Generative AI is the pinnacle of data science. It will boost profits, reduce costs, and help you expand into new markets. To take full advantage of generative AI’s capabilities, train your models on all your data.

The world is being transformed by AI-assisted medicine, education, scientific research, law, and more. Today, researchers at the University of Toronto use generative AI to model proteins that don’t exist in nature; pharmaceutical giant Bayer now uses generative AI to accelerate the process of drug discovery; and education provider Khan Academy has developed an AI chatbot/tutor, Khanmigo, to personalize learning. And with each passing day, the list of AI use cases across all industries only continues to grow.

According to the Capgemini Research Institute, nearly all (96 percent) of executives cite generative AI as a hot topic of discussion in their respective boardrooms. Generative AI is not just used as an aid to surface information the way a search engine does; with generative AI, organizations can combine their proprietary data with foundation models that have been pre-trained on a broad base of public data to create a sustainable competitive advantage.

Generative AI then becomes the most knowledgeable entity within your organization.

However, as with all analytics, generative AI is only as good as its data. To fully leverage AI, an organization needs a solid data foundation and organizational norms that facilitate responsible and effective use of data.

Data readiness for generative AI depends on two key elements:

1. The ability to move and integrate data from databases, applications, and other sources in an automated, reliable, cost-effective, and secure manner
2. Knowing, protecting, and accessing data through data governance

Automated data pipeline platforms, like Fivetran, allow enterprises to capture all of their data, irrespective of the source platform. These automated tools reduce the friction and overhead required to maintain the flow of data to continuously train generative AI applications.

OPERATIONALIZING GENERATIVE AI

To operationalize generative AI effectively, organizations must establish a solid foundation of automated, reliable, and well-governed data operations. Generative AI requires a modern and scalable data infrastructure that can continuously integrate and centralize data from a variety of sources, including both structured and semi-structured data.

However, as businesses start to operationalize generative AI, they may encounter a number of challenges.

• Data quality and preparation: Generative AI models are only as good as the data they are trained on. It is important to ensure that the data is high-quality, clean, and well-organized. This includes identifying any potential biases in the data that may distort the outputs of any model trained on it.
• Security and governance: Security and governance in the context of generative AI concern masking sensitive information, controlling data residency, controlling and monitoring access, and being able to track the provenance and lineage of data models.
• User experience: It is important to design user interfaces for your model that make it easy for people to interact with your models.
• Scalability: It is important to choose a generative AI platform that can scale to meet your needs at a reasonable cost.

Generative AI models are trained on massive datasets of text, code, images, or other media. Foundation models, which are off-the-shelf generative AI models that are pre-trained on large volumes of (usually public) data, may be specialized by industry or use case. Choosing the right foundation model can have a significant impact on performance and capabilities. For example, a foundation model that specializes in code generation will do so in a more comprehensive and informative way than a model that is trained on a general dataset of text. Other specialties of foundation models may include sentiment analysis, geospatial analysis, image generation, audio generation, and so on.

While you can easily make use of pre-trained, publicly available AI models, your data is a unique asset that differentiates your organization from the competition. To make the most of it, you must additionally supply foundation models with your business’s unique context.

With access to your organization’s accumulated data, a properly tuned generative AI model can become the most knowledgeable member of your organization, assisting with analytics, customer assistance, sales and marketing, software engineering, and even product ideation.

The Fivetran product team leverages generative AI and natural language processing technologies to develop Fivetran Lite Connectors in a fraction of the time of Fivetran’s standard connectors, while ensuring the same high quality, data integrity, and security customers expect from Fivetran.

In addition, several notable organizations have already found practical ways to use generative AI. Global commercial real estate and investment management company JLL recently rolled out a proprietary large language model that employees access through a natural language interface, quickly answering questions about topics such as an office building’s leasing terms. Similarly, the motor club in the US, AAA, now uses generative AI to help agents quickly answer questions from customers. Of the 100 tech companies profiled in the Forbes Cloud 100, more than half use generative AI.

