Tech CEOs travel a lotthats not news. They fly to meet investors on both coasts, and to pitch or coordinate with strategic partners around the world. For Esipov, it was growth that led him to move eastward, since more frequent in-person meetings with East Coast partners like American Express made the jet commute less tolerable.

A leader whos here just a fraction of the time can still drive local job growth, however. On the Tech:NYC job board, there were about 5,300 positions open with tech-industry employers who are members. A search for remote yields about 600 positions, but many more express flexibility. The board includes all jobs that can be done in New York, regardless of the headquarters address or founders location. Hugging Face, a hub for open-source AI tools, maintains an office in Paris and in Downtown Brooklyn, and its founder reportedly dwells in Florida. We support our employees wherever they are, one of its job listings reads.

Even before Esipov made it official, about half of his employees were based in New York City, he said. The company leases 5,000 square feet in the Flatiron District for the group, which includes members of every company teamcore administrative, go-to-market, legal, compliance and product engineering. It has long been in both the enterprise and consumer tech playbooks to post sales or marketing teams in New York City, while keeping core development or product elsewhere. That was the script Esipov followed, keeping NovaCredits go-to-market team here near banks and credit card firms. Then in 2021 and 2022, three Bay Area-based engineers asked if they could make the cross-country move to New York. Its become the headquarters of the company, he said. A San Francisco office with about one-quarter of employees will remain open.

From an economic development perspective, provided they put people on the ground, including the tech team, the physical headquarters of a company is not its most relevant feature, said Maria Gotsch, president and CEO of the Partnership Fund for New York City. In fact, Gotsch sees the question the other way: The growing talent pool attracts companies to come work here, since they know they will be able to hire. Anecdotally, leaders say the San Francisco-to-New York City move was a common journey for the countrys engineers during the great office untethering of 2020 to 2022.

Post-Covid we have more talent than ever, said Jonathan Lehr, partner at Work-Bench, which invests in enterprise tech firms. If we think about it company-wise, its a net positive. With talent, while not every founder or chief marketing officer is here, there is more talent than there was.

Still, the size of the tech pool is not growing as fast as in other places. Both Boston and the San Francisco Bay Area had double the tech talent growth between 2017 and 2022 as New York City, according to the CBRE data, and Dallas-Fort Worth and Seattle had increases of 28% and 29%, respectively. Austin, Salt Lake City and Phoenix are smaller markets that boasted even more growth.

The writer E.B. White said that newcomers give New York City its passion. He did not live to witness the enthusiasm of the tech nomads and recent arrivals.

Events like the packed Arthur panel are becoming more common, participants in the tech scene say. Lehr said he noticed that newcomers eager to get out into the city and build their networks had been driving attendance at events like his firms Enterprise Tech Meet-up over the last year and a half. New Yorks key place on the international circuit guarantees that not everyones here all of the time, but when they are here, they have business and socializing to do.

Morgan Barrett, who works in the tech group at law firm Lowenstein Sandler, said he has recently been running into industry acquaintances from Texas seeking a respite from the above-100F temperatures there on the streets of SoHo.

Because its the summer, and its so hot in Austin, it feels like half of Austin is here, Barrett said.

He discovered the irrepressible nature of the citys tech scene when he and 10 friends in the industry got together for a breakfast catch-up at Balthazar. We had such a good time, he said, we decided to do it again in a few weeks. The next time the group met, 30 tech types came, then 60. Eventually, he and co-host Ariel Purnsrian started hosting Breakfast Clubs at event spaces, and they were still packed.

I always try to host in SoHo, he said, because its like the center of the universe for startups. The density of New York Cityand the even closer geographic ties of the tech neighborhood in Manhattan below 30th Streetmeans most people can stop in easily to the meet-up on their way somewhere else.

Inquiries for events and dinners from tech-sector clients have grown exponentially over the last six to eight months at SoHo French restaurant La Mercerie, said Dana Rizzo, its director of hospitality. There are client dinners, board meetings and press events, she said: With in-person time less frequent, they want to ensure moments of gathering are memorable and elevated.

The restaurant has a 28-seat table in its private dining room that appeals to companies looking to carve out time to be deeply connective and creative, Rizzo said. Clients have included the big players like Google and Meta, as well as out-of-town companies, like Uber, which Rizzo said hosted an event with its San Francisco-based CEO Dara Khosrowshahi in attendance. Like Barrett, she said the SoHo location makes for an ideal central hub in a remote work world.

The party does not extend to the office. Tech companies with distributed workforces need fewer square feet for desks and conference rooms. Several larger tech firms have recently downsized or called off expansions, and Newmark reports that Meta, Verizon and Publicis are rumored to be returning blocks of office space to the market soon.

