How Generative AI is Reshaping the Telecom Sector
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Communication service providers (CSPs) are at an inflection point. From stagnating revenues, to network strain in meeting the demands of 5G, to challenges in delivering innovative customer experiences, there’s enormous pressure on the telecommunications industry to transform.
Over the past few years, CSPs around the globe have turned to artificial intelligence (AI) to address some of these challenges, but the lion’s share of an operator’s operational expenses are still spent on infrastructure and data management. This has limited their ability to capitalize on core data assets and develop differentiated customer experiences that meet individual needs.
Enter generative AI, a type of machine intelligence that’s received enormous attention as of late. We’ve all marveled over its ability to generate text that reads like it was written by a person, to create new images, and to build even musical scores. It’s a riveting addition to the AI toolset — and one that complements machine learning (ML) and its ability to identify patterns to make predictions, spot efficiencies, or interpret large data sets.
But while there’s a lot of hype around generative AI, at Google Cloud we view it through a much more practical lens for the telecommunications industry. Generative AI can accelerate the transformation already underway, with its potential to streamline many of the tools and processes that CSPs engage with daily, bringing a new level of natural interaction between people and computers, and enabling machines to be programmed to carry out an action with a spoken request, and respond in natural, interactive ways.
Generative AI builds on existing Google Cloud data, AI, and ML services. For example, Contact Center AI, with human-like interactions between callers and computers, has been successfully adopted by CSPs for many years, increasing the satisfaction of both customers and call center workers. As we add generative AI to this technology, CSPs and their customers will see even greater capabilities and impact, for example, virtual agents that not only provide helpful information, but also let customers make payments and execute other transactions. With generative AI, CSPs will be able to harness customer call summaries to better understand customer sentiment and identify cross-sell and up-sell opportunities. CSPs could also easily and quickly build and deploy virtual agents informed by customer conversations that enable more innovative and personalized customer interactions. And that’s just the start.
Three key areas
The contact center is just one of the areas where practical and generative AI will help drive new value. When reflecting on the key challenges facing CSPs today, three areas in particular stand out where generative AI may be transformative:
- Personalized experiences: In addition to further improvements in customer call center interactions, generative AI can deliver improved personalization in ecommerce interactions — a big factor in helping customers sort through their choices of phones and calling plans. Personalization is also important for lowering churn, offering relevant new services, and managing the customer lifecycle. For example, generative AI could enable CSPs to produce marketing campaign content customized for select themes, and target individual customers with customized text and images.
- Autonomous networks: Generative AI will also help to pave the way for autonomous networks by connecting multiple complex AI/ML models used across network planning and operations with large language models (LLMs) that can understand network behaviors and create action plans in areas including network capacity planning and performance. For example, generative AI will enable CSPs to train models with customer experience and sentiment data to build better prediction capabilities. Importantly, the customer data sets used to tune these models are not public, but curated internal customer data — significantly enhancing privacy, factuality, and relevance, while protecting intellectual property. In addition, generative AI will be able to help network planning and design, which requires high levels of reporting and analysis.
- Streamlined operations: Both operations center uptime and field service efficiency are crucial for managing costs and improving customer satisfaction. In particular, applying generative AI to field service devices can speed up diagnostics and analysis, and can even help with installation, parts, and troubleshooting, and minimizing the number of times companies have to send out trucks and improve field-service training. Generative AI will also provide a productivity boost to IT development processes, enabling code generation and troubleshooting to deliver reliable software products and services.
Data security and reliability
An under-discussed area in generative AI is the importance of data quality and data security in building and training the LLMs that power the technology. Many CSPs are rightly concerned about intellectual property leaking both into and out of LLMs, risking the security of their systems and their intellectual property. We’ve long offered industry-leading data security and privacy technologies, and with generative AI integrated with Vertex AI, we can ensure all data is secured within the CSP’s environment.
Meanwhile, to ensure that their LLMs generate accurate information, CSPs are building out scenarios and use cases for training on smaller, controlled amounts of their own data, sometimes accompanied by highly trusted sources from partners and others. Google Cloud also provides tools including Prompt Engineering, Tuning, and Reinforcement Learning from Human Feedback to further ensure data factuality and reliability. This will likely result in the first generative AI applications targeted to smaller, high-impact problems, like optimizing network topologies.
The human element
Of course, people are critical to the success of generative AI, whether that means solving problems in call centers, field service workers combining AI information with their own knowhow, marketing and creative teams brainstorming with generative AI to make new presentations and marketing materials, or operations engineers augmenting and approving an AI suggestion. We’ve built a lot of amazing technology to help augment what people cannot do: synthesize millions, if not billions, of records and sources to help energize new workflows and productivity.
Telecommunications is a fast-changing industry that is tech savvy and hungry to learn and deploy the best possible new technologies, and that includes generative AI. Each meeting with a CSP inspires new ideas, sparks more use cases, and leads to more industry-changing initiatives. It’s exciting to see this pace of change, and we are only just getting started.