According to Carrie Tharp, VP Strategic Industries, Google Cloud, “Generative AI opens up a new avenue, allowing people to think differently about how business works. Whereas AI and ML were more about productivity and efficiency – doing things smarter and faster than before – now it’sabout ‘I can do it completely differently than before.’”

Until enterprises get the data right, the nirvana of asking generative AI app-specific and contextual organizational questions in a “Siri-like” way will remain elusive. Get the data right, and it opens up possibilities for all analytics workloads, including generative AI and LLMs.

To make full use of an ever-expanding roster of powerful foundation models, you must first ensure the integrity, accessibility and governance of your own data. Your journey into generative AI and the innovation and change it can bring will be fueled by high-quality, usable, trusted data built on automated, self-healing pipelines.

“GENERATIVE AI APPLICATIONS ARE ONLY AS GOOD AS THE DATA THAT POWERS THEM.”

INNOVATION TAKEAWAYS

OPERATIONALIZE GENERATIVE AI

Operationalization begins with centralizing data and modernizing the data stack to include all available data.

AUTOMATED DATA ACCESS

By automating data pipelines, enterprises can focus on improving data models and algorithms to accelerate the efficacy and ROI of investing in a generative AI application.

CREATE AN UNFAIR ADVANTAGE

Generative AI trained on your data will provide insights and guidance driven by your data, creating a unique competitive advantage that cannot be replicated.

Interesting read? Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 7 features 16 such fascinating articles, crafted by leading experts from Capgemini, and partners like Aible, the Green Software Foundation, and Fivetran. Discover groundbreaking advancements in data-powered innovation, explore the broader applications of AI beyond language models, and learn how data and AI can contribute to creating a more sustainable planet and society.  Find all previous Waves here.

Capgemini has worked with high tech leaders for over 50 years. We understand the role of high tech – quite simply, it’s the engine that powers the highest levels of innovation. It’s the type of world-changing technology that transforms businesses, entire markets, and even human history. For example, when invented, the steam engine was absolutely high tech. Flip phones? High tech. When these innovations are early and have yet to cross the chasm to mass adoption, we sense the advance far away. The media starts buzzing with predictions of life-altering experiences and sudden changes in how we live, work, and play. Businesses begin to both worry and get excited. We call technologies in this critical, most exciting phase ”near-technology” or “near-tech.” This stage comes with unique challenges, where even a few days’ delay can be the difference between a market leader and a historical footnote.  

Near-tech describes the kinds of technology that exist not in research labs but just within reach. It represents tangible possibilities – technology that can, with the right expertise and capabilities, enable real opportunities. 

The extraordinary and the everyday 

These advancements ride in on massive waves of disruption, completely changing our global perspectives and human capabilities. The tension between extraordinary technology and everyday life drives the development of new business models and innovations. Right now, we are entering a remarkable time. Immense technological waves are cresting the horizon – generative AI, truly human robotics, individualized gene therapies, new chip manufacturing and lithography capabilities – changing the world and devastating our everyday. But living in the tension between the extraordinary and the every day isn’t new to Capgemini – it is our legacy. 

Outrageous yet logical 

The greatest innovations are born out of big bets by entrepreneurs and companies willing to challenge the core assumptions surrounding us. Software must run on-premises… enter SaaS. It’s only a phone… enter the Smart Phone. The common characteristic of transformative technologies is that they first fundamentally disrupt our mindset, then disrupt our infrastructure, manufacturing, supply chains, business models, and security. They may seem like outrageous ideas at first, but eventually, something tips and the disruption becomes normalized: This is the future. And the wave begins. We believe deeply that these innovations are outrageous and, at the same time, logical, and we help bring them to the world. It is our mindset of possibility that makes us different.

We are builders 

Capgemini High Tech recognizes that success is embracing and exploiting near-tech. It’s about bringing together talent and technology to help organizations reach near-tech faster. However demanding or specific the challenge might be, an expert can help solve it. We proudly act as a comprehensive partner for High Tech clients looking to leverage near-tech to transform their business. But what makes us unique is that we don’t just define a company’s future but also help them build it. 