The connection between remote work and a slower demand for real estate, even for companies that are committed to New York, is clear.

It is true that technology companies, large and small, are cutting back on their space needs, Fred Wilson, partner at Union Square Ventures, wrote on his blog in July. But that is more a reflection of the era of remote and hybrid workforces than anything else. That, he pointed out, does not mean that tech is no longer a growth industry here.

The story of Dayforward is an example. Launched in 2019, Dayforward sells accessible life insurance plans online to young families. At the helm is Aaron Shapiro, one of the founders of the digital advertising agency Huge, a company whose presence in Dumbo helped define the neighborhood as a hub for creative work in the 2000s.

Shapiro lives in Manhattan but the company is fully distributed.

A lot happened accidentally, he told Crains. He intended for Dayforward to sign a lease in 2020, but with the pandemic, we thought wed wait a little, he said. By the time everything opened up, the team was comfortable working remotely, and several key hires were living elsewhere. For example, Dayforward found its head of underwriting outside of Cincinnati. The 25% of the team based in New York still meet several times a week, but instead of leasing their own office, they go to a space at Juxtapose, the NoHo investor, thats reserved for portfolio companies. Shapiro does not rule out signing a lease for Dayforward some day.

To be sure, for every real estate-agnostic start-up, there are tech companies in physical growth mode. VTS, which sells software for commercial and residential building managers, just took 34,325 square feet at 3 Bryant Park, in a sublease from Salesforce, which will double its local headquarters. Celonis, a process-mining firm started in Germany, leased one floor at 1 World Trade Center in early 2020 and leased a second floor in the spring of 2022. Datadog, a cloud security firm that went public in 2021, leases over 150,000 square feet at 620 8th Ave.; its office has become a different kind of anchor than the meet-ups or the AI panels, said John Dickerson, chief scientist of Arthur.

We are only just starting to get the big players in the space, he said, calling Datadog the shining jewel of the New York scene because of its firm presence and deep integration with scores of other tech companies who use its cloud services and may need to meet its representatives in personsimilar to how the citys finance industry attracts fintech firms like NovaCredit to be on the ground in New York.

In addition to smaller and distributed firms, he said, We need that style of company.

Read this article:

New York's tech scene is back, but not in trophy headquarters - Crain's New York Business

By Laurenz Dallinger, Application Engineer at Cedalo.

The number of IoT devices and the data they generate is constantly increasing. Organizations use IoT data to obtain useful information, optimize their work, and increase profits. Therefore, it is necessary to use technologies that can process and analyze IoT data effectively.

The Internet of Things (IoT) refers to a network of devices and sensors capable of collecting and exchanging data over a network. After receiving the data, the device processes it and can perform certain actions such as sending notifications, automatically configuring devices, etc.

Key Components of IoT:

Big data refers to the extensive and complex datasets derived from diverse sources that are challenging to process using traditional methods. Nevertheless, businesses need to analyze big data to identify trends and patterns in user behavior to offer in-demand products and services. The trend is towards on-demand services. You no longer buy multiple devices that you then have to connect and you save money. You buy the result, preferably without having to buy the devices (e.g., a cloud application).

Big data and IoT are closely interconnected and dependent on each other. IoT devices constantly generate a huge volume of various data. This includes structured, semi-structured, and unstructured data obtained from various types of sensors and devices, which increase the variety and volume of big data.

The rapid development of the Internet of Things has contributed to the development of big data technologies, which are widely used to analyze IoT data.

To obtain useful information and identify patterns, companies apply big data analytics methods and tools to IoT data. This allows them to gain valuable information about system performance, customer behavior, predictive maintenance, anomaly detection, etc., and make decisions based on that information.

Big data analytics techniques and tools are used to process and analyze huge amounts of IoT data. Tools like Apache Spark, Apache Storm, and Flink allow companies to process the high-speed streaming data generated by IoT devices. Machine learning, data mining, and predictive modeling are used to make decisions based on IoT data.

Big data technologies enable the integration of IoT data with other data obtained from other sources. For example, customer, sales, and supply chain data. This allows organizations to get a complete picture of their activities, customers, or processes to provide a better understanding of complex systems and processes.

Big data technologies such as distributed file systems and cloud storage platforms are used to store and manage IoT data. They are fault-tolerant, support replication, and allow organizations to reliably and efficiently store large amounts of data.

Using the IoT and Big Data together helps companies effectively analyze data, identify trends, and make decisions. Lets list the main advantages of using big data in IoT for business.

