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The population of the ultra-rich, those with at least US$30 million in assets, fell the most, sinking 4.6% last year after a 9.6% surge in 2021, the company said. The wealth of this group of 210,000 individuals fell by 3.

https://www.bing.com/th?id=OVFT._YYSfhm-Up-r-VzI7N82Ti&pid=News

The Rich Got a Bit Poorer Last Year

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Barron's

GAMCO Investors

GAMCO Asset Management Inc. ("GAMCO"), an affiliate of GAMCO Investors, Inc. (OTCQX:GAMI), and certain other affiliates own, on behalf of their clients, approximately 4.3%

GAMCO Sends Letter to Telephone and Data Systems

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Benzinga.com

Wealth management

Wealth

Investors with between $250,000 and $1 million outnumber the ultrawealthy by 2.5 to 1. For financial advisors, that could be a huge opportunity.

https://www.bing.com/th?id=OVFT.Yorl17iUg16t2Z1Rj8KNhC&pid=News

The rise of the not-so-rich investors

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Financial Planning

Asia

Razer v. Capgemini highlights the opportunity available for companies that suffer data breaches to recover the damages, costs and expenses incurred in responding to the data breaches from any responsible third parties. This is equally applicable to cyber insurers with respect to subrogation opportunities.

https://www.bing.com/th?id=OVFT.TMPFWMIel88Tf981D8cSoi&pid=News

Recourse following data breaches – what can companies do?

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hcamag.com

Car audio

Major players in the car audio market are Alpine Electronics Inc., Harman International, Clarion Co. Ltd., JVC Kenwood Corporation, Panasonic Corporation, Pioneer Corporation, Sony Corporation, Bose Corporation,

Car Audio Global Market Report 2023

Yahoo Finanzas

Risk

Cyber

Major players in the crime risk report market are CAP Index Inc., CoreLogic Inc., HackSurfer LLC, IBM The International Business Machines Corporation, Intelligent Direct Inc., Location Inc., PricewaterhouseCoopers,

Cyber Risk Report Global Market Report 2023

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YAHOO!Finance

Decade

Capgemini’s World Wealth Report, published today, reveals the global high-net-worth-individual1 (HNWI) population dropped by 3.3% to 21.7 million in 2022, whi

https://www.bing.com/th?id=OVFT.rED2A1uWtymDhsPiI1EsgC&pid=News

Global HNW population sees biggest decline in size and wealth for over a decade finds Capgemini World Wealth Report

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IFA Magazine

The Works

Artificial intelligence

Industry

Little Black Book, The technology transforms existing websites into a knowledge base for a conversational AI assistant, providing users with immediate, relevant answers

The Works Reveals Industry-First Artificial Intelligence Optimisation for Brands

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lbbonline

Get the most recent info and news about CAPGEMINI on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Capgemini SE provides consulting, technology, and digital transformation services. It addresses clients' opportunities in the world of cloud, digital, and platforms, as well as enables organizations to realize business ambitions through an array of services from strategy to operations. The company offers strategy and transformation services, including strategy, technology, data science, and creative design to support various clients within the digital economy. It also provides applications and technology services that helps the clients to develop, modernize, extend, and secure their IT and digital environment using the latest technologies, as well as offers local technology services model. In addition, the company provides digital engineering and manufacturing services, such as infrastructure services that regroup the installation and maintenance of client IT infrastructures in data centers or in the cloud; and business process outsourcing services. It serves various industries, including consumer goods and retail; energy and utilities; financial services; manufacturing; public sector; services; telecommunications, media, and technology; and services. The company has operations in North America, France, the United Kingdom, Ireland, rest of Europe, the Asia-Pacific, and Latin America. The company was founded in 1967 and is headquartered in Paris, France.

CAPGEMINI

Capgemini, a global information technology services and consulting company based in Paris, France, celebrates a series of as it unveiled double-digit growth rates across all business lines in its annual report for fiscal year 2022. big wins The company’s most prominent business lines include technology, business consulting and outsourcing services. Best known for its technology services, Capgemini assists clients by identifying, isolating and supplying solutions for various core technological issues within a business, including problems related to systems integration and application development. Propelled by an increased demand for digital transformation, the company’s Applications and Technology services division – its primary and most profitable business line – reported an +18.0% increase in total revenues for the full year. As for its business consulting services, the company strategizes with partners to implement and execute digital transformation initiatives aimed at improving a business’ long-term growth and ensuring clients stay competitive amidst a rapidly changing landscape. Additionally, the company’s outsourcing services comprise the regular management, maintenance and ongoing support of client IT infrastructures through its wide array of . managed services Just last year, the industry-wide growth in Infrastructure and Cloud services as well as a healthy Engineering services ecosystem, brought about a +13.4% boost in total revenues for its Operations & Engineering sector. As for the company’s Strategy & Transformation consulting services, total revenues also rose by +28.2% at the end of 2022. Now, in spite of growing concerns surrounding the economic climate’s recent instability, Chief Executive Officer of the Capgemini Group Aiman Ezzat expressed confidence in the company for closing out the previous year with higher revenues and exceeding growth expectations among various sectors, marking significant gains of 16.6% in constant currency growth and 13.0% in operating margin. The report emphasized the company’s focus on investing resources in the efforts to bolster its workforce through various training initiatives, reporting the company, in fact, “provided 17.4 million training hours to employees in 2022, compared with 12.8 million in 2021…a 12% increase in the average number of training hours per employee, well above the Group’s commitment to an annual 5% increase.” In a reflecting on the company’s most noteworthy accomplishments of the fiscal year, Ezzat highlighted Capgemini’s ongoing business philosophy of investing in their current talent base for long-term success: Youtube video “In 2022, we also demonstrated our ability to attract top talent to fuel our growth and agility in a fast evolving market. It’s not just about attracting talent, it’s also about investing in them, thanks to our continuous commitment to development, training and growth.” As of the end of December, Capgemini’s total global workforce experienced an 11% jump compared to last year, growing from 324,700 to 359,600 workers. More employees than ever work outside the country’s base of operations in France, with nearly 60% of the labor force – 210,300 employees – operating in offshore centers. Thanks to standards set by the ), Capgemini has made good on its promise to further its environmental sustainability efforts in 2022, dropping company-wide carbon emissions by 29% and 46% per employee to levels lower than they were in 2019. (SBTi Science-Based Targets initiative In line with the initiative’s net-zero standards for 2021, Capgemini also became one of the first in the world to have its net-zero emissions targets validated through the SBTi as of this reporting. Following its admittance to the Europe for its sustainability advancements in 2022, Capgemini revealed its intentions to meet various near-term and long-term carbon footprint targets for the next 10-20 years, including its long-term objective of reaching a in all carbon emissions by 2040. Dow Jones Sustainability Index (DJSI) 90% reduction

Capgemini Growth Rates Double as Sustainability Efforts Expand

Las tasas de crecimiento de Capgemini se duplican a medida que se expanden los esfuerzos de sostenibilidad

Les taux de croissance de Capgemini doublent à mesure que les efforts de développement durable se développent

स्थिरता के प्रयासों के विस्तार के रूप में कैपजेमिनी विकास दर दोगुनी हो गई है

サステナビリティへの取り組みが拡大する中、キャップジェミニの成長率は倍増

As taxas de crescimento da Capgemini dobram à medida que os esforços de sustentabilidade se expandem

Tốc độ tăng trưởng của Capgemini tăng gấp đôi khi các nỗ lực phát triển bền vững mở rộng