Making connections 

Perhaps the most essential tool for any business seeking new opportunities through high tech is connection – connections between knowledge, capability, and technologies. By drawing on broad networks of deep expertise, companies can use high tech to enter industries and markets that were otherwise unobtainable until now. We enable our clients to connect with the right semiconductor manufacturing partner, the right business strategy, the right design and UX partner, the right production and shipping plan, and the right data and software security solution. We bring the connections to make near-tech real.

Capgemini High Tech serves the tangible possibilities that are just within reach – decisions and actions that matter now. Whether through connections, living in the gap between the extraordinary and everyday, building real solutions, or embracing the outrageous, we are the partner for near-tech.

Let’s innovate the near technology of your industry together. 

For questions, reach me here!

Takeaways from the GSA European Executive Forum and SEMICON West 2023

Introduction

In the past months, we witnessed two major semiconductor events across the globe: The 2023 Global Semiconductor Alliance’s (GSA) European Executive Forum gathered leading global senior executives on June 14-15 in Munich to embrace the most pressing issues affecting an industry caught in the throes of change. SEMICON West 2023 took place in San Francisco on July 11-13 to discuss key challenges affecting the global microelectronics industry. In this article, we’ve distilled the major trends that arose during both events; trends that will continue to shape this industry in the foreseeable future. These include supply chain volatility, sustainability, government investments, generative AI, geopolitical tensions, equality, and the tremendous opportunities in automotive. We’ve also mapped out Capgemini’s role as an intermediary in building trust and understanding and helping to welcome new players to the market.

Resilient supply chains require flexible production and shipments

Semiconductors are pervasive and will become much more pervasive. Semiconductors are the brain of digitization. It is not widely known that semiconductors are among the most traded goods in the world. Any disruption in the semiconductor supply chain can significantly impact the global economy.

The first big trend centers around building resilience to the volatility of the semiconductors’ supply chain and ensuring end-to-end transparency to predict forecasts better and manage demand. Supply chain issues caused by the fragility of the supply chain and the incompatibility of production cycles have cost semiconductor company customers, such as automotive, many billions of dollars in lost sales and profits. Automotive customers controversially asked that they be given control over the flow of chips from one Tier 1 to another.  For semiconductor companies, it is imperative to build the resilience and process maturity that will enable them to switch easily between industries. GSA triggered a dialogue on how to be better prepared for whatever the future may hold by increasing inter-industry cooperation and building strategic relationships.

Harnessing the transformational power of sustainability

Another major trend broached at both events was the clear focus on sustainability and producing the products that drive it. As the earth’s ability to provide what we need decreases, the need to act on sustainability is increasing. Semiconductor companies have come out with their strategy and goals to become sustainable. Companies are launching initiatives focused on producing sustainable products that enable low power consumption or reduce the carbon footprint of their customers.

Sanjiv Agarwal, Vice President Global Semiconductor Industry Leader, says, “Semiconductor companies need to embrace sustainability and aim to make technology sustainable. Sustainability is everyone’s responsibility.”

As the semiconductor industry is projected to double by 2030, and carbon emissions projected to quadruple by 2030, sustainability and government investments also dominated the agenda at SEMICON West as well. Five key messages emerged:

• AI is very high computing power-intensive – for example, a ChatGPT search consumes thirteen times as much energy as a Google search
• Companies should design for sustainability – there is a need for dedicated engineering teams to support sustainability goals (equipment, sub-fab, process recipes, and operations). Companies such as Intel and Applied Materials have engineers dedicated to sustainability as part of the engineering PODS
• Every semiconductor company has thousands of suppliers – Intel, for instance, has 16K suppliers; however, most suppliers have yet to set their sustainability goals. Therefore, there is an urgent need to establish metrics and develop a measurable roadmap to achieve net zero. Sustainable procurement is gaining attraction in the market.
• Digital technologies can help reduce the carbon footprint and make fabs more sustainable – this can be achieved by optimizing efficiency through advanced analytics (ML, analytics, and AI); improving digital lifecycle collaboration within fabs (digital twin platform across the lifecycle of a fab can reduce production loss and energy waste); and ensuring enterprise-level collaboration across fabs.
• Companies have made more progress on their US sites than in other regions – for example, Intel and STMicro are net positive water in the US but not in other regions.