Big data analytics in IoT allows companies to better understand customer behavior and market trends. This allows them to make the right strategic decisions and conduct marketing campaigns to expand the business.

Thanks to IoT devices, businesses can gain detailed information about their operations, processes, and assets. By analyzing this data, they can identify inefficient processes, reduce downtime, and reduce costs. With the help of special sensors, administrators can also detect equipment malfunctions and schedule device maintenance to avoid breakdowns and unplanned downtime.

IoT devices provide valuable information about product usage and customer feedback. This information is used to develop new features, optimize design, and adapt the product to customer needs.

A variety of IoT sensors enable real-time supply chain tracking. E.g., they provide information about the amount of fuel, the location of the car, the delivery route, etc. Using this information, companies can identify bottlenecks, improve inventory management, and optimize overall supply chain efficiency.

Although big data in IoT plays an important role and brings many benefits for business, it is also important to consider some challenges and risks of its use. Lets look at some of the problems that may arise when implementing this technology.

IoT devices are vulnerable to cyber threats, and the data they collect may contain sensitive information. Therefore, it is an important task to protect the data of IoT devices during transmission, processing, and storage. Keep in mind that every security measurement adds to the potential risk of overhead.

Every day, the amount of data generated by the Internet of Things is increasing. Storing and processing this amount of data requires significant investment in scalable data storage, processing, and analytics infrastructure.

Using MQTT is particularly resource-efficient for storing and processing large amounts of data. So, it is important to choose an MQTT broker, which plays a critical role in ensuring efficient communication and data exchange between devices, facilitating the seamless flow of big data in IoT.

IoT devices continuously generate and transmit data in real-time. Real-time streaming data processing and analysis requires the latest technologies to manage high-speed data flow and obtain useful information on time.

Visualization of a huge amount of data of many different formats and types in real-time is a rather difficult task. Therefore, companies have to implement new data visualization capabilities to better understand the data.

Big data in IoT is useful for various business sectors. Lets look at some examples.

Big data analytics in IoT allows banks to identify and resolve problems faced by users. In addition, special algorithms are used for analyzing customer behavior and detecting fraud.

Connected health monitors make it easy to remotely monitor various patient health indicators. Special machine learning algorithms can detect negative trends and inform the doctor on time, saving patients lives and health.

IoT devices and sensors in retail stores can generate vast amounts of data on customer behavior, footfall, and inventory levels. Big data analytics in IoT enables retailers to gain insights into consumer preferences, optimize store layouts (e.g., heatmaps), personalize marketing campaigns, and improve the overall shopping experience.

IoT devices embedded in assets or vehicles provide real-time location data. Combining this data with IoT Big Data analytics enables effective tracking, inventory management, and optimization of logistics operations.

Big Data analytics in IIoT optimizes manufacturing processes, improves supply chain management, and enhances product quality. By analyzing data from sensors, machinery, and production lines, organizations can identify bottlenecks and optimize workflows. Using AI algorithms, IIoT systems can make automatic decisions without human intervention.

Big data plays an important role in the IoT system. The use of big data in IoT enables the efficient storage, processing, and analysis of huge volumes of data generated by IoT devices. It allows companies to easily obtain important information about internal processes, improve marketing activities and supply chain management processes, effectively analyze user needs, and implement new technologies.

However, working with a huge amount of real-time IoT data requires solving some significant problems, such as using a scalable and reliable infrastructure, ensuring data and device security and privacy, and implementing advanced analytics and data management techniques.

See the original post here:

The Role Of Big Data In IoT in 2023 - IoT Business News

ReportLinker

The need of Composite AI in Security & surveillance is growing as organizations may increase situational awareness, strengthen threat detection capabilities, automate monitoring procedures, and allow more effective and proactive security actions by using composite AI in security and surveillance.

New York, July 31, 2023 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Composite AI Market Size, Share & Industry Trends Analysis Report By Technique, By Vertical, By Application, By Offering, By Regional Outlook and Forecast, 2023 - 2030" - https://www.reportlinker.com/p06481214/?utm_source=GNW Therefore, Security & Surveillance acquired $83.4 million revenue in the market in 2022. Composite artificial intelligence (AI) may significantly advance numerous elements of safety monitoring, danger identification, and response. The market is predicted to expand due to the increased requirement for data protection since many firms have recently experienced severe security breaches and data breaches.

The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In June, 2023, Google LLC signed a partnership with Sysdig to integrate its generative artificial intelligence (AI) features with Sysdigs cloud security platform. The partnership would provide the joint customers of the two companies with secure cloud-based automation solutions that would drive better productivity. Additionally, In June, 2023, Salesforce extended its partnership with Google Cloud to develop solutions for AI-powered customer experience solutions. The partnership would allow the two companies to provide their clients with personalized customer experience solutions.

Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Market. In June, 2023, Microsoft Corporation extended its partnership with HCLTech, an Indian IT company, to provide generative AI-powered business transformation solutions to enterprises. The partnership would allow Microsoft to serve its customers in a better way by providing them with solutions for better decision-making through the use of AI-powered operational insights. Companies such as Intel Corporation, NVIDIA Corporation, SAP SE are some of the key innovators in the Market.

Market Growth Factors

Real-time decision-making via integration with edge computing and IoT

Edge computing adoption is being fueled by the ubiquity of Internet of Things (IoT) devices and the need for real-time decision-making. By processing and analyzing data locally, composite AI systems linked with edge devices may lower latency and enable quicker insights and answers. This integration makes the deployment of composite AI solutions in edge computing settings possible. Intelligent applications and 5G/6G Internet of Things (IoT) networks rely heavily on edge computing. The IoT ecosystem creates enormous, heterogeneous, extremely noisy, spatiotemporal-correlated, real-time data streams that need intelligent learning for effective data analysis and insight extraction. These devices include sensors, mobiles, and memory units.

AI applications are becoming more intricate to improve performance and accuracy

The complexity of AI applications is increasing, necessitating the fusion of many AI models and technologies. Organizations are now dealing with the fact that training a large neural network using ML only sometimes scales to address challenges of growing complexity. In conclusion, the increasing complexity of AI applications will support the rise of the market for extraordinary performance and accuracy.

Market Restraining Factors

Issues with data security and privacy

Due to a lack of confidence in AI technology or incomplete awareness of its potential and constraints, some businesses could hesitate to employ composite AI solutions. Implementation challenges may also arise from worries about data security, privacy, and possible biases in AI algorithms. Data leaks and illegal access to private information are the key privacy issues with AI. Thus, over the projection period, worries about data security and privacy may restrain the development of the market.

Offering Outlook

Based on offering, the market is segmented into hardware, software, and services. The software segment held the highest revenue share in the market in 2022. Composite AI software often provides a platform or framework that enables the smooth integration and orchestration of several AI models or algorithms. It allows users to make better judgments by using the many capabilities of these AI components to address complex problems. Organizations may create sophisticated AI applications that process and analyze various data kinds, comprehend complicated patterns, provide insights, anticipate outcomes, and carry out activities that call for a mix of AI approaches using composite AI software.

Software Outlook

Under software, the market is bifurcated into AI Development Platforms & Tools, ML Frameworks, AI Middleware, and other software. In 2022, the AI Development Platforms & Tools segment registered the largest revenue share in the market. Businesses and developers may now design and modify AI solutions more quickly and simply because of the emergence of low-cost and accessible AI tools and platforms like TensorFlow, PyTorch, and IBM Watson. AI systems may speed up software development procedures like code compilation and quality assurance, which can cut down on the time and expense involved in developing software.

Technique Outlook

Based on technique, the market is categorized into proactive mechanisms, data processing, pattern recognition, conditioned monitoring, data mining & machine learning, and others. In 2022, the data mining & machine learning segment projected a prominent revenue share in the market. Techniques for data mining and machine learning are essential for commercializing Composite AI solutions. Composite AI systems that combine data mining and machine learning methods allow the system to analyze enormous volumes of data, find patterns, anticipate the future, and improve decision-making. Using datas power allows composite AI systems to offer intelligent, adaptive, and context-aware capabilities across a range of domains and applications.

Application Outlook

On the basis of application, the market is divided into product design & development, quality control, predictive maintenance, security & surveillance, customer service, and others. In 2022, the predictive maintenance segment registered a significant revenue share in the market. In predictive maintenance, which seeks to anticipate equipment breakdowns or maintenance requirements, composite AI may be beneficial. It provides better asset management methods, data-driven decision-making, and accurate projections.

Vertical Outlook

Based on the vertical, the market is segmented into BFSI, retail & eCommerce, manufacturing, energy & utilities, transportation & logistics, healthcare & life sciences, media & entertainment, government & military, telecom, and others. The BFSI segment registered the maximum revenue share in the market in 2022. In the BFSI industry, composite AI solutions are used for various purposes, including compliance and regulatory reporting, risk assessment and management, customer service, credit scoring, and fraud detection and prevention.