We look forward to showing you more soon as we deep dive into some of the exciting CSP industry use cases in upcoming blog posts. We also invite you to find out more about how Google Cloud is partnering with CSPs around the world to deliver a holistic cloud transformation.
Companies Can Speed-up AI Developments with NVIDIA’s One Stop Catalog for AI Software
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NVIDIA GPU-powered instances on Google Cloud provide an optimal platform for organizations to develop their AI applications on the latest hardware and software stack, then seamlessly deploy those applications at scale in production.
Simplifying Workflows to Speedup AI Developments
NVIDIA recently announced the One Click Deploy feature on the NVIDIA NGC catalog, the hub for GPU-optimized AI software. Developed in collaboration with Google Cloud, this feature simplifies the deployment of AI software, to a single click from the NGC catalog.
This allows data scientists to deploy frameworks, software development kits and Jupyter Notebooks directly to Google Cloud’s Vertex AI Workbench, a new managed Jupyter Notebook service on top of Vertex AI, Google’s service for machine learning operations.
Under the hood, this feature launches the JupyterLab instance on Google Cloud Vertex AI Workbench with optimal instance configuration, preloads the software dependencies, and downloads the NGC notebook in one go.
NGC Catalog – One Stop for AI Software
NVIDIA is expanding the rich trove of NVIDIA AI software in the catalog to ensure AI practitioners have everything they need to get started — from frameworks to models.
All of the AI models in the catalog come with credentials. They’re like resumes that show the model’s skills, the dataset that trained it, how to use the model and how it’s expected to perform.
These model credentials provide transparency, which gives developers the confidence in picking the right model for their use case.
The NGC catalog also hosts Jupyter Notebooks tailored for the most popular AI/ML applications. Examples include:
Computer Vision – A collection of models for detecting human actions, gestures and more.
Automatic Speech Recognition – An end-to-end workflow for text-to-speech training.
Recommendation – A collection of example notebooks to help build end-to-end recommendation services.
Serve Robotics uses Vertex AI and the simple easy-to-use interface that the NGC One Click Deploy delivers.
“NGC catalog allows our ML research engineers to launch environments for experiments on Vertex AI with a single click. This saves us the efforts on ML infra setup and lets researchers focus on the ML problem in the computer vision and robotics space more efficiently.”—Kaiwen Yuan, Director of ML/Head of Perceptions & Predictions at Serve Robotics
Accelerate ML Deployments
Explore hundreds of Jupyter notebook examples for speech, computer vision and recommenders and, if you’re just getting started with AI, browse NVIDIA’s collection of Jupyter notebook examples and run it using the One Click Deploy feature on Google Cloud Vertex AI.
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Metro Consolidates 100 SAP Instances on Google Cloud
German retailer Metro, with operations in 25 countries across Europe and Asia and employing over 150,000 people, decided to consolidate its 100 SAP instances into one SAP S/4HANA system running on the Google Cloud Platform.
This helped the company lower costs, increase efficiency, and improve customer experience. In addition, the company is using Google BigQuery and advanced analytics to increase its sales.
Watch Timo Salzsieder, Metro CIO/CSO, explain the company’s journey with SAP on Google Cloud.
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How AI Has Helped Enterprises Adapt Quickly to Moments of Change
The pandemic has clearly caused a tremendous amount of rapid change for businesses across industries and regions.
In this session, Michael Baldwin, Head of Product – Financial Services, Google Cloud speaks about ways that artificial intelligence has helped enterprises adapt to those changes.
He will cover trends that Google Cloud experts are witnessing as they work with enterprises and the impact those trends have on key industries.
Some of these include:
- Significant shifts in demand
- Increased cost pressures
- Supply chain uncertainty
- Spikes in customer support cases
- Virtual work for continuity of services
- Accelerated digital transformation
He then demonstrates, with specific examples, how AI can helpful in these moments of change.
Cloud and AI Paves the Future of Finance: Excerpts from FIA Boca 2022
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Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade.
I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, for a fireside chat at FIA Boca 2022 to discuss the future of markets and policy, the new technologies that are already paving the way for greater speed and transparency, and how cloud can help promote greater resiliency, performance, and security to enable the long-term vision for the market. The following is a summary of our discussion.
The current state of cloud technology
When it comes to technology adoption, we’re seeing the market and participants adopt cloud technologies, and increasingly, machine learning (ML) on a wider scale. Cloud technology allows for easier, faster, and much more secure experimentation with large datasets and ML.
A recent Google sponsored study by Coalition Greenwich (September, 2021) showed that more than 93% of trading systems, exchanges, and data providers are in some way providing services on the cloud. The same study, revealed that about 72% of the financial industry across the buy side and sell side, intend to consume public cloud-data based market data within the next 12 months.