随着可持续发展努力的扩大，凯捷的增长率翻了一番

Welcome to the exciting world of financial technology, where innovation and change never stop. As an expert in crypto, I'm thrilled to share the top fintech trends set to shake up the industry in the nearest future. There are 26,000 fintech startups globally, almost double the number in 2019. The fintech industry is growing at about 20% annually, and the global market could reach $174 billion by 2023. Prepare to witness a wave of change and transformation powered by the latest breakthroughs in blockchain, open banking, and digital payments. approximately We'll delve into eight critical trends poised to dominate the fintech landscape, from the explosive growth of decentralized finance (DeFi) to the essential role of cybersecurity and data privacy. So, fasten your seatbelts and join me on this journey into the dynamic and ever-evolving world of financial technology. Digital payments Digital payments are set to revolutionize the way we handle transactions by 2023. As society becomes cashless, more companies are incorporating digital payments into their business models. The global digital payments market was at $7.36 trillion in 2021 and is projected to reach $15.27 trillion by 2027, implying an average annual growth rate of 12.38% from 2022 to 2027. The rise of cryptocurrencies and other digital currencies has further fueled this trend, offering consumers more payment options. Experts predict that by 2023, digital payments will account for over half of all transactions . valued globally The landscape of digital payments is . It is no longer limited to just familiar bank cards. While card transactions still account for a significant share of the non-cash transaction market - 72% in 2020, according to Capgemini - growth is stagnating due to the emergence of local and regional schemes such as EuPay, EPI, Verve cards, and others. These schemes are challenging the worldwide dominance of the card market. changing Disposable virtual cards have been around for some time, and their popularity for online payments is growing. They remain an excellent option for improving security and convenience, providing a worry-free experience when making payments on sites you have never bought from before and do not trust. Contactless payments simplify the payment process and make it more convenient for consumers. They benefit from shorter lines, the elimination of cash at the register, and faster queue movement. With disposable virtual cards, you can rest assured that your actual card data will remain safe from theft. Voice commands are a ubiquitous and rapidly growing trend, with virtually everyone now carrying a voice assistant. With the constantly improving capabilities of AI, this technology is poised to become even more sophisticated and revolutionary. It's a safe bet that voice commands will soon extend beyond simple commands and into payment systems. Payment bots, smart contracts, and payment automation are truly fascinating topics revolutionizing how we conduct transactions. As we progress towards a digital economy, the significance of these topics only increases. For instance, payment bots allow for quicker and more , while smart contracts provide a decentralized way of executing contracts without intermediaries. Payment automation, on the other hand, streamlines the payment process, reducing the likelihood of errors and increasing efficiency. secure transactions Payment chips have yet to be widely adopted, but their development has significant potential, given the increasing use of mobile devices that support NFC technology. Payment chips offer advantages over NFC technology, such as increased security or more efficient transactions. Furthermore, payment chips have potential applications beyond payment systems, such as contactless access control or identification verification. As new use cases for payment chips are explored, we expect to see an increase in their adoption and potential for innovation. Digital payments have become increasingly popular in recent years, and for a good reason. They offer unparalleled convenience, safety, security, and transparency that traditional payment methods simply cannot match. One of the key factors driving this trend is the prevalence of blockchain technology, which has emerged as an essential component of many digital currencies. Using a tamper-proof and decentralized ledger, blockchain ensures that transactions are secure and immutable, giving consumers the peace of mind to prioritize their safety and privacy. Additionally, digital payments have made managing finances and tracking spending more accessible. Thanks to digital tools, transactions can be quickly recorded and analyzed, empowering consumers to make more informed purchasing decisions. As a result, it's no surprise that more and more consumers are turning to digital payments to stay on top of their finances and make their lives easier. Another to shape the digital payments landscape in 2023 is the rise of . With the ubiquity of smartphones and mobile devices, more and more consumers are opting for mobile payments as a convenient and secure way to make transactions. Mobile payments are expected to surpass traditional card payments by 2023. trend mobile payments As we enter a new era of digital payments, companies that don't incorporate this payment method into their business models risk being left behind. With the potential for increased security, convenience, and transparency, it's no wonder that digital payments are set to be one of the most significant trends in fintech for years to come. ICO and DAO In 2023, we expect crowdfunding and P2P lending to continue to evolve and expand in the fintech industry. One exciting development is using initial Coin Offerings (ICOs) and Decentralized Autonomous Organizations (DAOs) in fundraising efforts. Through these technologies, individuals can come together to launch projects and raise funds. Another trend to watch is using fiat currency to secure loans backed by crypto assets. This lending model, known as the "fiat series," was a precursor to the peer-to-peer lending that emerged later. The growth of P2P lending has been remarkable, with a 62% increase in 2022, according to a report by . McKinsey The rise of blockchain and decentralized platforms has P2P lending more accessible, secure, and transparent. With smart contracts and blockchain-based identity verification, lenders and borrowers can transact without intermediaries. This eliminates the need for traditional banking institutions and offers more financial freedom to individuals. made Crowdfunding and P2P lending will continue to disrupt the traditional finance landscape, enabling new forms of fundraising and borrowing for individuals and businesses. Openbanking and API integrations Open banking and API integrations are expected to grow in popularity in 2023. With open banking, financial institutions allow third-party developers to access their customer data, creating new opportunities for financial innovation. This trend is fueled by regulatory initiatives like the European Union's PSD2 and the UK's Open Banking Standard. API integrations allow businesses to connect their systems and data with financial institutions, creating a more streamlined and efficient customer experience. In 2023, we expect to see more enterprises integrating with and other financial institutions through APIs, enabling faster and more secure transactions. banks Moreover, the rise of fintech startups has led to new API-based banking services that offer features like budgeting tools, automated savings, and investment management. These services provide users with a more personalized and intuitive experience, and banks are increasingly partnering with fintech to offer these services to their customers. In addition to these trends, we expect to see advancements in areas like blockchain-based identity verification, artificial intelligence, and machine learning in financial services. These technologies will help improve financial transactions' security, efficiency, and personalization, driving further innovation in the fintech industry. DeFi platforms to adopt advanced risk management techniques Decentralized finance is expected to continue its rapid growth in 2023, offering new financial products and services that challenge traditional financial institutions. With this growth comes increased attention on risk management, as the decentralized nature of DeFi presents both opportunities and challenges. DeFi platforms will likely adopt advanced risk management techniques using artificial intelligence (AI) and machine learning (ML) to address these challenges. These technologies can help identify potential fraudulent activities and assess the creditworthiness of borrowers more efficiently and accurately. Furthermore, DeFi protocols may implement more sophisticated risk management strategies, such as credit scoring models and collateralization requirements, to minimize the risk of default and protect investors' funds. Embedded Insurance Embedded insurance, a division of the finance industry, is a new trend to transform the insurance industry in 2023. It the integration of insurance into other products or services, such as mobile devices, cars, and home appliances. This means that customers will no longer have to purchase insurance policies separately but can instead have them automatically embedded into their products. involves One of the main advantages of embedded insurance is its convenience. Customers can meet their insurance needs seamlessly without filling out lengthy forms or visiting an insurance broker. This can lead to significant cost savings and increase customer satisfaction. In addition, embedded insurance can also be personalized to meet the specific needs of individual customers. For example, a car insurance policy can be tailored to the driver's usage and driving habits. Several have already started offering embedded insurance products. In the US, companies like Tesla and Uber have integrated insurance into their products, while in Europe, BMW and Volvo have launched similar programs. companies This trend is expected to grow in 2023, with more companies adopting embedded insurance to provide a more seamless and personalized customer experience. RegTech RegTech is a growing trend in the FinTech industry aimed at easing the burden of regulatory compliance through automation. It using technologies such as artificial intelligence, blockchain, and machine learning to help financial institutions comply with regulatory requirements more efficiently and cost-effectively. involves In 2023, RegTech is expected to continue growing, with more companies adopting it to streamline compliance processes and reduce the risk of non-compliance. One potential is using RegTech for identity verification and KYC (know your customer) procedures, which are critical for preventing fraud and money laundering. Another area is the integration of RegTech with blockchain technology to create more transparent and secure financial systems. growth area Cybersecurity In addition to traditional cybersecurity measures, advanced technologies such as machine learning, artificial intelligence, and biometric authentication are expected to grow in to further enhance security measures in the fintech industry. Biometric authentication, which uses unique physiological or behavioral characteristics such as fingerprints, facial recognition, or voice recognition, can provide a more secure and convenient way for users to access their financial accounts. 2023 Furthermore, the increasing use of computing and remote working in the financial sector has also led to the development of cloud-based security solutions. These solutions allow financial institutions to secure their networks and data from cyber threats while providing flexible and scalable security solutions. cloud Embedded FinTech In 2023, the growth of embedded FinTech is to continue, with more companies implementing this system to provide a seamless and integrated customer experience. With the help of embedded FinTech, traditional financial institutions can leverage other companies' customer base and user experience to offer financial services without needing a standalone app or platform. expected In 2021, the integration of credit and payments into non-financial products in the U.S. generated $20 billion in revenue for companies. Technological advancements made this trend possible, allowing for the seamless integration of financial services into everyday products. This led to higher customer satisfaction, increased sales, and more convenient shopping experiences. It has also revolutionized how businesses operate, creating new revenue streams, reaching new markets, and increasing competition among financial service providers. As technological advancements continue, businesses can offer more sophisticated payment solutions, better meet customer needs, and remain competitive. One of the most significant benefits of embedded FinTech is the ability to offer personalized financial services to customers. By analyzing customer data from other platforms, embedded FinTech can create tailored financial solutions based on user needs and preferences. This customized approach can help financial institutions to attract and retain customers in a highly competitive market. Embedded finance is a real win-win. On the one hand, it provides a better user experience for consumers; on the other hand, it leads to an increase in sales volume and average checks for distributors and the customer base for solution providers. Experts predict an annual industry growth of 40.4%. Moreover, the use of embedded payments is to become more prevalent, allowing users to make purchases without leaving the platform they are using. This can significantly increase the conversion rates for merchants while providing a convenient experience for users. expected How Have Web 3.0 and Blockchain Transformed Everything? Web 3.0 and Fintech are closely linked, with Web 3.0 focused on financial technologies such as blockchain and smart contracts. This allows for real-time features like transparency, safety, and decentralization for secure and transparent payments with information on the sender and reason. However, certain limitations, such as the inability to refund payments in case of an incorrect account number, must be addressed through ongoing innovation. Web 3.0 and blockchain technologies put the user first, with tokens giving ownership to platform users. This ownership is distributed among users and enables the transformation of the ownership structure of a company. Web 4.0, with the implementation of AI, will further personalize fintech and increase financial inclusion for people worldwide. Transparency is a fundamental principle of Web 3.0 and is being adopted by more fintech companies to build trust between users and the platform. Every financial transaction is recorded on the blockchain, making it immutable and transparent. By 2024, we expect more fintech companies to adopt Web 3.0 principles to improve transparency and build user trust. Blockchain Opportunity Scale Blockchain technology offers various opportunities, with the highest potential being its implementation in government systems. Several countries have already adopted cryptocurrency and integrated it into their banking systems. However, the official banking system and the unofficial blockchain system conflict. This will change when the government adopts digital payment solutions. While there may not be a unified system for the government to exchange resources in 2023, corporations already use blockchain technology. JP Morgan's Quorum is a blockchain-based platform with several apps in the blockchain supply chain. Alfa Bank in Russia has also launched a blockchain project with S7 Airlines for calculating jet fuel. Visa and Mastercard have introduced B2B Connect and Ripple for cross-border payments. These projects simplify transferring money overseas by eliminating the need for multiple verifications. Cryptocurrencies and blockchain technology enable secure and transparent transactions, and smart contracts automate manual activities. Corporations are already embracing technology and exploring its possibilities, while some governments may hesitate to adopt it. Legal regulation of cryptocurrency The regulation of cryptocurrency is a big issue in the fintech industry. Governments and regulatory bodies worldwide are trying to figure out how to regulate this new asset class effectively. Some countries have regulating cryptocurrency, but the regulatory landscape is still broad. Lack of clarity poses challenges for businesses and investors unsure about the legal implications of using or investing in cryptocurrency. started El Salvador has recently Bitcoin as a legal tender, and other countries may follow. In some jurisdictions, regulators are starting to take a more proactive approach to regulating cryptocurrency to balance innovation and consumer protection. New regulations in the require cryptocurrency exchanges and custodian wallet providers to comply with anti-money laundering and counter-terrorism financing regulations. adopted European Union In the United States, the Financial Crimes Enforcement Network has new rules requiring exchanges to collect user information and report certain transactions to the government. Regulatory bodies will likely establish new guidelines and rules for the use of cryptocurrency as its use expands. While this may create uncertainty and challenges for businesses and investors, it could also help to establish a more stable and secure environment for the growth of the cryptocurrency industry. proposed