Regional Outlook

Region wise, the market is analysed across North America, Europe, Asia Pacific and LAMEA. The North America region led the market with highest revenue share in 2022. North America is a global leader in implementing and expanding composite AI solutions. The existence of cutting-edge AI technology businesses, strong R&D skills, and a developed market ecosystem all play a role in the regions fast rise of composite AI solutions. Composite AI is being used by major sectors, including healthcare, retail, BFSI, and manufacturing, to promote innovation, improve consumer experiences, and increase operational effectiveness.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, SAS Institute, Inc., Microsoft Corporation, Google LLC (Alphabet Inc.), Salesforce, Inc., Amazon Web Services, Inc. (Amazon.com, Inc.), NVIDIA Corporation, Intel Corporation, SAP SE, and Squirro AG

Recent Strategies Deployed in Composite AI Market

Partnerships, Collaborations, and Agreements:

Jun-2023: Microsoft Corporation extended its partnership with HCLTech, an Indian IT company, to provide generative AI-powered business transformation solutions to enterprises. The partnership would allow Microsoft to serve its customers in a better way by providing them with solutions for better decision-making through the use of AI-powered operational insights.

Jun-2023: Google LLC signed a partnership with Sysdig, a cloud-based security solutions provider, to integrate its generative artificial intelligence (AI) features with Sysdigs cloud security platform. The partnership would provide the joint customers of the two companies with secure cloud-based automation solutions that would drive better productivity.

Jun-2023: Google LLC partnered with Twilio Inc., a customer engagement platform, to integrate Twilios customer engagement offerings with Google Clouds generative artificial intelligence (AI). The partnership would allow Google to provide its clients with AI-powered customer engagement tools that would drive their business growth.

Jun-2023: Salesforce extended its partnership with Google Cloud, a cloud platform offered by Google, to develop solutions for AI-powered customer experience solutions. The partnership would allow the two companies to provide their clients with personalized customer experience solutions.

Jun-2023: Amazon Web Services Inc. announced an extended partnership with Lacework Inc., a cloud security services provider, to integrate Amazon GuardDuty with Laceworks portfolio. The partnership would allow Amazon Web Services to provide its customers with anomaly detection solutions that would drive better security outcomes.

May-2023: Microsoft Corporation signed a partnership with NVIDIA, an American technology company, to provide solutions for Ai-powered business transformation. The partnership features a combination of Microsofts Azure Machine Learning with NVIDIA AI Enterprise software that would allow the joint customers of the two companies to gain access to a protected business platform.

May-2023: NVIDIA Corporation partnered with ServiceNow, a software company based in the US, to provide workflow automation capabilities. The partnership would allow NVIDIA to serve its IT customers in a better way by providing them with generative AI solutions.

May-2023: SAP SE signed a partnership with Google Cloud, a cloud platform offered by Google, to integrate Google Clouds Data and Analytics Technology with SAPs portfolio. The partnership enables SAP to serve its customers in a better way by providing them with a portfolio that would simplify the data landscapes, perform advanced analysis and provide quick access to important business-related data.

Feb-2023: IBM Corporation announced a partnership with NASA, an American civil space agency, to develop AI-powered solutions for contextual analysis. The solutions developed by IBM in the course of this partnership would be useful to several other organizations including NASA, thereby, allowing IBM to strengthen its market position.

Feb-2023: Amazon Web Services, Inc. partnered with Baker Hughes, an energy technology solutions provider, to develop solutions for automated field production. The partnership would allow the joint customers of the two companies to increase their productivity by providing them with solutions for proactive management and automated field production.

Dec-2022: SAS Institute, Inc. entered into a partnership with Basserah, a data solutions provider based in Saudi Arabia, to provide solutions for data analytics to Saudi enterprises. The partnership allows SAS to expand its data analytics offerings and would allow the company to enhance its market share in the Saudi region.

Nov-2022: Squirro AG teamed up with Semantic Web Company, a knowledge graph provider, to launch Composite AI solutions. The collaboration would provide the joint customers of the two companies with better decision-making solutions by combining Squirros Insight Engine with Semantic Web Companys knowledge graph technology.

Sep-2022: Salesforce, Inc. entered into a partnership with AWS, an IT services provider based in the United States, to integrate the Salesforce Platform with Amazon SageMaker. The partnership allows the two companies to provide their customers with solutions to build business-specific AI models.

May-2021: SAP extended its partnership with Team Liquid, a leading worldwide professional esports organization. By using the power of SAP HANA, predictive & machine learning functionalities, and the SAP Business Technology Platform, Team Liquid would more rapidly and efficiently evaluate opponents approaches.

Jan-2021: SAP SE extended its partnership with Microsoft, an American multinational technology company. This partnership aimed to combine Microsoft Teams with SAPs intelligent portfolio of solutions and boost the adoption of SAP S/4HANA on Microsoft Azure. In addition, the companies also focus on simplifying and streamlining users journeys to the cloud.

