的客戶數據。對於生成式人工智能,人們有理由擔心透明度、偏見和幻覺(AI-
生成的回覆包含虛假或誤導性信息)。這些都必須得到謹慎的管理。就像所有其他行業一樣,電信公司需要實施嚴格的測試和監控,同時讓 「人員參與其中」,以確保人工智能系統可靠和合乎道德地運行。

When selecting a generative AI service, my advice to telcos is to prioritize solutions that allow bringing third-party models to your data instead of the reverse. This safeguards data privacy and confidentiality across all businesses.

在選擇生成式人工智能服務時,我對電信公司的建議是優先考慮允許將第三方模型引入數據的解決方案,而不是相反。這可以保護所有企業的數據隱私和機密性。

Intelligent Algorithms Require Human Talent
Generative AI represents a departure from traditional automation, where machines execute predefined tasks with mechanical precision. Instead, it empowers humans by augmenting their capabilities, unleashing creative potential, and enhancing productivity in unprecedented ways.

智能算法需要人才
生成式人工智能與傳統自動化背道而馳,在傳統自動化中,機器以機械精度執行預定義的任務。取而代之的是,它以前所未有的方式通過增強人類能力、釋放創造潛力和提高生產力來賦予人類權力。

A 2024 AWS-commissioned AI skills APAC survey which found that 97% of IT and Telecommunications 1 To better understand emerging AI usage trends and where workplaces might be headed, AWS commissioned global research firm, Access Partnership, to conduct an Asia-Pacific AI skills survey. Almost 15,000 employees an employers expect to use AI tools by 2028. As this trend to adopt AI accelerates, telco leaders must provide pathways for employees to pursue AI skills training, cultivating the talent necessary to maximize the potential of generative AI tools. This calls for greater collaboration between governments, industries, and educators to help employers implement AI training programs and guide employees in
matching their AI skillsets to the right roles.

由AWS委託進行的一項2024年亞太地區人工智能技能調查發現,97%的IT和電信1爲了更好地了解新興的人工智能使用趨勢以及工作場所的發展方向,AWS委託全球研究公司Access Partnership進行亞太地區人工智能技能調查。預計到2028年,將近15,000名員工和僱主將使用人工智能工具。隨着採用人工智能的趨勢加速,電信公司領導者必須爲員工提供接受人工智能技能培訓的途徑,培養必要的人才,以最大限度地發揮生成式人工智能工具的潛力。這要求政府、行業和教育工作者之間加強合作,以幫助僱主實施人工智能培訓計劃並指導員工
將他們的人工智能技能與正確的角色相匹配。

Spanish telco Telefónica recognized that training its internal team was essential to closing the cloud- skills gap and advancing its digital transformation to better support its customers. To achieve this, Telefónica worked with AWS to develop a multiyear upskilling plan. Working with AWS Training and Certification teams, Telefónica Tech equipped its teams with the practical cloud skills necessary to fully leverage AWS capabilities.

西班牙電信公司Telefo'nica認識到,培訓其內部團隊對於縮小云技能差距和推進數字化轉型以更好地支持其客戶至關重要。爲了實現這一目標,西班牙電信與AWS合作制定了一項爲期多年的技能提升計劃。Telefo'nica Tech 與 AWS 培訓和認證團隊合作,爲其團隊提供了充分利用 AWS 能力所需的實用雲技能。

AI is a Winning Edge
In 2024, telco leaders face stark a choice: maintain the status quo or boldly reinvent their value-creation formula by embracing generative AI. The stakes are high: it's not only the telcos' success that hinges on their ability to digitally transform, the sobering fact that 70% of business transformation efforts fail means that putting in place a measured strategy is increasingly going to be a table stakes issue, as customers' expectations relentlessly evolve.

人工智能是制勝優勢
2024年,電信公司領導者面臨着嚴峻的選擇:維持現狀或通過採用生成式人工智能大膽重塑其價值創造公式。風險很高:不僅僅是電信公司的成功取決於他們的數字化轉型能力,70%的業務轉型努力失敗這一發人深省的事實意味着,隨着客戶期望的不斷變化,制定謹慎的策略將越來越成爲賭注問題。

Jayanth Nagarajan, Head of Telecommunications Industry, Asia Pacific & Japan, AWS

Jayanth Nagarajan,AWS 亞太和日本電信行業負責人

声明:本內容僅用作提供資訊及教育之目的,不構成對任何特定投資或投資策略的推薦或認可。 更多信息
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