Blockchain, Open Banking, and Digital Payments: the Most Important Fintech Trends to Watch

Cybersecurity is often cited as a minefield for businesses, and while this cliche remains true, it has become slightly outdated. Thanks to the expansion of the digital world, navigating the cybersecurity minefield has become virtually impossible. For every mine that explodes, ten more threat bombs are put in. , a hacker attack occurs on average every 39 seconds. Unfortunately, these hazards cannot be avoided; instead, they must be dealt with through a well-coordinated approach to safeguard sensitive information and defend systems from intrusions. According to a University of Maryland analysis Additionally, many businesses have had to adapt operations due to the ongoing COVID-19 outbreak and the uncertainty it has caused. To this end, technology enabled remote working, powering  new cloud services, changing firewall settings, and firing up new VPNs. But with each new innovation internet exposure and risk was increased. However, it’s not all doom and gloom. Cybersecurity can not only support organizational objectives, but it can also function as a business enabler that speeds up development. Here are four crucial ways that advancements in cybersecurity may promote business growth. 1. Appease Customers and Potential Investors According to a survey by Capgemini’s Digital Transformation Institute, as the third most important factor when selecting a retailer and products. Additionally, customers would generally be more satisfied, and 40% would increase their online spending by 20% if they felt trusting of an online retailer. 77% of customers rank cybersecurity Nowadays, customers have high expectations for how their vendor network should handle cybersecurity. If you demonstrate that you have a cybersecurity plan and mission statement in place, it can give a clear message to your consumers that cybersecurity is vital. Many growing and new firms will be aiming for some sort of exit strategy when searching for investment. If you deal with private equity companies, you could be required to do cybersecurity due diligence audits on prospective acquisitions. When a private equity company cannot verify appropriate cybersecurity policies, it may influence the sale price or the entire purchase. 2. Visual Indicators for Further Customer Confidence Visual safety cues greatly influence the feeling of security, and this perception of security may help you become more profitable overall. One helpful method is installing SSL/TLS (secure sockets layer and transport layer security) certificates to improve security and build user confidence. SSL/TLS certificates are used to authenticate a website, domain, or organization and establish an encrypted connection while displaying the appropriate security indicators (padlock, a green bar, etc.) on a user's web browser. 3. Stopping Attacks to Prevent Losses A ransomware attack has the capability to halt a business in its tracks and potentially finish them altogether. According to , the cost of ransomware attacks could reach $265 billion globally by 2031. The reality is that most businesses simply cannot afford a breach. And, if these businesses can't figure out a means to avoid attacks right now, they may cease to exist even to face future threats. research firm Cyber Ventures 4. Avoid Downtime and Save Your Bottom Line The average cost of downtime, , is $5,600 per minute, or $336,000 per hour. Most businesses might be able to afford the expense of a brief outage without it having a significant effect. However, if that downtime lasted 12 hours on average, your income would be considerably affected by that length of downtime—something to the tune of more than $4 million . This kind of damage can often drive small to medium-sized businesses to close their doors within a few months. according to Gartner By maintaining an up-to-date incident response plan, IT disaster plan, and business continuity plan (BCP) and testing your data backups, you can ensure that you have the personnel, tools, and procedures to restore your IT systems after an emergency. While having a solid security posture is crucial for your company from an IT security point of view, it's also clear that it's critical from a financial standpoint. Enhancing your cybersecurity posture and explaining how you have done so will help you win over more customers' trust and confidence, which can lead to more sales and income. This shift in perspective gives your company a strategic new way to grow profitability rather than merely cutting costs. - By Taylor Hersom, Founder and CEO of Eden Data

Four Tips for Capitalizing on Security Posture to Expedite Business Growth

The statistics on the role of AI in cybersecurity, in this article, were proposed by generative AI. DYOR. As our world becomes increasingly digitized, the need for cybersecurity measures has grown exponentially. With more and more sensitive data being stored online, cyber threats have become a constant concern for businesses and individuals alike. To combat these threats, the use of artificial intelligence (AI) in cybersecurity has become a popular solution. In this post, we will explore the basics of AI in cybersecurity, provide examples of its application, discuss the challenges of implementation, and look toward the future potential of AI in cybersecurity. Here are some statistics on the role of AI in cybersecurity: According to a report by , the global AI in cybersecurity market is expected to reach $38.2 billion by 2026, growing at a CAGR of 23.3% during the forecast period from 2021 to 2026. MarketsandMarkets A survey conducted by found that 61% of organizations are using AI to enhance their cybersecurity posture, and 21% plan to do so within the next year. Capgemini According to a report by the Institute, 69% of organizations believe AI is necessary to respond to cyberattacks, and 77% believe AI will be able to detect attacks before they occur. Ponemon A survey conducted by ound that 63% of organizations are using AI to detect and respond to cyber threats, and 62% are using it for identity and access management. f Deloitte According to a report by , by 2022, 30% of all cyberattacks will leverage AI, up from less than 5% in 2018. Gartner Overall, these statistics suggest that AI is playing an increasingly important role in cybersecurity, as organizations look to enhance their ability to detect and respond to cyber threats. The Basics of AI’s Role in Cybersecurity AI refers to the development of computer systems that can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. In cybersecurity, AI is used to analyze large amounts of data to identify patterns and anomalies that could indicate a potential cyber attack. The primary objective of AI in cybersecurity is to enhance existing security measures and improve threat detection and response times. The most commonly used forms of AI in cybersecurity are machine learning and natural language processing. Machine learning algorithms are used to identify patterns in data and use this information to make predictions about future events. In cybersecurity, machine learning algorithms can be used to identify anomalies in network traffic, identify malware, and detect potential breaches. Natural language processing is used to extract meaning from written or spoken language. In cybersecurity, this technology can be used to detect phishing emails and other types of social engineering attacks. Examples of AI in Cybersecurity There are many examples of AI being used in cybersecurity, and the technology is continuously evolving. Some of the most common uses of AI in cybersecurity include: AI is used to analyze vast amounts of data in real-time to detect potential security threats. This includes monitoring network traffic and user behavior for any anomalies that may indicate a potential cyber attack. Threat Detection: AI can be used to detect malware by analyzing the code and behavior of applications. Machine learning algorithms can identify patterns in the code that are typical of malware and flag any suspicious activity. Malware Detection: AI can be used to detect fraudulent activity, such as credit card fraud, by analyzing transaction data and identifying patterns of suspicious behavior. Fraud Detection: AI can assist in incident response by providing real-time information about the nature and scope of a cyber attack. This information can be used to prioritize response efforts and minimize damage. Incident Response: AI can be used to identify vulnerabilities in a system by analyzing data from various sources, including network traffic and user behavior. This information can be used to prioritize security measures and mitigate potential risks. Vulnerability Assessment: Challenges of Implementing AI in Cybersecurity While the benefits of AI in cybersecurity are significant, there are also several challenges to its implementation. One of the primary challenges is the potential for . These biases can be the result of the data used to train the algorithms, which may be incomplete or biased toward a specific group. As a result, AI algorithms may be less effective at detecting cyber threats that do not fit the established patterns. bias in AI algorithms Another challenge is the need for human oversight. While AI can assist in threat detection and incident response, it cannot replace human intelligence and decision-making. Humans are still needed to interpret the data and make decisions about how to respond to potential cyber threats. Finally, there is the challenge of ensuring that AI systems are secure. As AI becomes more integrated into cybersecurity systems, the potential for cybercriminals to exploit vulnerabilities in these systems also increases. Therefore, it is essential to ensure that AI systems are built with security in mind and that they are continually monitored for potential vulnerabilities. Future of AI in Cybersecurity The future potential of AI in cybersecurity is vast and is expected to continue to evolve in the coming years. AI is becoming increasingly sophisticated, and its ability to detect and respond to cyber threats is expected to improve significantly. Some of the key areas where AI is expected to make an impact in cybersecurity include: Predictive Analytics: AI can be used to identify patterns and predict future cyber attacks. This information can help organizations proactively prepare and defend against potential threats. Automated Incident Response: AI can assist in incident response by providing real-time information about the nature and scope of a cyber attack. In the future, AI systems may be able to automatically respond to cyber attacks, reducing the time it takes to mitigate potential damage. Behavioral Analytics: AI can be used to analyze user behavior and identify potential insider threats. This technology is expected to become increasingly important as more organizations shift to remote work environments. Edge Computing: With the rise of the Internet of Things (IoT), AI will play an increasingly critical role in securing connected devices. Edge computing, which involves processing data closer to the source, will require AI systems to be built into IoT devices to detect and respond to potential cyber threats. While the potential benefits of AI in cybersecurity are significant, there are also potential ethical implications that need to be considered. One of the primary concerns is the potential for bias in AI algorithms. This bias can result in discrimination against certain groups or individuals, which could have significant social and economic implications. As AI becomes more prevalent in cybersecurity, it will be essential to ensure that it is designed and implemented in a way that is fair and unbiased. Conclusion The role of AI in cybersecurity is becoming increasingly important as our world becomes more digitized. AI can assist in threat detection, incident response, and vulnerability assessment, and its future potential is vast. However, there are also challenges to its implementation, including the potential for bias in AI algorithms and the need for human oversight. As we move forward, it will be essential to consider the potential benefits and challenges of AI in cybersecurity and ensure that it is implemented in a way that is ethical and fair.