Product Launches and Product Expansions:

May-2023: IBM Corporation announced the launch of the Watsonx Platform, an AI-powered solution that provided end-to-end workflow solutions. The Watsonx Platform features three product sets namely, IBM watsonx.ai, an AI-powered solution that facilitates generative AI and machine learning deployment, IBM Watsonx.data, a data store used for AI workloads and governed data applications and IBM Watsonx.governance, an AI-workflow solution.

Apr-2023: Amazon Web Services announced the launch of Bedrock, a service used for developing generative AI applications. The Bedrock features accessibility to Amazons Titan language models and customizability for data-specific needs. Furthermore, Bedrock can also be used with third-party language models.

Mar-2023: Salesforce, Inc. announced the launch of Einstein GPT, a CRM technology solution that utilizes AI to provide AI-created content at a hyper-scale. The Einstein GPT is compatible with Salesforce Data Cloud and features OpenAIs chatbot technology.

Mar-2023: NVIDIA Corporation introduced the NVIDIA NeMo framework and NVIDIA Picasso service. The NVIDIA NeMo framework is used for deploying large language models (LLMs) and facilitating enterprise hyper-personalization. The NeMo framework features NVIDIA DGX Cloud. The NVIDIA Picasso service is purely cloud-based and is used to create and deploy generative AI-based visuals. The Picasso service features Edify foundation for generating high-definition videos and images.

Mar-2023: SAP SE unveiled SAP Datasphere, a cloud-based data warehouse used for data integration and data cataloguing applications. The Datasphere features the SAP Business Technology Platform (BTP) used for data protection and governance purposes. Furthermore, Datasphere can be used with third-party technologies to overcome errors in data management.

Feb-2023: Google LLC unveiled Bard, a chatbot that leverages the use of AI for better results. Bard features the use of Googles Lambda language model that would facilitate humanlike interaction between the user and the chatbot.

Scope of the Study

Market Segments covered in the Report:

By Technique

Data Processing

Data Mining & Machine Learning

Conditioned Monitoring

Pattern Recognition

Proactive Mechanism & Others

By Vertical

BFSI

Telecommunications

Retail & eCommerce

Healthcare & Lifesciences

Media & Entertainment

Energy & Power

Transportation & Logistics

Government & Defense

Manufacturing

Others

By Application

Product Design & Development

Quality Control

Predictive Maintenance

Security & Surveillance

Customer Service & Others

By Offering

Hardware

o Processors

o Memory Units

o Networks

o Others

Software

o AI Development Platforms & Tools

o ML Framework

o AI Middleware & Others

Services

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

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The Global Composite AI Market size is expected to reach $8.5 billion by 2030, rising at a market growth of 36.9% CAGR during the forecast period -...

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Dublin, Aug. 03, 2023 (GLOBE NEWSWIRE) -- The "China In-Vehicle Payment Market Research Report, 2023" report has been added to ResearchAndMarkets.com's offering.

This comprehensive analysis delves into the current state of China's in-vehicle payment market, examining its industry chain components, original equipment manufacturer (OEM) and payment platform layouts, consumer surveys, and development trends.

With the increasing demand for in-vehicle payment solutions, this report explores the rising popularity of this technology that allows for payment through in-vehicle communication and the In-Vehicle Infotainment (IVI) system. In-vehicle payment offers car owners the convenience of paying for various services, such as parking, refueling, food ordering, and shopping, all without leaving their vehicles, resulting in a more seamless and enhanced user experience.

Despite its current relatively low adoption rate, the survey reveals a high willingness among consumers to explore and use in-car payment functionality. Uncover the emerging scenarios where users are already employing in-car payment for parking, highway pass, refueling/charging, and more, and anticipate the promising future of this innovative payment solution in China.

The in-vehicle payment industry chain is taking shape.

In terms of supply chain, in-vehicle payment involves two major segments: in-vehicle payment device and in-vehicle payment platform.

In-vehicle payment devices are led by communication devices (SIM card, communication module and T-Box), interaction devices (touch/voice/ face/gesture/fingerprint interaction), and authentication devices (security chip); in-vehicle payment platforms are primarily cloud platform, payment platform, IVI system, ecosystem service platform, ecosystem service provider, and OEM.

As companies in each industry chain segment worked to make layout in recent years, the in-vehicle payment market has kept growing, with the following two major features.

In-vehicle payment is available to more scenarios.