Generative AI – Need for extreme compute power and smaller suppliers

GenAI is probably our generation’s most disruptive innovation, and it can potentially shape humanity’s future. From simple automation of tasks to writing codes to drug discovery, the scope of areas where it can find use is practically limitless, and the semicon industry is right at the forefront to enable this transformation journey. With such technologies that have the potential to impact so many different industries in a myriad of ways, there are always the early adopters, the ones who need a plan, the late risers, the ones with the FOMO, and the ones who choose to be in their state of inertia unless the market forces apply.

Surprisingly with Gen AI, no one wants to maintain the status quo. There is a clear indication that almost every industry is looking for ways to adopt Gen AI in its day-to-day operations, be it in Manufacturing, Sales, Marketing, IT, or customer service – and the High-tech segment is leading the pack in terms of adoption. As Gen AI-based applications and use cases for design and manufacturing support start to proliferate, it will transform how the current automation in factories functions. This will create a major shift in how the industry adapts and molds itself to this new reality. According to Vignesh Natarajan, Hi-Tech Segment Leader of Europe, Capgemini, “As generative AI becomes mainstream, the transformation of the data center space will be driven by semiconductor players, who will be the crucial building blocks in the power chain competence.”

For Capgemini, the biggest trends are generative AI-based use cases, AI-based development use cases, AI-based joint design use cases, and foundry solutions. This will be the big wave as demand for consumer electronics continue to grow, albeit slower than during the Covid era. However, demand for electrification, sustainable solutions, and smart cities will soar. Government funding of large-scale projects will provide a floor for demand to produce the next “boom” cycle for semis.

“Our ambition is to support the semiconductor ecosystem companies in scaling up to meet their market opportunity with solutions in Intelligent Industry and Enterprise Management,” says Shiv Tasker, Global Industry Vice President, Semiconductor and Electronics.

Digital Twin offers fast scalability

Digital Twin showcases huge potential in the semiconductor industry through its ability to simulate the entire fab, manufacturing processes, and various use cases and models to improve efficiency and productivity. Companies are looking to transform various aspects of the manufacturing processes. Some of the examples where semiconductor companies are focusing are:

1. Device-scale twin – detailed visualization of a device to reduce cycles of silicon learning, thus reducing waste and resources,
2. Process-scale twin – using simulation to streamline process development thus reducing chemicals and electricity usage,
3. Equipment-scale twin – improving first time right from design through installation by finding issues before physical build or building equipment expertise faster and more effectively.

Digital twin, or the digital omniverse, coupled with Generative AI, provides an incredible opportunity by providing millions of variations to the model, and through reinforcement learning, can change models for best-performing output or model. When implemented well, it can escalate product output at a speed that tests the laws of physics.

OEMs’ growing needs, especially in the automotive

Automotive is a huge driver for many of the changes facing the semiconductor industry. In fact, there was palpable tension at the GSA event between the semiconductor representatives and auto manufacturers. The auto market is hard to resist for any semi-manufacturer due to its size, but the auto manufacturers will never forget the chip shortages of the Covid era and the tremendous damage that did to their business. The evolving supply chain relationships and the trust challenges were the subject of many formal and side-bar discussions.

Sanjiv Agarwal adds: “At Capgemini, we work both sides of the equation, helping chip manufacturers “get to market” and fit into the automotive ecosystem, working with the automotive manufacturers to create their chip strategy, selecting and working with foundries to manufacture those chips, and integrating chips into their designs.” We bring in the promise to create an affordable, ever-smarter, software-driven mobility ecosystem that’s centered around customer needs and protects them from both physical and digital threats.