AI's Impact on Cybersecurity in 2023

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AI is approaching the next level of maturity, coming out of the hype cycle, says Gartner. Its adoption is expanding across industries beyond automation to building new-generation intelligent products and services for business growth. Around 90% of companies in the US and UK view artificial intelligence as a priority, with AI projects either in planning or production. However, half of them acknowledge that they lack the skill to make the most of AI advances. This is where experienced AI consulting firms come in to help. The market of AI consulting is vast, ranging from tech giants like IBM and Accenture to Big 4 firms and smaller-scale innovators. Here, we’ve compiled a list of the top 10 AI consulting firms that have earned a reputation for disruptive innovations. These experts run product discovery and deliver proofs of concept (PoC) and MVPs. This way, they help organizations bring pioneering products and services to the market and build proprietary AI systems for transforming operations. Let’s explore these top AI consulting companies — they are worth their salt. In this blog, you will find an overview of each of them and useful tips on picking the right partner for your AI project. Our list of the top 10 AI consulting firms for 2023 1. ITRex is a full-cycle AI and big data solutions provider with teams in Poland, the US, and Mexico and a dedicated R&D lab where they design new AI concepts and build prototypes. They are tech savvy in all AI-related fields (ML, deep learning, NLP, and computer vision), delivering AI projects end to end, from preparing data for algorithms to rolling out systems to thousands of users. ITRex Having adopted a start-small-and-scale-up approach, ITRex helps Fortune 500 companies and startups launch breakthrough AI products, apart from automating operations, boosting customer experiences, and supporting smart decision-making. They’ve delivered quite a number of impressive AI projects, from an AI-powered big data platform for the world’s biggest retailer to a fitness mirror with a personal coach experience, an ML-based clinical decision support system for cancer patients, a SaaS cybersecurity solution using face recognition, and many others. Walmart, 21st Century Fox, and Warner Bros. Entertainment Inc. are just some of their big-name clients. Rates: $50 – $99/hr Size: 300+ Founded: 2009 Headquarters: Aliso Viejo, California 2. Deeper Insights The UK-headquartered is fully focused on providing AI consulting and development services, with its expertise spanning from AI exploration to ML engineering, NLP, and computer vision. As they say on their website, they solve their clients’ “impossible problems,” helping them achieve the desired business results. Deeper Insights The company develops custom AI algorithms and ML models using either their clients’ data sets or data collected via their proprietary ML web scraper – the Skim Engine™. They have delivered many innovative AI projects for big-ticket and smaller clients, including an automated insights app for sales teams at Deloitte, a custom media monitoring platform for the global real estate services company Jll, and bespoke computer vision algorithms integrated with deep learning to recognize body parts in images in robotic surgeries. Rates: $50-$99/hr Size: 10-49 Year: 2014 Headquarters: London, UK 3. InData Labs also specializes in AI consulting and implementation services. As most top AI consulting firms on our list, they offer not only advice but also AI R&D, PoCs, MVPs, and development of AI web/mobile apps, empowering clients with data science and AI technology. InData Labs The company helps clients leverage predictive analytics, NLP, computer vision, and intelligent automation to get meaningful insights from data, understand audiences, forecast demand, reduce risks, and eliminate cost overruns. Examples of their AI-powered solutions include a computer vision system for pose estimation in fitness, face recognition for a surveillance system, image analysis for ticket processing software, and many more. They’ve also delivered different kinds of predictive analytics systems for logistics, eCommerce, sports, entertainment, and healthcare, along with NLP tools for simultaneous interpreting, remote education, and sentiment analysis. Rates: $50-$99/hr Size: 80+ Year: 2014 Headquarters: Nicosia, Cyprus 4. Cambridge Consultants With over 20,000 sqm of R&F facilities and a 900-strong team, helps organizations develop AI-based products that “will transform how we live and the world around us.” Cambridge Consultants Being part of the French-based tech consultancy Capgemini, Cambridge Consultants is a big and established player on our top AI consulting firms list, operating since 1960. Cambridge Consultants is honored for AI breakthroughs in many fields across industries. Projects in their portfolio range from a drone delivery service to military robots using AI-based navigation and reinforcement learning, a proprietary crop spraying technology, an AI system for an automated response to cyberattacks built for the UK Ministry of Defence, and a piano music classification tool. Rates: Undisclosed Size: 900+ Year: 1960 Headquarters: Cambridge, UK 5. ThirdEye Data Based in Silicon Valley with delivery centers in the US and India, describes itself as a one-stop shop for data science, analytics, and engineering services. Apart from providing end-to-end big data and AI solutions, the company has developed its own open-source tools for anomaly detection and predictive analytics. ThirdEye Their expertise is recognized by multiple industry awards. Microsoft, Walmart, British Petroleum, and AWS are among their Fortune 500 clients. The company has implemented over 30 end-to-end data and AI projects, such as an image quality detection system for Southern California Edison, MLOps engineering for XPERI, and predictive metrology for Glas Trosch. Rates: $25 – $49 hr Size: 51 – 200 Founded: 2010 Headquarters: San Jose, California 6. LeewayHertz Operating since 2007, specializes in many AI fields, from ML and NLP to computer vision. Among its big-name clients are Procter & Gamble, McKinsey & Company, Siemens, and Hershey’s. LeewayHertz With the mission to help organizations adopt AI at scale, LeewayHertz has entered our top AI consulting firms list for delivering innovative AI solutions across sectors. Among its landmark projects in collaboration with an Indian food tech startup, which has resulted in the creation of the world’s first tea-making robot Arya using ML, speech recognition, and NLP. They’ve also built a computer vision-enabled marketplace for AI applications and an AI-based employee time-tracking app. Rates: $50-$99 hr Size: 51-249 Founded: 2007 Headquarters: San Francisco, California 7. DataRoot Labs is a fast-growing company with teams in Ukraine and Israel. As a top AI consulting firm, it helps ambitious startups and startup accelerators turn their boldest AI ideas into reality. DataRoot Labs DataRoot Labs prides itself on driving tech innovation by running the DataRoot University, which offers data engineering and data sciences courses. They have accomplished a great number of projects around computer vision and other AI technologies for clients, delivering, for instance, a solution for defect detection in solar panels, a personal coach app, an in-store customer activity tracking app, an empathetic virtual assistant, and many others. Rates: $51-$99/hr Size: 10-49 Founded: 2016 Headquarters: Kyiv, Ukraine 8. Azati Software Creating fully functional AI solutions since 2016, shares its expertise on a consultancy basis across AI fields, from supervised/unsupervised learning to NLP, computer vision, predictive analytics, and conversational AI. Azati Software Their featured AI-based projects include a data processing tool to analyze inputs from oil and gas meters using ML and computer vision, an ML model for pharmaceutical marketing, a semantic search engine to tap into scientific datasets, and an AI-powered app that changes the interior of smartphone pictures. With an R&D center in Poland, this top AI consulting firm successfully helps clients translate their AI vision into prototypes and MVPs. Rates: $25-$49/hr Size: 150 Founded: 2001 Headquarters: Warsaw, Poland 9. Tooploox The EU-based AI consultancy has built its AI expertise by working extensively with US startups, innovators, and VCs. Tooploox With the mission to improve people’s lives through cutting-edge technology, Tooploox has extensive R&D capabilities that have widened after the company joined forces with Microscope IT, a development shop offering AI-related services for healthcare, medtech, and life sciences. Their skillsets around AI now extend to computer and 3D vision, deep learning, sensor fusion, NLP, data capture & OCR, and beyond. Tooploox has been recognized for its research at international conferences, such as NIPS and ICML. They are also known for building Virtum, an AI-powered image analysis system that helps spot cancer cells. Rates: $100-$149/hr Size: 150 Founded: 2012 Headquarters: Warsaw, Poland 10. Addepto is another fast-growing company that deserves a spot on our list of top AI consulting firms. It was established in 2017 and now helps companies grow with the power of data and AI. Addepto With expertise in all data-related fields, the Addepto team has won the trust of a few Fortune 500 companies, which they support in creating complex AI solutions. Among them are Citibank and P&G. Addepto has helped build a digital twin monitoring airport operations, an intelligent passenger and baggage tracking system, a shipping schedule and inventory management system using predictive modeling for the world’s second-largest aluminum company, and an ML-powered demand forecasting tool for postal services. Rates: $50-$99/hr Size: 10-49 Year: 2017 Location: Warsaw, Poland How to choose your AI partner - Tips from the ITRex CTO Reducing a lot of options available in the AI consulting market to just a few should make life simpler. However, you still need to choose only one of them at the end of the day. What should you consider? We’ve asked ITRex CTO Kirill Stashevsky, who has over 15 years of experience helping companies deliver innovative solutions, to share his tips. Here they are: 1. Explore the vendor’s website to make sure they indeed offer AI consulting services, not just development. [ ] This can be especially helpful for those with little or no technical background. The thing is, an experienced consulting partner might figure out a more elegant tech solution for translating your vision into a reality. This solution can be cheaper, quicker, and more efficient than the one you had in mind. The latest AI breakthroughs can be fascinating, but it is useful to make sure first before any coding begins that they are exactly what you need to build a fascinating product. A good AI consulting firm would be happy to look at your vision from different perspectives and give you free advice over the phone. Don’t hesitate and ask for it first. 2. Look through the vendor’s portfolio to check if they delivered a similar solution in the past. [ ] Companies’ portfolios with featured projects or case studies are a great source of valuable information. They let you understand which clients they served and what projects they accomplished previously. It is best to reduce your list of top AI consulting firms to companies that have a demonstrable experience with what you are trying to achieve. These guys should know your domain well so you will speak the same language with them without having to waste time explaining the basics. They are also likely to know all the potential pitfalls that might be typically encountered on a journey like yours. However, don’t be too quick to strike off a vendor of your choice that doesn’t have the exact expertise you are looking for. The firm can still be a perfect fit for your project if they specialize in AI consulting services (see the first tip). 3. Do a bit of homework to define your business objectives clearly. [ ] Instead of contacting all the 10 or 20 companies on your top AI consulting firms list to discuss your future product, prepare a one-pager describing what you are trying to build from the business point of view, and send it out. Some vendors would reply they don’t know how to help; some might figure out how they can implement your idea best, while others might offer you cost-efficient customization of a tool already in the market. This way, you will narrow down your list quicker, saving time and energy. 4. Don’t forget about social proof [ ] Check out previous reviews from clients who partnered with the company and the company’s rating. This information can be found on sites like clutch.co or goodfirms.co. Ask your acquaintances, colleagues, and people you meet at hackathons and industry conferences — they might have heard something about a firm of your choice. You can also ask the vendor for feedback from their previous clients. Wrapping it up Artificial intelligence is already profoundly impacting our daily lives while transforming the business landscape. that AI could add more than $15 trillion to global GDP by 2030, meaning that it has the potential to change the way we live completely. Capitalize on the emerging trend and put your company at the front of the line by partnering with the right AI consulting firm. PwC estimates Originally published . here