Foreign automakers including BMW, Mercedes-Benz, Honda and Hyundai, and Chinese automakers such as Great Wall Motor, Xpeng Motors, Geely, Chery and AITO have launched their in-car payment function. They have widely deployed this function in parking, refueling/charging and food ordering scenarios, and are also applying it on a small scale in car wash/maintenance/repair services, feature subscription, ticket booking and other scenarios.

For example, in October 2022, BMW added the BMW ConnectedDrive Store to its IVI system via OTA updates. It enables in-car payment for subscriptions, and 13 features such as front seat heating, steering wheel heating and Carplay through the IVI system.

Multimodal interaction is being added to in-vehicle payment.

At present, the most common in-car payment is scan to pay and password-free payment. As in-car multimodal interaction technology improves, face recognition, fingerprint recognition and voice recognition are becoming the new in-car payment interaction and authentication methods.

For example, Mercedes-Benz has added fingerprint recognition and authentication to its latest in-car payment system PAY+; Chery EXEED TX/TXL supports face verification payment, a function allowing users to pay for parking fees or shopping through face recognition. The addition of multimodal interaction makes in-vehicle payment more secure and convenient.

The ecosystem is a key factor affecting in-car payment.

In the mobile payment system, millions of iOS and Android developers have developed various applications and built very rich application ecosystems, meeting living, work and entertainment needs of consumers and making smartphones an indispensable terminal in users' life.

In the in-car payment system, financial institutions like China UnionPay and VISA have developed a series of in-car payment systems; Alipay, Banma Zhixing and Huawei among others have built a variety of vehicle ecosystem platforms and launched a range of in-car services covering parking, refueling, travel, shopping and other scenarios.

Compared with mobile payment, the in-vehicle payment ecosystem is still weak at this stage, only meeting the payment needs in specific scenarios. With the development of intelligent cockpit and high-level autonomous driving, drivers will be freed from driving tasks in specific scenarios and pay more attention to other in-car needs. At this time, creating an in-car living space and building a closed-loop ecosystem with payment as the entrance will become a big demand.

Story continues

In-vehicle Payment Summary and Trends

Summary of Telematics Development

Development Path of In-vehicle Payment

SWOT Analysis of In-vehicle Payment

Mobile Payment Habit Formation

Technological Environment for In-vehicle Payment

There Will Be Bigger Space to Imagine In-vehicle Payment Data Mining and Application

Of the users who have used in-car payment:

Up to 78.9% use in-car payment for parking;

42.1% use in-car payment for highway tolls;

In-vehicle payment is also often used to pay for refueling/charging fees (31.6%), IVI traffic and APP membership (31.6%), feature subscription (21.1%), car maintenance/repair/wash (15.8%), and car insurance (10.5%);

Fewer users use this function in the scenarios of online food ordering and dining (5.3%) and travel (5.3%).

Key Topics Covered:

1 Overview of In-vehicle Payment1.1 Development History of In-vehicle Payment1.2 Application Scenarios of In-vehicle Payment1.3 In-vehicle Payment System Flow1.4 Mainstream In-vehicle Payment Methods1.5 In-vehicle Payment Industry Chain1.6 In-vehicle Payment Chip1.7 In-vehicle Payment Platform1.8 In-vehicle Payment Ecosystem1.9 In-vehicle Payment Business Layout of OEMs1.10 In-vehicle Payment Patents1.10.1 In-vehicle Payment Patent Map1.10.2 In-vehicle Payment Patent Layout of OEMs1.10.3 In-vehicle Payment Patent Layout of Suppliers1.10.4 In-vehicle Payment Patent Layout of Ecosystem Companies

2 In-vehicle Payment Consumers2.1 Overview of In-vehicle Payment Survey2.2 In-vehicle Payment Usage and Willingness to Use2.3 Frequent Usage Scenarios of In-vehicle Payment2.4 Users' Satisfaction for In-vehicle Payment2.5 Expected Scenarios of In-vehicle Payment2.6 Differences between Actual and Expected Scenarios of In-vehicle Payment2.7 Reasons for Using In-vehicle Payment2.8 Concerns about In-vehicle Payment2.9 In-vehicle Payment Interaction Modes and Payment Method Preferences

3 In-vehicle Payment Layout of OEMs3.1 BMW3.2 Mercedes-Benz3.3 Honda3.4 Hyundai3.5 Renault Samsung Motors3.6 Jaguar Land Rover3.7 Ford3.8 Great Wall Motor3.9 Xpeng Motors3.10 Geely3.11 Chery3.12 AITO3.13 SAIC Volkswagen3.14 SAIC ROEWE3.15 Other OEMs3.15.1 Human Horizons' Layout of In-vehicle Payment Application Scenarios3.15.2 GAC's In-vehicle Payment Patent Filings3.15.3 Xiaomi's In-vehicle Payment Patent Filings

