Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry

Amdocs and NVIDIA today announced they are collaborating to optimize large language models (LLMs) to speed adoption of generative AI applications and services across the $1.7 trillion telecommunications and media industries.(1)

Amdocs and NVIDIA will customize enterprise-grade LLMs running on NVIDIA accelerated computing as part of the Amdocs amAIz framework. The collaboration will empower communications service providers to efficiently deploy generative AI use cases across their businesses, from customer experiences to network provisioning.

Amdocs will use NVIDIA DGX Cloud AI supercomputing and NVIDIA AI Enterprise software to support flexible adoption strategies and help ensure service providers can simply and safely use generative AI applications.

Aligned with the Amdocs strategy of advancing generative AI use cases across the industry, the collaboration with NVIDIA builds on the previously announced Amdocs-Microsoft partnership. Service providers and media companies can adopt these applications in secure and trusted environments, including on premises and in the cloud.

With these new capabilities — including the NVIDIA NeMo framework for custom LLM development and guardrail features — service providers can benefit from enhanced performance, optimized resource utilization and flexible scalability to support emerging and future needs.

“NVIDIA and Amdocs are partnering to bring a unique platform and unmatched value proposition to customers,” said Shuky Sheffer, Amdocs Management Limited president and CEO. “By combining NVIDIA’s cutting-edge AI infrastructure, software and ecosystem and Amdocs’ industry-first amAlz AI framework, we believe that we have an unmatched offering that is both future-ready and value-additive for our customers.”

“Across a broad range of industries, enterprises are looking for the fastest, safest path to apply generative AI to boost productivity,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration with Amdocs will help telco service providers automate personalized assistants, service ticket routing and other use cases for their billions of customers, and help the telcos analyze and optimize their operations.”

Amdocs counts more than 350 of the world’s leading telecom and media companies as customers, including 27 of the world’s top 30 service providers.(2) With more than 1.7 billion daily digital journeys, Amdocs platforms impact more than 3 billion people around the world.

NVIDIA and Amdocs are exploring a number of generative AI use cases to simplify and improve operations by providing secure, cost-effective and high-performance generative AI capabilities.

Initial use cases span customer care, including accelerating customer inquiry resolution by drawing information from across company data. On the network operations side, the companies are exploring how to proactively generate solutions that aid configuration, coverage or performance issues as they arise.

(1) Source: IDC, OMDIA, Factset analyses of Telecom 2022-2023 revenue.
(2) Source: OMDIA 2022 revenue estimates, excludes China.

Editor’s Note:

Generative AI uses a variety of AI models, including: 

  • Language models: These models, like OpenAI’s GPT-3, generate human-like text. One of the most popular examples of language-based generative models are called large language models (LLMs).
  • Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences.
  • Generative adversarial networks (GANs): These models use two neural networks, a generator, and a discriminator.
  • Unimodal models: These models only accept one data input format.
  • Multimodal models: These models accept multiple types of inputs and prompts. For example, GPT-4 can accept both text and images as inputs.
  • Variational autoencoders (VAEs): These deep learning architectures are frequently used to build generative AI models.
  • Foundation models: These models generate output from one or more inputs (prompts) in the form of human language instructions.
Other types of generative AI models include:  Neural networks, Genetic algorithms, Rule-based systems, Transformers, LaMDA, LLaMA, BLOOM, BERT, RoBERTa. 

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Nvidia Survey Reveals How Telcos Plan to Use AI; Quantifying ROI is a Challenge

A Nvidia sponsored survey of more than 400 telecommunications industry professionals from around the world found a cautious tone in how they plan to define and execute on their AI strategies.  Virtually every telco is already engaged with AI in some way, although mostly at an early stage.  NVIDIA’s first “State of AI in Telecommunications” survey consisted of questions covering a range of AI topics, infrastructure spending, top use cases, biggest challenges and deployment models.  The survey was conducted over eight weeks between mid-November 2022 and mid-January 2023.

Amid skepticism about the money-making potential of 5G, telecoms see efficiencies driven by AI as the most likely path for returns on investment.   93% of those responding to questions about undertaking AI projects at their own companies appear to be substantially underinvesting in AI as a percentage of annual capital spending.

Some 50% of respondents reported spending less than $1 million last year on AI projects; a year earlier, 60% of respondents said they spent less than $1 million on AI. Just 3% of respondents spent over $50 million on AI in 2022.

The reasons cited for such cautious spending? Some 44% of respondents reported an inability to adequately quantify return on investment, which illustrates a mismatch between aspirations and the reality in introducing AI-driven solutions. 34% cited an insufficient number of data scientists as the second-biggest challenge.

The biggest telco objectives for AI are to: optimize operations (60%), lower costs (44%) and enhance customer engagement (35%).  Respondents cited use cases ranging from cell site planning and truck-route optimization to recommendation engines.

Just over a third of respondents said they had been using AI for more than six months.  31% said they’re still weighing different options, 18% reported being still in a trial phase and only 5% said they had no AI plans at all. Most industry execs say they see AI technologies will positively impact their business – 65% agreed AI was important to their company’s success, and 59% said it would become a source of competitive advantage.

Operators are spending a fraction of their capex budgets on AI projects – last year half said they spent less than $1 million on AI. At the top end, 2% spent more than $50 million in 2021, with that number rising to 3% in 2022.

The latest AI Index compiled by Stanford University puts telcos at the forefront of AI deployment. Using its own data and that from a McKinsey study, it found that the highest level of AI adoption is in product or service development by hi-tech companies and telcos (45%), followed by AI in service operations (45%).

The biggest single application in any industry was natural language text understanding deployed by 34% of hi-tech and telco firms, with 28% implementing AI-based computer vision and 25% using virtual agents.

Factors impacting AI investment decisions for 2023:
  • Moving from proof of concept to production/scale 47%
  • Economic uncertainty                                                    46%
  • Infrasctructure upgrades                                               46%
  • Market differentiation                                                    34%
  • Change in priority of data science                                20%
  • 92% will either increase or maintain their AI spend in 2023.
In the near term, the focus appears to be on building more effective telecom infrastructure and unlocking new revenue-generating opportunities, especially together with partners.  The challenge will be moving from early testing to widespread adoption of AI.


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