Top 10 AI Consulting Firms Today

Will you be content if you hire and pay four people but only three show up to actually do the work? And yet, many employers do just that. According to McKinsey, an average worker spends 1.8 hours daily gathering and aggregating data, a task that is redundant and doesn’t have a direct impact on business success, and, above all, can be easily automated. Is your insurance company looking for ways to relieve your employees from this routine burden while cutting costs and minimizing errors? If so, you can consult a to build or customize an RPA solution specific to your needs. robotic process automation company But before that, where can you use RPA in insurance? And how to prepare for its implementation — either in specific processes or company wide? What is RPA, and how can it serve the insurance sector? In the insurance industry, robotic process automation (RPA) refers to rule-based software bots that can perform repetitive manual tasks, such as extracting claims data, aggregating client information from various internal and external sources, and performing background checks. If we look at the statistics of RPA implementation, the insurance sector, along with retail, are the main beneficiaries of this technology. With this popularity, Juniper Research predicts that almost a half of the insurance sector . And McKinsey believes that those who have already adopted the technology will be able to by 2025. will invest in RPA by 2024 automate 25% of their manual processes Deploying RPA in the insurance sector brings about the following benefits: . RPA can serve as a bridge between modern software, such as an ERP solution, and the existing legacy programs and devices that are still important for business. Sometimes deploying RPA in insurance can replace legacy systems altogether. For instance, a North American insurer Security Benefit in one year by automating legacy processes. Facilitates integration with legacy systems saved 40,000 labor hours . There are many real-life examples of when adopting RPA in insurance took the excessive load off employees’ backs and gave them more time for higher-value tasks. For instance, Encova Insurance saved its workers by automating their customer retention process, while Australian Unity of manual labor over the course of eight months thanks to RPA. Reduces manual work 25 hours a week spared its employees 22,493 hours . When employees are not overwhelmed with tedious routine tasks, their job satisfaction increases. A large insurance group based in Hong Kong used to delegate routine clerical work to junior staff, who made mistakes resulting in high employee turnover. The company after automating 80% of these processes. Lowers attrition rates reported lower attrition rates . Deploying RPA in insurance does require initial investment, but it helps companies to save money in the long run. For instance, a global insurance provider after deploying just 13 RPA robots. Cuts costs saved £140,000 in six months Top 7 robotic process automation insurance use cases Underwriting Underwriters must analyze information from numerous sources to assess risks and find the best rates and policy options for their clients. In the life insurance niche, for instance, this task can take up to four weeks. Underwriters of their time on data gathering and entry. spend on average 40% Bots can aggregate and process data from internal and external sites and display it on a dashboard for faster and more convenient decision making. According to McKinsey, using RPA in insurance can . Moreover, enhanced with AI, bots can perform the following tasks: reduce data processing time by 34% Populate corresponding fields in the internal system Analyze clients’ claim history and suggest pricing Verify if the person already has an existing policy Assess loss runs Identify health risks, such as checking if the person smokes Extract the client’s credit rating from third-party resources, such as Experian When RPA takes over the trivial tasks, human underwriters can focus their efforts to streamline more complex cases. A Tier 1 US insurer from different policy systems and provide underwriters with a 360-degree of the client and their risks. Prior to this collaboration, underwriters wasted a lot of time to perform this task manually. And now they could use this time to better serve the customer. turned to Accenture to automate data extraction Business process improvement The insurance sector is full of paper-intensive processes, which makes it challenging to assess operational efficiency and identify areas for improvement. When automation bots take over, it becomes possible to track their workflow and record each step. Afterwards, during audits, companies can review the logs and measure parameters, such as processing speed and the number of manual interventions required, to identify candidates for process optimization. Even by simply automating a process, the efficiency increases in terms of speed and error rate. To achieve this, many organizations are incorporating insurance RPA into their workflow. Cattolica Assicurazioni, an Italian insurance firm, turned to UiPath to automate their processes. They investigated the process of financial reconciliation and found that it contained very few exceptions and could be almost fully digitized. They deployed RPA bots to perform tedious tasks, such as matching 20.000 lines of numbers. The financial department allocated six months to complete this project. With RPA automating 90% of the process, the company with a zero error rate. accomplished this task in merely two months Claims management and fraud detection McKinsey reports that automation . reduce the amount of manual work by 80% As you can see from the stats, this is a major robotic process automation insurance use case. When claim registration is done manually, it results in errors, slow service, and unpleasant customer experience. Incorporating RPA in insurance can streamline the whole process from First Notice of Loss (FNOL) to settlement: Integrate claims data from multiple sources, such as medical records for RPA in health insurance, photos of damaged vehicles, etc. Process scanned paper claims by classifying them and entering into the system correctly Assist in claims verification and sport fraudulent claims Identify any missing information and bring this issue to the responsible officer’s attention The Canadian claims management provider SCM Insurance Services fully automated data entry for FNOL, which allowed claims . And a large US property and casualty insurer, EXL, worker compensation claim handling time by 60% within the first four months of deploying RPA in insurance claims processing. to be completed 80% faster could decrease Policy administration and cancellation Even though current software packages for policy administration save employees a lot of labor, they still include complex navigations through multiple applications, causing inefficiency and opening room for error. Insurance robotic process automation solutions, backed by and natural language processing, can receive emails from policy holders, extract data, make the required changes, such as bank mandate and address alteration, and send confirmations. The technology can handle policy rating, issuing, endorsing, renewing, and cancellation, among other tasks. machine learning Zurich Insurance Group built by Capgemini based on BluePrism software. It involves RPA robots early in policy handling as they enter policy details into the system, issue invoices, and draft policy documents that officers can review. Both Zurich clients and the insurer benefit greatly from this approach. Raffaele Nutricati, Head of Robotic Process Automation at Zurich Commercial Insurance, said, deployed an automation solution “Not only are they receiving policies of higher quality than those processed in the traditional manner, there is a significant decrease in the number of emails and phone calls to clarify information with our support desk.” Regulatory compliance The insurance sector relies on several compliance standards, such as tax law and HIPAA privacy rules, to guide documentation and processes. Officers are expected to monitor and act upon any changes in regulations. They need to align claim types with the corresponding jurisdiction as fast as possible, risking a regulatory breach. Automation bots can take over this responsibility by monitoring regulations, validating customer data accordingly, keeping a log of changes, and generating regulatory reports. For example, one popular compliance use case is Name Screening Alert Review for Sanctions on politically exposed persons (PEP). Screening systems can generate thousands of alerts every day, and it would be exhausting to verify any false positives manually. RPA in insurance can pre-process these alerts, significantly limiting the number of false positives remaining for manual verification. Customer service As we established above, deploying RPA in insurance speeds up client-facing services and almost eliminates error rates, resulting in a better customer experience. With the abundance of insurance companies, clients always have an option to leave one provider for another. According to Deloitte’s survey, after experiencing poor customer service. 41% of respondents have left their insurer Moreover, automation enables companies to perform advanced analytics to gain a better understanding of customer expectations and provide personalized offerings. For example, a client has posted on social media about his upcoming trip. An RPA assistant takes notice of this information, and when the client contacts an insurance agent about, let’s say, home insurance, the smart assistant will notify the agent about the vacation, enabling the agent to produce a customized offer covering all of the client’s needs. Such an approach will give you a competitive advantage as about the lack of personalization in insurance products. 88% of clients complain Query processing The insurance sector receives large volumes of broker and customer queries that demand fast resolution. Manual query processing can be error prone and exhausting for employees, while deploying RPA insurance bots will guarantee faster and more accurate results. Hollard Group managed to facilitate broker communication through . As a result, they reduced execution time by 600%, saving 2,000 hours of employee time per month. automating 98% of email handling processes The path to successful adoption of robotic process automation in insurance Here are the five steps that you can take to prepare yourself and your company for the upcoming automation project: . Opt for processes that are well-structured, rule-based, and don’t have many exceptions. It is also a good practice to choose frequently used processes as they will amplify the benefits and speed up return on investment. According to Gartner, the should not include more than 20 steps or involve over three applications. Identify candidate processes for automation ideal RPA candidate . Analyze the process first and eliminate any unessential steps. Also, even though every employee performs the task differently, come up with a standardized process version for automation. Optimize processes before automation . This will help people appreciate RPA benefits, give improvement suggestions, and will make them generally motivated to use the new system. Involve your employees from the beginning . There are four well-established RPA vendors, namely , , , and . Select your RPA platform UiPath Blue Prism Automation Anywhere WorkFusion . Most likely, you will still need to customize your preferred RPA solution to cover all your needs and fit into your workflows. An experienced robotic process automation company will ensure a seamless integration. Find a vendor who will help you incorporate the selected RPA insurance tools into your existing system You can learn more about in our recent article. RPA adoption challenges and how to overcome them What’s next for RPA in insurance? Deploying robotic process automation in the insurance industry can speed up operations, bring down expenses, and increase customer satisfaction. But this is just the beginning of the road towards intelligent process automation (IPA), which will empower RPA bots to take over tasks typically reserved for humans. With the help of cognitive technologies, such as , , and , RPA can maximize its contribution across the insurance value chain. AI emotion recognition optical character recognition You can learn more about intelligent automation from our recent article that . We also composed a comprehensive guide on and . Finally, you can discover more on by visiting our blog. compares IPA to RPA how to start with enterprise automation how much it will cost to implement an RPA solution RPA in finance Want to increase efficiency and cut costs by automating some of your manual insurance processes? ! 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RPA in Insurance: Your Complete Guide