Geo-political tensions

Geopolitical tensions are a shared concern rather than a trend, but they will have a large impact on the way semiconductor companies work since 60-70 percent of all chips are manufactured in Taiwan or South Korea, which are both relatively volatile. Divergent national approaches exacerbate these concerns. The US, for example, has shifted from outsourcing production to encouraging chip producers to transfer operations stateside. In general, the U.S. CHIPS Act and the European Chips Act will “onshore” more production and drive diversification of production geography.

Brett Bonthron, Executive Vice President and Global High-tech Industry Leader, says, “Through the two Chips Acts, semiconductor companies see that governments understand the criticality of the industry.”

The US Chips Act is a true public-private partnership model and probably the first proactive federal program where the program will be executed along with the states, which would manage permits, labor, land, and other logistics.

Statements of interest are currently being accepted for all direct funding opportunities (USD2B floor, no ceiling), and over 400 have already been received. The US Chips Act envisions success in four areas:

• Leading-edge logic –at least two new large-scale clusters of leading-edge logic fabs wherein US-based engineers will develop the process technologies underlying the next-generation logic chips.
• Memory – US-based fabs will produce high-volume memory chips on economically competitive terms and R&D for next-gen memory technologies critical to supercomputing and other advanced computing applications will be conducted in the US.
• Advanced packaging – the US will be home to multiple advanced packaging facilities and a global leader in commercial-scale advanced packaging technology.
• Current generation and mature – the US will have strategically increased its production capacity for current-gen and mature chips. Chipmakers will also be able to respond more nimbly to supply and demand shocks.

Similarly, the European Chips Act enables the EU to address semiconductor shortages and strengthen Europe’s technological leadership. It will mobilize more than € 43 billion of public and private investments through the Member states through five key areas:

1. Strengthen Europe’s research and technology leadership towards smaller and faster chips,
2. Put in place a framework to increase production capacity to 20% of the global market by 2030,
3. Build and reinforce capacity to innovate in the design, manufacturing, and packaging of advanced chips,
4. Develop an in-depth understanding of the global semiconductor supply chains,
5. Address the skills shortage, attract new talent, and support the emergence of a skilled workforce.

Diversity and workforce development

Diversity, workforce development, and talent were major topics at both events, with the consensus being that inclusion must start at a much earlier age and that more women and minorities must be allowed to enter leadership positions. Considering the existing workforce, many companies are partnering with universities, granting scholarships, and launching apprenticeship programs so that when these fabs are ready, and the existing workforce is close to retirement, the new, more diverse talent will be ready.

Conclusion

The semiconductor industry is in a state of flux. This year’s European Executive Forum by GSA outlined the five major trends – supply chain resiliency, generative AI, geopolitical tensions, the impact of the automotive industry, and sustainability – to emerge from this transition. There are, of course, numerous other factors at play, including issues around inclusion or reducing barriers to entry within the industry. There are also several topics that remained unsaid, for example, shifting relationships between automotive OEMs and tier-one suppliers or the evolution of the semiconductor company vis a vis the value chain. However, at the end of the day, semiconductors are fundamentally about propelling civilization forward and enabling the creation of better societies. As something that is also written our raison d’etre, Capgemini has substantial know-how and near-tech vision to drive this ultimate goal forward.

How generative AI is enabling business results at a scale that dwarfs other possibilities.

The semiconductor industry is being rocked by so many waves, it’s hard to identify the tsunamis. Yet, it still might be calm before the storm: Generative AI has the potential to be the most disruptive – and its impact is just beginning.

