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The McKinsey report highlights #ten #transformative #technology trends shaping the mobility sector. These include advanced connectivity, applied AI, cloud and edge computing, generative AI, immersive-reality tech, industrialized machine learning, next-generation software development, quantum tech, trust architectures, and Web3. These technologies are enhancing various aspects of the industry, such as #engineering, #procurement, #manufacturing, #marketing, and #life #cycle #services. Applied #AI, in particular, is driving significant innovation and investment. For more details, you can read the full report: https://lnkd.in/ddAEUXsH

Some key points from this interesting article: - Annual report highlights ten cross-industry trends, including applied AI, cloud computing, and Web3. - 20% of 3,500 industrial companies analyzed engage in innovations related to these trends, especially applied AI. - Top tech trends include advanced connectivity, generative AI, quantum computing, and Web3. - Applied AI leads innovation, impacting engineering, procurement, manufacturing, marketing, and customer services. - Challenges in applied AI implementation require strategic, organizational, talent, and tech adaptations for successful transformation in mobility sector. - Technology and innovation management are key factors to evolve in the mobility sector. #innovation #technology #future #mobility

Technological innovation is transforming every aspect of the mobility sector, and more companies are beginning to investigate disruptive technology. A McKinsey analysis identifies the trends to watch. #futuremobility #emobility #EVs #connectivity #AI #machinelearning #software

Ten transformative technologies in mobility. The ten technology advances from the McKinsey Technology Trends Outlook 2023 report1 most relevant to mobility are as follows: 1. advanced connectivity, which refers to wireless and low-power networks, fifth- and sixth-generation cellular networks, Wi-Fi 6 and 7, low-Earth-orbit satellites, and other tech that support a host of digital solutions; it can improve productivity across industries 2. applied AI, which involves using models trained in machine learning to solve classification, prediction, and control problems; the models can help add or augment capabilities and offerings, automate activities, and make better decisions 3. cloud and edge computing, which allow distribution of workloads across locations, such as hyperscale and remote data centers, regional centers, and local nodes; these technologies can help improve adherence to data sovereignty regulations, autonomy over data, data transfer costs, latency, and security 4. generative AI, which can automate, augment, and accelerate work by tapping into unstructured, mixed-modality data sets to enable the creation of new content in various forms, such as code, text, and video 5. immersive-reality tech, which uses sensing tech and spatial computing to help users see the world through augmented, mixed, and virtual reality 6. industrialized machine learning, which refers to an ecosystem of software and hardware solutions that help accelerate and derisk the development, deployment, and maintenance of machine learning solutions 7. next-generation software development, which enables modern code deployment pipelines and automated code generation, testing, refactoring, and translation 8. quantum tech, which is still in relatively early development but could provide an exponential increase in computational performance for certain problems and transform communication networks by making them more secure 9. trust architectures and digital identity tools, which allow organizations to build and scale their data and digital-enabled products and services, as well as maintain the trust of their stakeholders 10. Web3, which is a new type of decentralized internet that has open standards and protocols and simultaneously protects digital-ownership rights; its potential applications include decentralized mobility platforms on which individual users can book travel with various providers for a single trip https://lnkd.in/dvyHEisu

🚀 Exploring the Future of Autonomous Networks 🌐 As the technology landscape evolves, the rise of autonomous networks is transforming the way we approach connectivity and data management. According to the latest report by Market Research Future, the autonomous networks market is set for significant growth, driven by advancements in AI, machine learning, and automation technologies. These networks promise to revolutionize the industry by enhancing efficiency, reducing operational costs, and improving scalability. The integration of AI allows for self-optimizing and self-healing networks, which can adapt to changing conditions in real-time. This means fewer disruptions, better performance, and a more resilient infrastructure. Key players in the market are pushing the boundaries with innovative solutions that are not only enhancing network management but also paving the way for smarter, more adaptive systems. As we look ahead, the potential for autonomous networks to reshape various sectors, from telecommunications to data centers, is truly exciting. For those interested in staying ahead of the curve and understanding the full scope of this emerging market, the detailed analysis and insights provided in the Market Research Future® (MRFR) report are a must-read. Dive into the future of networking and discover how autonomous networks are set to redefine the industry. 📈🔍 https://lnkd.in/dBRACywE #AutonomousNetworks #AI #MachineLearning #Innovation #TechTrends #MarketResearch #FutureOfNetworking