Background: The need for change is evident, as the fashion industry is responsible for 10% of the world’s total carbon emissions, 20% of wastewater from dyeing and treatment, and the disposal of 87% of its total fiber input in landfills. If we do not act now, there is a 60% expected increase in industry emissions by 2030. Sustainability in the industry needs to be put at the forefront, the fight against overconsumption and unethical practices must be embraced in order to create a difference. With you and me, ÀLA.HAUSSE can be a catalyst to produce a feasible, circular ecosystem, for a long-lasting and greener future. The Ideation One evening Post-New Year's Eve. I was sitting on my yoga mat. My Bichon Frise Caesar came by to demonstrate his infamous downward-facing dog. While I was watching him stretch with confidence, I started to contemplate what dogs in other countries were up to. I couldn’t help but think about the homeless ones, the ones waiting to be rescued, and the ones forced to be euthanized. That night, I made a pledge to myself to help these poor souls any way I could. The following days were spent in intense contemplation. I reflected on what sort of conclusions could solve this urgent and imminent issue, so that then we may be able to help drive attention towards helping impoverished animals. However, as more research is done, more urgency makes itself apparent within the environmental surrounding the fashion industry. concerns https://vimeo.com/464822189 The Formation I’ve always been a fan of fashion and the scalability of a marketplace model, but unfortunately I have also been an unconscious shopper in the past. I decided to combine my passion and faults into one, and voila: ÀLA.HAUSSE. ÀLA.HAUSSE is an AI-powered, sustainability-driven ecosystem where everyone and anyone can buy, sell, rent, or lend their personal " Clossets" (clothing assets) to each other, facilitated by our networking and technology prowess. [ ala.hausse ] The Uprising: ÀLA.HAUSSE is French for “On the Rise”. I chose this name because it represents the uprising sustainable movement and principles that has started and will be held close to our values as individuals and members of our society. https://vimeo.com/642517986 Fast forward a year later, we see that the fashion industry is ready to embrace our movement. The tipping point has been the observed new-gen attitude shift towards sustainable and eco-friendly practices. Brands In 2020, H&M launched a sustainability obligation and are now using 64.5% of materials from recycled or more sustainable sources (planning on 100% by 2030), 100% organic cotton, is number 1 in the fashion revolutions fashion transparency index Gucci has been establishing its sustainability efforts by achieving a 44% reduction for our total footprint, surpassing their target four years ahead of time also achieving a 47% reduction in greenhouse gas emissions alone since 2015 thredUP,  a sustainable (online resale platform for women’s and kid’s apparel) IPO that has been able to save 1.0 billion pounds of CO2 emissions, 2.0 billion kWh of energy, and 4.4 billion gallons of water, allocating $500,000 from the proceeds of the IPO to start a policy arm that helps curb the disposable fashion crisis. Versace vows to use 100% renewable energy by 2025. Consumerism According to 's report, 2nd hand resale and rental market top ⅔ fastest growing sectors in fashion, with a growth rate of 25X than the broader retail sector 49% VS 2%. By 2029, resale is expected to be bigger than fast fashion. thredUP Compared to the second-hand apparel market increased from $11 million in 2012 to forecasted $64 million in 2024. In 2020, 86% of over 200 Million surveyed consumers say they have or are open to shopping. 45 percent of GenZ and millennials say they refuse to buy from non-sustainable brands and retailers. The resale market is growing at a rate 11 times faster than traditional retail and should be worth $84 billion by 2030, with fast fashion predicted to be worth about $40 billion. Genz and millennials are powering the rise of shopping second hand with each generation having 42% of people who shop second hand secondhand products, when compared to the 45% in 2016/ Secondhand, is expected to be 2x bigger than fast fashion in 2020 Corporation CSR ( Corporate Social Responsibility ) is being emphasized as the key to the industry’s sustainability journey: from behaving ethically as a business practice to taking on the environmental responsibility of a corporation and business process and operating cost. Kering has developed an innovative tool in 2018 and has further implemented and shared to the industry for adaptation for measuring and quantifying the environmental impact as a key enabler of a sustainable business model, for guiding its sustainability strategy, improving its processes, and supply sources, and choosing the best-adapted technologies. Sustainability practices have become a brand-defining agenda for small to luxury brands in the 21st century. UN Sustainable Development Goals are also taking on new importance: More companies than ever are embracing the SDGs as drivers of their CSR/ESG work in the next decade. As corporate culture becomes more transparent and accessible to buyers, who are driven by their beliefs and big-picture values, corporate giving and other social impacts are recognized and rewarded in the form of brand loyalty. 27 percent of companies reported an increase of more than 25% in corporate giving. 77 percent of consumers are motivated to purchase from companies committed to making the world a better place, while 73 percent of investors state that efforts to improve the environment and society contribute to their investment decisions.  These figures indicate a business can realize significant rewards by prioritizing CSR initiatives and working them into its strategic plans, attracting both customers and investors. 25 percent of consumers and 22 percent of investors cite a “zero tolerance” policy toward companies that embrace questionable practices on the ethical front. The takeaway here is clear: A business that knowingly engages in deceitful or otherwise socially or environmentally harmful behavior risks losing a significant percentage of potential customers. Circular business models, including re-commerce, could enable the industry to cut around 143 million tonnes (~315B lbs) of GHG emissions in 2030. To align with the 1.5-degree pathway [targeted by the Paris Climate Change Agreement], by 2030 we need to live in a world in which one in five garments are traded through circular business models. Nearly 90 percent of executives believe a strong sense of collective purpose within their organization drives employee satisfaction. A further 84 percent believe it affects the organization’s ability to transform, and 80 percent believe it can increase customer loyalty. As you can deduce from earlier statistics, they’re correct: Both customers and employees value purpose and specifically seek businesses that aim to make a difference. Investment The global online clothing rental market is anticipated to expand at a compound annual growth rate ( ) of 10.76% during the 2018-2023 period and will reach a value of USD 1.96 Bn by 2023, up from USD 1.18 Bn in 2018. CAGR Secondhand Market is Projected to Double in the Next 5 Years, Reaching $77B Environmental, social, and governance ( ) are making their way into the venture capital space. From 2016 to 2018, ESG integration increased by 69% across global public and private markets. Nearly 2/3 of investors believe that ESG guidance will become essential to alternative assets over the next three years. ESG Sustainable investing, impact investing, socially conscious investing, or socially responsible investing, ESG investing has gained significant momentum over the past few years—and the coronavirus pandemic has accelerated its speed. Sustainable assets in the US are growing at a particularly rapid rate. after quadrupling from 2017 to 2020, the US now accounts for 20% of all global ESG assets. In June 2020, there were already 534 sustainability-focused index funds globally, with collective assets totaling $250 billion. AI/Machine Learning Global spending on AI technologies by the fashion & retail industry is expected to reach $7.3 billion each year by the year 2022. According to a survey by Capgemini, artificial intelligence could help retailers save $340 billion annually by the year 2022 by enabling efficiency in several processes and operations. AI offers a sustainable solution to fashion, reducing overall inventory levels by 20–50% as well as improving working conditions in the fashion industry*.