The wave of AI adoption is reshaping the operational and strategic landscapes of businesses across the spectrum as companies find new ways to optimize processes, enhance decision-making, and innovate products. In fact, 96% of executives admit Generative AI is being discussed in their boardrooms (CRI Report, 2023). AI is proving especially useful for manufacturing and for predicting trends where multiple, complex, data-heavy externalities are in play. We call this Simulated Futures – seeing different variations of the end result. For the chip industry, it’s about to change everything.

We bring you forward-thinking ideas on how semiconductor companies can transform their operations – and their business models – through the simulation capabilities of Generative AI.

Breaking the Iron Triangle through simulation

Many product developers are familiar with the ‘Iron Triangle’ (also known as the Triple Constraint, or Project Management Triangle): given the goals of speed, cost, and quality, you can choose any two, but rarely can you accomplish all three. Now, Generative AI has broken the Iron Triangle. It is enabling semis to bring new, higher-quality products to market swiftly and at lower cost, shaping optimal chip designs and rapidly prototyping, testing, and validating new designs. Gen AI is also enabling more efficient resource allocation and process optimization, driving down production costs.

Generative AI for Software Engineering helps improve efficiency and quality across the whole software life cycle (from design and coding to documentation, testing, deployment, and operations), accelerates the time to market for new software, and reduces the technical debt of enterprises by facilitating large modernization programs of legacy software. It also enables increased security with a reduced attack surface by automatically identifying bugs or vulnerabilities and proposing adjustments to software development teams.

Let’s take a closer look now at production.

Simulated operations: Generative AI on the factory floor

In the realm of network and factory operations, AI serves as a transformative catalyst, optimizing numerous aspects of the production cycle in chip manufacturing, depending on the objective. Generative AI enables an incredible capacity to plant and process modeling. From simulating the right type of chip design to projecting through model-based engineering, the production line of the future can now be re-envisioned through digital twins and fully predict what is indeed required in the physical world to create the highest-efficiency factory floor for semiconductors. Equipment-scale twin helps improve immediately right from design through installation by finding issues before physical build or building equipment expertise faster and more effectively.

As another example, the Generative AI receives an order to create a more sustainable chip design. Through a device-scale twin, detailed visualization of the device helps to reduce cycles of silicon learning, thus reducing waste and resources in production. Similarly, by using process-scale twin, i.e., using simulation to streamline process development, thus reducing chemicals and electricity usage.

As the factory floor becomes AI-trained, the deployment of AI for predictive maintenance is instrumental in foreseeing potential equipment failures and scheduling timely maintenance – sometimes at line speed. A systems failure that might have required a stopped line, a team of engineers, and hours of research, discussion, and testing, can now be solved with no break in production.

Quality assurance systems benefit similarly, only instead of AI anticipating machine irregularities, it targets the detection and rectification of anomalies improving yield. Once again – speed, cost, and quality are all improved.

Simulated supply chains

Intelligent supply chain operations help ensure the availability of necessary materials and components at the right time and at optimal costs – an essential capability in the global climate today. AI can also ensure standards for ethical labor and sustainability in an industry where both are rising in importance. Predictive Demand and Supply Chain Modeling ensures, for example, the right transportation method, timing, and packaging options for various outcomes and predicts what is required from the receivers and other stakeholders in tandem.

For digital products, where the chip is at the core of the functionality, Generative AI enables better processes from a customer experience. Generative AI for Customer Experience enhances customer experience with 4 dedicated generative AI assistants. It allows hyper-personalized customer experience with a synthetic design assistant, elevates customer self-service with personalized chatbots, augments customer care services with a content and knowledge assistant, and boosts sales teams’ performance with a product & offers knowledge assistant.

Change at the core: Generative AI in documentation, HR, and legal departments

AI will impact every industry – some more than others, few more than chip manufacturing. Let’s start deep inside the industry, at some of the back-end processes.

Product reference documentation can run into thousands of precise and detailed technical information. The capability of Generative AI to generate these documents instantaneously from requirements and functional specifications is a game-changer. The ability to cross-check the specification versus the implementation improves accuracy and spares huge downstream costs of customer support. AI cuts this painstaking task from months to moments.