Among the ten main #technologytrends that are currently shaping the mobility sector, applied #AI is far the most relevant, with up to 64% of companies working in areas related to its development. According to McKinsey & Company, AI popularity is due to its capability to be adopted for different purposes and it strongly impacts the whole mobility supply chain thanks to its versatility. 👨💻 Engineering and R&D 👉 Applied AI is used to improve simulations, identifying weaknesses in current models, and generating numerous scenarios for testing. 💰 Procurement 👉 AI is utilized to assess environmental, social, and governance risks related to suppliers or partners, improving compliance and sustainability practices, which are increasingly important to consumers. ⚙️ Manufacturing 👉 AI can enhance quality control during manufacturing processes, pursuing standards with improved accuracy and efficiency. 📣 Marketing and Sales 👉 Applied AI helps companies identify at-risk customers and implement strategies to enhance customer experience by personalizing their journeys. ♥️ Life Cycle Services 👉 OEMs leverage applied AI to analyze consumer preferences and provide the right infotainment experience or navigation system. Read more here: https://lnkd.in/dyXaZ7ew

🏛🚀 Governments are looking at technological innovations to support the transformation of the #mobility sector, and more private sector companies are beginning to investigate disruptive technology that can support as well. Here are the ten technology advances from the McKinsey & Company Technology Trends Outlook 2023 report most relevant to mobility: ➡️ Advanced connectivity, which refers to wireless and low-power networks, fifth- and sixth-generation cellular networks, Wi-Fi 6 and 7, low-Earth-orbit satellites, and other tech that support a host of digital solutions; it can improve productivity across industries ➡️ Applied AI, which involves using models trained in machine learning to solve classification, prediction, and control problems; the models can help add or augment capabilities and offerings, automate activities, and make better decisions ➡️ Cloud and edge computing, which allow distribution of workloads across locations, such as hyperscale and remote data centers, regional centers, and local nodes; these technologies can help improve adherence to data sovereignty regulations, autonomy over data, data transfer costs, latency, and security ➡️ Generative AI, which can automate, augment, and accelerate work by tapping into unstructured, mixed-modality data sets to enable the creation of new content in various forms, such as code, text, and video ➡️ Immersive-reality tech, which uses sensing tech and spatial computing to help users see the world through augmented, mixed, and virtual reality ➡️ Industrialized machine learning, which refers to an ecosystem of software and hardware solutions that help accelerate and derisk the development, deployment, and maintenance of machine learning solutions ➡️ Next-generation software development, which enables modern code deployment pipelines and automated code generation, testing, refactoring, and translation ➡️ Quantum tech, which is still in relatively early development but could provide an exponential increase in computational performance for certain problems and transform communication networks by making them more secure ➡️ Trust architectures and digital identity tools, which allow organizations to build and scale their data and digital-enabled products and services, as well as maintain the trust of their stakeholders ➡️ Web3, which is a new type of decentralized internet that has open standards and protocols and simultaneously protects digital-ownership rights; its potential applications include decentralized mobility platforms on which individual users can book travel with various providers for a single trip. https://lnkd.in/ehfiUzRN

The global auto industry is undergoing a significant transformation driven by artificial intelligence (AI), as highlighted by industry expert Zhang Yongwei from China EV100. He noted that the shift towards electrification is reshaping the automotive landscape, with intelligent technologies further revolutionizing it. Zhang stressed that AI-driven intelligence is becoming the key competitive edge, rendering traditional advantages obsolete. A major challenge for car manufacturers is building a robust computing power infrastructure. Zhang pointed out that in the AI era, production capacity is becoming less critical than developing competencies in computing power, chips, and algorithms. Companies that fall behind in these areas face substantial risks, particularly as autonomous driving demands a baseline computing capacity of 1 EFLOPS and beyond. To stay competitive, Zhang proposed that carmakers create collaborative mechanisms for data and computing power sharing. He emphasized the limitations of individual automakers’ data for effective software training, highlighting the need for extensive data collection. Establishing a market-based data-sharing framework and fostering collaboration are essential steps for enhancing global competitiveness in China's auto industry. #AI #China https://lnkd.in/gMaYSvsC

When asked by Bloomberg News about a potential shift in the timeline for fully electric vehicles, Bosch Chairman Stefan Hartung said, “We try to serve everybody who makes cars, and we’re monitoring carefully what happens, because in the end the consumer decides… It’s a thing that’s moving, and in the end that’s very influenced by the markets of consumers.” See more of the discussion related to a number of global issues and news like Bosch’s new collaboration with Microsoft on #generativeAI for #automated vehicles. #mobility #BEV #EV #AI

When asked by Bloomberg News about a potential shift in the timeline for fully electric vehicles, Bosch Chairman Stefan Hartung said, “We try to serve everybody who makes cars, and we’re monitoring carefully what happens, because in the end the consumer decides… It’s a thing that’s moving, and in the end that’s very influenced by the markets of consumers.” See more of the discussion related to a number of global issues and news like Bosch’s new collaboration with Microsoft on #generativeAI for #automated vehicles. #mobility #BEV #EV #AI