* Tommy Hilfiger announced a partnership with IBM and the fashion institute of technology, applying machine-learning tools to 15,000 of its product images, 600,000 publicly available runway images, and almost 100,000 fabric patterns sites. China-based fashion retail giant Alibaba has launched its first FashionAI store, the company introduced in-store features including smart garment tags, intelligent mirrors, along with Bluetooth chips embedded within every product. With its foray into using AI for fashion, Amazon is deploying an AI-enabled fashion designer algorithm that can design apparel by copying the design styles of many in-vogue clothes and applying them to a new clothing item. The Solution Introducing: The World's First Sustainable AI-Powered P2P Multifunctional Ecosystem, for Me and You. https://vimeo.com/642515868 Phase 1 overview: An Inclusive Fashion Ecosystem: Over 2500 Brands ( Chanel to Marni to Zara ) AND Unbranded items (thrift wear and vintage ) for Male, Female and Others. Buy and Sell: Up to 75% off from retail price on niche finds Rent and Lend: Rent for less than 1/10th of the retail price and make recurring income from lending your items. Mark the Date: Calendar booking and order management system for streamlined order tracking. Vacation Mode: Pause the availability of your items posted for rent with the press of a button. No sweat, only a good tan line with vacation mode. Get Social: follow, chat, and connect with your neighbors and new friends. Shop Brighter: Use our in-house analytics tool to help you make smarter decisions. CSR Driven Co2 Carbon Metrics: Keep track of the amount of Carbon you have helped prevent from being released into our atmosphere! Shop with Confidence: Your buy/sell/rent/lend transactions are secured by our state-of-the-art escrow technology and Stripe’s Fraud Protection. Get Surprised: Tons of hidden surprise features for you to discover. To sum up, our impactful and user-driven technology solution allows users to save up to 90% of the retail price on items, and up to 80% of their cluttered “Closset” space. The ÀLA.HAUSSE Fashion Ecosystem will provide you with a secured, time-saving, streamlined shopping environment, and a place to form relationships. With the help of you and our powerful all-in-one in-app features, we will create recurring income and a truly circular economy for all. By implementing our ever-changing sustainable-driven business model and an in-app circular economic framework, everyone can become a part of the solution. Together we will establish a harmonious and inclusive ecosystem of uplifting individuals, and marching together against the massive carbon footprint created by the traditional textile industry. ÀLA.HAUSSE is determined to be a leader in sustainable fashion. We will push social AI and E-commerce Tech as a solution for 21st-century shoppers, We recognize the potential of a closed-loop ecosystem, powered by people from all walks of life, backgrounds, and cultures. Because here, everyone has an opportunity to create change and to alter the fast consumption cycle, all while supporting their love for fashion and our planet. See you soon #HAUSSEPEOPLE and don’t forget to #WEARYOURPURPOSE. À New Wave to Fashion, À New Way of Living. " https://player.vimeo.com/video/619461988?h=3629a507ce The World's First Sustainable AI-Powered P2P Multifunctional Fashion Ecosystem, for Me and You. Now live on iOS Android. Special thanks to everyone from the technology team, supporting organizations, management and advisory team, contractors, employees, student members, professors, and all of our contributors who have supported us since the beginning of our early journey. Find them on our website soon! 💚 Join us on the upcoming ride, and learn more about our expansion plans in sustainability, AI & machine learning, and many more: / hello@alahausse.ca alahaussehr@gmail.com Deck request: hello@alahausse.ca EPK request: hello@alahausse.ca/ alahaussepr@gmail.com Endnotes: ÀLA.HAUSSE's product and brand mission is designed to meet 8/17 of UN's sustainable goals. Stay tuned, for the upcoming release on our white paper. GOAL 6: Clean Water and Sanitation We have a strong will to ensure ÀLA.HAUSSE contributes to clean and sustainable water around the globe, as the fashion industry is one of the top polluters of aquatic ecosystems, leaking synthetic microfibers and other determinants through its production and cleaning. With the establishment of a circular ecosystem, we can maintain water pollution to a minimum. GOAL 7: Affordable and Clean Energy Clean, affordable energy is one of our priorities, to build an eco-friendly attitude towards the production and consumption of energy. This starts from resources needed to produce clothes to its distribution; it's crucial to minimize the amount of energy used in these processes and ensure it is from a sustainable source. GOAL 8: Decent Work and Economic Growth-> for personal and small business and brands earning and creating recurring revenue Alongside sustainability and fashion, ÀLA.HAUSSE was made with the thought of personal and small businesses at the forefront. We want to provide a market where individuals can apply their passion with the benefit of income, supporting their endeavors while contributing to a greener future. GOAL 9: Industry, Innovation, and Infrastructure ÀLA.HAUSSE prides itself as a standard of innovation in our processes, using high-tech AI-powered analytical systems to build our applications and ensure we achieve our goals. We are always innovating new ideas to aid our efforts with the hopes of exuding high merit in our infrastructure and in the fashion industry. GOAL 11: Sustainable Cities and Communities By establishing a circular ecosystem within fashion, we are taking a step into changing mindsets to prioritize sustainability. Together we can create long-lasting change in the world, intersecting technology and environmentalism to move forward at a pace that hasn’t been witnessed before. Together we will make the difference. GOAL 12: Responsible Consumption and Production The fashion industry has monumental issues regarding fast consumption and unethical production. ÀLA.HAUSSE recognizes these challenges and wants to alter the rates by creating a closed-loop system that defies the “out with the old, in with the new” quick trend cycle system we are currently witnessing. Through our BUY/SELL/LEND/RENT functions, consumption can be slowed, impacting production rates and methods. GOAL 13: Climate Action Carbon emissions are increasing at an all-time high, approaching the rate of decline that we will not be able to reverse. Action is needed and it is needed now, which heightens the importance of our mission to decrease fashion’s CO2 levels. We included a CO2 standardized generator for users to witness how much emissions they are saving from their trades or purchases. GOAL 16: Partnerships ÀLA.HAUSSE welcomes any potential partnerships that can help aid our mission of saving our environment, one clothing piece at a time. Please email for more information. hello@alahausse.ca