In chip manufacturing firms, the application of AI technologies streamlines multiple functions within internal processes such as HR and Legal.  Generative AI facilitates efficient talent acquisition and management, helping organizations identify optimal candidates with precise qualifications and manage workforce needs. In the transforming world of semiconductor manufacturers, the ability to rapidly identify, attract, and keep talent can make the difference between a new venture succeeding or being scrapped. Additionally, AI aids in ensuring stringent adherence to legal norms across all operations, identifying and addressing potential legal risks and compliance issues, and shielding a company against legal vulnerabilities. These are all part of how Generative AI can be used in an Enterprise setting.

When these tasks are managed well, leadership is free to turn their attention to more innovative, value-adding tasks, such as product innovation and development. There, too, the impact of Gen AI is beginning to make waves.

Looking ahead

Here’s another way to think about the scale of this change, and it speaks to the fundamental nature of Generative AI. Unlike any tool humans have developed, AI has the ability to make decisions. In each of the categories above, we’re going to see a shift from automated to autonomous. But it won’t be groups of people on one side of the building and blinking lights on the other. Teams are going to be integrated – somehow – with people and Gen AI sharing decision-making. How will tasks be divided? Who will take responsibility for successes? For failures? These are some of the questions which we’ll need to address.

And in the core, there is this one capability on its way that truly tests the bounds of credulity: predicting the future. Or, more accurately, simulating more or less likely futures. Today, we have some models that are developed enough to predict bits and pieces of the near future – weather forecasting, for example. What we’re about to see are full-fledged business simulations that leaders will use to inform their decisions. They’ll adapt in real-time and provide decision-makers with practical, probabilistic outcomes. For a cyclical industry with immense dependency on multiple global trends, the ability to reduce uncertainty will change everything.

The accuracy of these simulations will be dependent on the data available to them. For companies that haven’t yet joined collaborative data networks, it may be a good time to get on board.

Zero or one?

The integration of AI across multiple dimensions of the semiconductor industry will bring transformative advancements. By optimizing internal processes, catalyzing product development, and enhancing operational efficiency, Gen AI equips chip manufacturing organizations to navigate the evolving technological landscape with unmatched agility and foresight, opening the door to possibilities previously unimaginable. Today, some leaders are already beginning to benefit. In ten years, there will be two types of semiconductor manufacturers: those that have incorporated Gen AI into their operations and those that exist only in memory.

The launch of ChatGPT in late 2022 propelled Generative AI to prominence, gaining significant visibility and popularity among both consumers and organizations. Telecom operators have been quick to experiment with this technology, exploring applications that would boost individual productivity and optimize industry-specific processes, like client interactions

Most recently we have seen a significant interest in applying Generative AI to Network Operations, which is an area in which CSPs (Communications Service Providers) have long sought efficiency gains to enable the management of an increasingly complex technology landscape. In this article, we explore how operators can leverage Gen AI to augment the employee experience and automate business processes, all in the name of smarter, faster, more resilient networks.

Network knowledge at your fingertips

Generative AI is a gamechanger when it comes to intelligent document querying and information retrieval. The retrieval augmented generation (RAG) pattern allows the interrogation of text indexes via a semantic representation of a query and has quickly become a standard. Based on retrieved sections of selected documents, Generative AI can easily produce summaries compliant with pre-defined templates.

This application is demonstrated with one of our clients, who is now providing network technicians with a tool that enables them to quickly access a summary of past incidents that have affected a specific node on the broadband access network. Thanks to Generative AI, the new tool can retrieve all incidents relevant to the current investigation and produce a formatted summary of past events – reducing the time for this task.

CSPs are also investigating the use of Generative AI for managing contracts, such as interconnect and roaming contracts, or cell tower lease agreements. Through the help of Gen AI-enabled tools, users can quickly search for specific clauses or ask direct questions, such as “What are the security procedures for accessing this site?” or “What is the average price for site rentals in Madrid?” This removes the need for extensive, complex searches for the most up-to-date and relevant contracts and amendments. It also reduces the risk of error, especially when navigating large, complex document repositories that can span several years and many geographies.