Data-driven decision-making and risk management have always been, and continue to remain, the cornerstones of the financial markets. Over time, technology innovation has facilitated access to better insights from data, and therefore, better decision-making and the ability to manage risk. That expectation is now mainstream, and will continue to grow in sophistication.
The multi-phased technology trajectory
The movement of exchanges to the cloud will occur in a “crawl-walk-run” fashion, with low-hanging fruits the first to be picked in the near term while bigger, paradigmatic changes will occur over the medium and long term. Some organizations are starting all three stages simultaneously, understanding that each will move at an independent cadence.
The “crawl” phase is one in which foundations are built, starting with organizations moving data to the cloud and experimenting with some degree of analytics. It’s one of the most important phases because it’s where the opportunity to increase transparency and risk management takes shape.
In moving to the cloud, the infrastructure – which in the past relied on a combination of people, processes, and some technology – becomes the code that runs applications. This early phase is key to empowering organizations to shift to a cloud-based, agile-first operating model that makes it easier and more seamless to launch new products in the future, including by freeing up people and resources from IT management to more mission-focused work.
Establishing the cloud operating model simplifies the “walk” and “run” phases where compliance is more automated, latency-sensitive applications are more readily available, and the next generation of exchanges, market participants, and regulators is better prepared to meet future challenges.
The “walk” phase is where much of the innovation happens. Exchanges are making significant progress in leveraging foundational data decisions in the “crawl” phase and innovations in the cloud to improve settlement, clearing, risk management, collateral management, and compliance, and launch new products.
And finally, the “run” phase is where organizations will start to move the latency-sensitive markets to the cloud, as the markets increasingly will demand low-latency and high performance along with transparency and analytics to solve historical obstacles to market access.
Opportunities for both regulators and market participants
Any time significant technological change takes place, regulators explore its implications, particularly with respect to their ability to meet their regulatory objectives.
Increasingly, we are seeing technological change driving more opportunities for regulators and market participants alike. Such changes may also allow better protection of the marketplace, with greater integrity and transparency.
Over time, regulatory regimes – rules, regulations, statutes, interpretations, and guidance – will also adjust to new technologies, both benefiting the marketplace and advancing regulatory goals.
As one example, the cloud is increasing the ability to meet compliance obligations by allowing compliance to be built into transactions. Moreover, predicated on the vision of real-time regulatory reporting, and given the pace of technological change in the marketplace over the last several years, various regulators have been using more advanced analytics. This trend will continue to help them more effectively and efficiently meet their objectives, and monitor and meet the expectations they have for the entire market.
Machine learning’s role in the financial markets
Google Cloud’s head of AI and Industry Solutions, Andrew Moore, said that ML will be doing three key things for us in the next 10 years: giving us meaning, providing concierge services, and serving as a guardian. Extracting information that is critical to investor decision-making can be extremely important. With more data than ever, ML can increase the ability to process it while also becoming more accessible in the cloud and better supporting regulatory objectives.
The technology will likely manifest in trading and anti-money laundering activities as they relate market functions, as well as managing a wide variety of risks – supporting the interests of both investors and regulators in terms of decision-making, surveillance, and protections.
Rather than taking individuals out of the equation, the digitization of markets, assets, and guard rails combined with ML will allow people to focus their expertise in different ways to achieve key objectives.
Building the market foundation for the future
The goals of operational resiliency, security, and privacy will continue to be critical for building the market foundation for both participants and regulators. While technology promises to create advantages in concrete, tangible ways, it will be important to scrutinize potential risks and concerns.
Priority one for technology providers is to build an environment of trustless security, including encryption at motion and encryption at rest, ensuring that markets are operationally resilient while instilling confidence for any exchange that runs on top of that infrastructure. Multicloud architectures and approaches are likely also to be part of the solution for operational resilience.
Throughout time, liquidity has been the outcome of improved access, transparency, and security. Technology providers are responding by sharing both the responsibility for, and fate of, the markets of the future to build an efficient, faster, and more transparent and secure financial industry.
You can learn more about our approach in our newest white paper, Building the financial markets foundation for the future.
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Public cloud, big data, and AI technologies offer competitive advantages and cost savings for capital markets firms ready to make the transition. This paper discusses the three phases capital markets firms go through in transitioning to public cloud, and the workloads, benefits, and cultural changes that characterize the three phases:
Infrastructure Optimizers: The first step on the public cloud journey, where firms focus on migrating specific workloads to save costs.
Cautious Strategists: Firms build on the success of their first public cloud migrations, and begin to change the way they develop technology to increase cost savings and start taking advantage of capabilities only available on public cloud.
Transformative Innovators: Firms shift to a fully public cloud-enabled mentality, and fully leverage the flexibility and agility of the public cloud to build industry-changing solutions and attract top IT talent.
Additionally, we reveal the five things that capital markets innovators who have advanced to the transformation phase do well in their adoption of cloud, big data, and AI technologies across the front, middle, and back office functions.
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