4 In-vehicle Payment Platforms4.1 VISA4.2 China UnionPay4.3 Alipay4.4 Huawei4.5 Other In-vehicle Payment Platforms4.5.1 Xevo4.5.2 IPS Group4.5.3 ZF4.5.4 DABCO

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China In-Vehicle Payment Market Research Report 2023: Multimodal Interaction in Your Car - The Next Wave of Secure In-Vehicle Payments - Yahoo Finance

Arvato CRM Solutions has partnered with real-time productivity, optimisation, and process mining platform, KYP.ai to continue driving its digital transformation and AI offering.

The strategic partnership will enable Arvato to enhance its already established artificial intelligence and RPA (robotic process automation) solutions, providing clients with further scope for digital transformation through AI-powered intelligent automation.

Arvato CRM Solutions has expanded its product portfolio vastly in the last few years, with multiple solutions available to clients, including RPA, automation, AI, business process outsourcing, and digital road mapping. This partnership offers further benefits to its clients, as well as a brand-new product ADE, Arvatos Discovery Engine.

Utilising KYP.ais unique productivity mining platform, ADE can identify opportunities, potential savings, utilisation gaps, and areas for automation. The product will revolutionise customer service and experience by understanding how people and processes can work better together. In turn, this will enable Arvato to make more informed decisions and recommendations to its clients, increasing productivity based on real life data.

Working closely with a leading luxury automotive client, Arvato CRM Solutions has already implemented a customer service pilot campaign. KYP.ai enabled the team to reach its objectives of increasing the number of cases worked on, as well as increasing accuracy and efficiency, delivering better visibility and insight into team activity, and improving ease of use for the team.

Over the pilot campaign, several key results were identified. One of the main outcomes was the discovery of a 19.4% hidden utilisation gap. Solutions, such as knowledge pooling and creating a streamlined knowledge repository, would allow for the reduction of escalations and time for clarifying. Opportunities identified included utilising AI-powered transcription and content capturing tools.

Aside from the business benefits this new tool provides, its an ideal platform for identifying behaviour changes and well-being issues for employees. Long hours, minimal breaks, and other factors can be highlighted, allowing managers or supervisors to quickly identify issues and put preventative measures in place to hinder burnout or quiet quitting.

Henry Ellender, Head of Sales at KYP.ai, commented: Were ecstatic to develop this partnership with Arvato. By combining KYP.ais advanced productivity mining capabilities, with Arvatos outstanding customer relationship management experience and innovative technological solutions, were excited to see how this partnership can further enhance their expertise.

James Towner, Chief Growth Officer at Arvato CRM Solutions, commented: This partnership brings a fantastic opportunity for growth within our AI capabilities. More importantly, it ensures that we can help our clients identify areas for improvement and implement effective actions for their customer service or back-office teams.

Our focus is always on ensuring our teams are empowered by technology, through best practices, actions, and procedures. This allows for better decision-making, better customer service, and a better work-life balance for employees.

Debra Maxwell, CEO at Arvato CRM Solutions, added: Having the ability to provide this digital transformation platform to our clients is paramount. It puts the customer and the employee at the forefront, utilising data mining effectively to help automate relevant tasks.

It also highlights the value that an innovation-led, digital approach to customer experience can deliver for our clients, both within the public and private sectors.

With digital transformation at the core, Arvatos new AI/IA platform provides its clients with the ability to influence and extract process insights, implement automation, and help their employees excel.

About Arvato CRM Solutions

Arvato CRM Solutions is a trusted partner to the private and public sectors, with expertise in delivering award-winning customer relationship management, business process outsourcing (BPO) and public sector and citizen services.

The business focuses on providing customer service which is driven by technology and powered by its people. It designs and delivers innovative, individual solutions for some of the most respected global consumer brands and UK public sector organisations, through long-term partnerships.

A division of Bertelsmann, Arvato CRM Solutions employs approximately 1,500 people across five UK locations.

For more information, visit: http://www.arvato.co.uk

About KYP.ai

KYP.ai is a Productivity Mining company fuelling digital change. They enable clients to rapidly understand their abstract processes and the complex interaction between people and technology. KYP.ais automatically-generated, data-driven improvement recommendations aim at delivering the fastest possible transformation ROI. KYP.ai algorithms help to accelerate implementation of digitally augmented processes, combining unique human impact with machine-driven outcomes.

Find out more at http://www.kyp.ai

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Arvato partners with KYP.ai to enhance digital transformation ... - Directors Club News