The rise of conversational interfaces

For the past 60 years, most traditional applications have been using either command-line or graphical, menu-based interfaces. While regular users have mastered these tools, occasional users struggle. This is why some customers still prefer calling the contact center instead of using a mobile app!

Generative AI gives a new dimension to user interfaces, moving from predefined, rigid dialogs, to more free-flowing and intuitive conversations. This is relevant for some of the interfaces used when operating networks, where the user experience can be much improved.

For instance, we are currently defining a proof-of-concept with one of our CSP clients to support field technicians who perform interventions at customer homes and/or network points of interest. These technicians frequently need specialized help from team leads or colleagues while in the field; if this help cannot be provided on demand, then the service agent may need to schedule a follow up intervention.

Generative AI allows the development of a conversational bot that enables the technician to get the information they need about the specific location/services and technologies that will enable them to successfully troubleshoot or deploy services. A voice bot or a chat bot makes the job of the technician easier and quicker, allowing for faster issue resolution and avoiding repeat and costly truck rolls.

These service tools can also be combined with augmented reality (AR) applications, such as using a mobile phone to scan physical devices and generate relevant information. A variety of new interfaces that enable this type of support is made possible by the emergence of multimodal models such as Google’s Gemini, OpenAI’s GPT-4 and Mistral AI’s Mistral 7B.

An additional application focuses on network configuration. Specific intents, like boosting radio capacity at a stadium ahead of a major event, are set up through dedicated interfaces that require expertise on network management applications. By employing a conversational interface, network engineers can effortlessly grasp available capacity and make configurations for upcoming activities through friendly conversations with an agent. This conversational agent doubles as an advisor, drawing insights from historical activities and the present network status.

Autonomous Networks monitoring with a human touch

Moving to a higher level of network automation is critical for network operators. In fact, this is the key to improving service quality within a more complex technological environment without adding additional staff. AI is key to moving from human-managed networks, supported by insights from data, to AI-managed networks. Generative AI can thus complement other AI models, such as anomaly detection and classification.

While the industry has settled on the term “Autonomous Networks,” the goal is not complete autonomy. Configuration of intent is essential, and ongoing network monitoring for compliance is crucial. Even with a significant level of automation, human oversight remains imperative to ensure safety and maintain the quality of service.

Generative AI can produce a human readable summary of the status and activity in the network, allowing human agents to understand if and how the intent is satisfied. Even when operating networks with a high level of automation, human agents must be able to investigate, ask specific questions and get replies.

In the same way, an Autonomous Network’s reaction to an alarm or an anomaly must be defined in advance by a network engineer. On older generations of solutions, scripts must be developed and tested, which requires strong expertise. With Generative AI, natural language could be used to define responses to alarms, going as far as to extract appropriate remediation procedures from process documents. This approach allows human experts to review and make necessary adjustments.

Leading the way in an AI revolution

Like traditional AI, there are many use cases for Generative AI in the Telecom industry. In addition to individual agent productivity tools, Generative AI can be used to refine and streamline operational processes to better manage networks.

At Capgemini, we are now experimenting with leading CSPs on how to augment or automate existing workflows through AI technologies, helping them create a smarter, faster, more resilient network.

TelcoInsights is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders.

Building a persistent, open and interoperable #virtualworld 

 Through gamified experiences to reach broad audiences remains on top of brand’s agenda.
The Walt Disney Company investment of $1.5B in Epic Games is an iconic recent deal in that perspective.

While being a world-class games experience, Disney’s intend is to leverage Fortnite as the new persistent universe to offer a multitude of opportunities for #consumers to play, watch, shop and engage with content, characters and stories from Disney, Pixar, Marvel, Star Wars, Avatar and more.

That’s definitively a significant move toward bringing more 3D and immersive experiences in the entertainment industry.