The Uprising of A Sustainable Ecosystem for the Circular Future

By 2021, globally have already adopted DevOps. As more companies undergo digital transformation, you will see a rise in these numbers. DevOps enables smooth communication and collaboration among development and operation teams to shorten the development cycle and increase release frequency. However, to successfully implement DevOps into your ecosystem, you should know all its best practices and principles. 74% of the organizations One of those fundamental principles is continuous testing, where you execute automated test cases as a part of the delivery life cycle to obtain real-time feedback about software quality. Unfortunately, only have successfully implemented this principle, which shows it’s not easy as it sounds. So the question is, how can one optimize continuous testing to utilize DevOps to its fullest potential? OR what best practices one can follow for it? 55% of top-level firms For addressing this burning issue, I’ve decided to pen down my thoughts on this subject. This article will help you know the best practices that can help you optimize continuous testing in the DevOps life cycle. So, let’s get things moving now! Best practices to optimize continuous testing Craft teams with a diverse skillset You can assign the responsibility of continuous testing to a few QA experts and hope that they can get the job done in a quick time. However, cloud-driven interconnected apps and networking endpoints can succumb to failure. Maintaining a stable, resilient, and high-performance backend is critical for , and for that purpose, you will require teams with a diverse skillset. continuous delivery DevOps' multidisciplinary teams can accelerate the testing process without compromising the continuity. Furthermore, these teams can rapidly respond to any type of failure during testing and analyze test results. Thus, start preparing such teams in advance. Create a feedback loop As per a conducted by Capgemini, 40% of users measure the effectiveness of continuous testing via user feedback and adoption. Therefore, you should have a feedback loop from the beginning of the process in the DevOps ecosystem. The feedback loop will connect every team member and ensure a real-time collaboration. survey continuous testing When you have seamless communication among the various teams, there’s every chance that you will be able to deliver a high-quality product to the client. Furthermore, introducing a real-time feedback mechanism system will allow creative ideas and problem-solving approaches to brainstorm. Utilize test automation suite is the heart and soul of the continuous testing life cycle. It will enable you to perform automated checks on the developers. However, to utilize the true potential of the test automation suite, you need to select the right tools. Sometimes, you would only use the tool your team is familiar with. Test automation suite Other times, people get trapped into choosing a widely popular tool. Both these approaches are wrong; you need to know the skill set of your developers and the allocated budget for testing. Then, based on that analysis, come up with suitable tools that fit your need. Here are some of the most commonly used tools for automation testing: Performance and load testing JMeter: Web, API, desktop, and mobile testing Katalon Studio: REST, SOAP, and GraphQL API testing Soap UI: Web application testing Selenium: Maintain a proper test documentation Successful DevOps teams have maintained proper test documentation to make the continuous testing process transparent and organized. The testing documentation comprises Quality Management Plans (QMP), Test summary reports, Test case specifications, Risk assessment reports, , etc. You should create these documents before or after completing the testing phase in the DevOps lifecycle. One of the biggest USPs of these test documents is that they would serve as a reference for the trainees, and the organization doesn’t have to create the essential test documentation from scratch again, which saves a lot of costs. Regression test reports Try out headless browsers Headless browsers allow you to run the test cases in the browser without using a UI (User Interface) or GUI (Graphical User Interface). So, you can use them for implementing continuous testing in the DevOps ecosystem, especially if you’re developing any web application. , HTML Unit, Headless Chrome, etc., are some of the popular headless browsers you can utilize. Furthermore, headless browsers can also help you to scale up continuous testing. Lastly, it requires less RAM and processing power, and that’s why it can offer results quicker than a standard browser which is precisely what you want in a continuous testing scenario. Puppeteer Focus on designing optimal workflows Designing optimal workflow is critical to the success of a DevOps lifecycle. However, the effectiveness would only last until you have the stable infrastructure to support a continuous DevOps cycle for constant testing. For example, if your system is error-prone and gives false-negative results, you can’t implement continuous testing in that environment. To avoid this situation, focus on designing optimal continuous testing workflows that automate manual procedures and deliver faster results. So, you should focus on automation for . Then, it would help you in a continuous testing scenario. successful enterprise DevOps transformation Measure performance with metrics Implementing continuous testing in the DevOps ecosystem is one thing by following all the best practices, but knowing whether your strategy worked or needs improvement is also essential. For that purpose, you can take assistance with various metrics. Some of the key metrics you can measure include the ratio of the pass to failed cases, the number of bugs detected, frequency of test case failure, etc. By analyzing these metrics in detail, organizations can know the areas vulnerable to failures and where continuous testing can be the most effective. Lastly, these metrics will also help test automation engineers to write better test cases for the future. Wrapping up Implementing continuous testing is not only about following the best practices. If you want to successfully implement continuous testing for your DevOps ecosystem, try to streamline the collaboration among development and operation teams. If the teams work alongside each other, getting constant feedback and resolving the error in real-time becomes more manageable, which is the essence of continuous testing. Continuous testing is an integral part of CI/CD pipelines that helps you deliver high-quality software rapidly. However, the success of this technique depends on the best practices that you follow. Here, I’ve covered some of these best practices in detail. I hope this will help you in optimizing continuous testing in DevOps implementation.

Continuous Testing in DevOps: Only 55% Of Top-Level Firms Have Implemented It

By Alyze Sam and Tech & Authors First, Fiat To understand where crypto came from, one must start at the history of fiat, you can find that blog post . After you’re versed in fiat, enjoy this unbiased and poetic overview of cryptocurrency, starting with the birth of blockchain technology. here Blockchain History Blockchain is a complex technology where digital information is saved within the “blocks” and stored on a public database termed the “chain.” Within each block, there is a unique code termed “hash,” which is its identification. Hashes are cryptographic codes created by specific algorithms. The purpose of blockchain is to allow digital data to be recorded and distributed. Blockchain technology was fundamentally sketched in 1991 by Stuart Haber and W. Scott Stornetta, two researchers who aspired to execute a system where document timestamps could not be tampered with. Approximately two decades following in January 2009, the launch of Bitcoin birthed blockchain technology’s initial real-world application. The Bitcoin protocol is constructed on blockchain technology. In the research paper, or ‘white paper’ introducing the digital money, Bitcoin’s pseudonymous originator Satoshi Nakamoto introduced it as “a new electronic cash system that’s fully peer-to-peer, with no trusted third party.” Bitcoin, a proper blockchain application, saw success within financial technology when other digital assets failed to solve the double-spending problem. The double-spending dilemma arises when digital transactions are mimicked, allowing assets to be spent twice. Bitcoin has thrived as it solves this problem. The original decentralized cryptocurrency, Bitcoin, was produced in 2009 by pseudonymous developer Satoshi Nakamoto. It used SHA-256, a cryptographic hash function, as its proof-of-work system. In April 2011, Namecoin was a resolution to establish a decentralized DNS, making internet censorship difficult. Litecoin was released six months later. It was the earliest successful cryptocurrency to employ a script as its hash function alternative to SHA-256. Another notable cryptocurrency, Peercoin, was the initial project to use a proof-of-work and proof-of-stake hybrid. Blockchain Protocol When one spends or trades Bitcoin, ASIC Miners, or computer elements on the Bitcoin network, compete to validate transactions. To do so, users run a program on their processors to solve complex mathematical puzzles called a “hash.” When a processor resolves the problem by “hashing” a block, its algorithmic work will verify the transactions. As mentioned, the accomplished transaction is openly documented and saved as a block of data on the blockchain, becoming immutable unless 51% of miners agree to change it. Bitcoin and many blockchain protocols utilize machines that successfully verify blocks to obtain rewards for their labor paid in cryptocurrency. This method is termed “mining.” Although events are publicly documented on the blockchain, user information is not — or, at least, not in maturity. To conduct transactions on the Bitcoin network, participants must run a program called a “wallet.” Each wallet consists of two unique and distinct cryptographic keys: a public key and a private key. The public key is the location where transactions are deposited to and withdrawn from. This is also the key that appears on the blockchain ledger as the user’s digital signature. Even if a user receives a payment to their public key, they will not be able to withdraw without its private counterpart. A user’s public key is a shortened version of their private key, created through a complicated mathematical algorithm. However, due to the complexity of this equation, it is almost impossible to reverse the process and generate a private key from a public key. For this reason, blockchain is considered confidential. Public and Private Key Basics We can think of a public key as a school locker and a private key as the locker sequence. Anyone can slip notes into the hole in a locker. However, the only character who can reclaim the contents is the entity with the individual key or combination. Nevertheless, it should be recorded that while school locker combinations are kept in the principal’s office, there is not a central server that retains a record of a blockchain network’s private keys. In the event someone misplaces their private keys, access to their Bitcoin wallet is also lost. Single Public Chain The Bitcoin blockchain is shared and maintained by a public network of users and is a community consensus model. When users join the network, their processor obtains a duplicate of the blockchain that is renewed whenever a new block of events is computed. Nevertheless, what if, through human error or a hacker’s efforts, one user’s copy of the blockchain was altered? The blockchain protocol discourages the existence of multiple blockchains through a process called “consensus.” In the presence of multiple, differing blockchain copies, the consensus protocol will adopt the longest chain available. More users on a blockchain mean that blocks can be added to the end of the chain faster. By that logic, the blockchain record will always be the one agreed upon by the majority of the participants. The consensus protocol is one of blockchain technology’s most significant strengths but also a potential weakness. Hacker-Proof Ecosystem In theory, a hacker could gain the position of the consensus protocol by a 51% attack. Here is how it would happen. Assume there are twenty million processors on the Bitcoin interface. To attain a preponderance on the network, a hacker would need to control at least 10.5 million and one of those ASIC Mining processors. An attacker or accumulation of antagonists could hinder the method of recording new events. They could send a transaction and then reverse it, making it appear like they still had the coin they just spent. This vulnerability, known as double-spending, is the digital equivalent of a perfect counterfeit and would enable users to spend their crypto twice. Such an initiative is complicated to execute on a blockchain of Bitcoin’s size, as it would require an attacker to gain control of millions of computers. It would have been much more manageable when Bitcoin was founded in 2009 and its users numbered in the dozens. This defining characteristic of blockchain has been flagged as a weakness for fledgling cryptocurrencies. The fear of 51% attacks can limit monopolies from forming on the blockchain. In “Digital Gold: Bitcoin and the Inside Story of the Misfits and Millionaires Trying to Reinvent Money,” New York Times journalist Nathaniel Popper writes of how a group of users, called “Bitfury,” pooled thousands of high-powered computers together to gain a competitive edge on the blockchain. Their goal was to mine as many blocks as possible to earn Bitcoin, which were then approximately $700 each. Harnessing Bitfury By March 2014, however, Bitfury was poised to surpass 50% of the blockchain network’s computational capability. Instead of strengthening its hold over the network, the assemblage elected to self-regulate and declared never to go beyond 40%. Bitfury recognized that if they elected to continue expanding their authority across the network, Bitcoin’s value would decrease as users auctioned off their digital assets in anticipation of a 51% attack. In other words, if users lose faith in the blockchain network, the information on that network runs the risk of becoming worthless. Blockchain users can then only increase their computational power to a point before they begin to lose money. Blockchain’s Practical Application Blocks on the blockchain store data about monetary transactions, as previously discussed, but it turns out that blockchain is a reliable way of storing data about other types of transactions. Blockchain technology can be utilized to store data about property exchanges, medical records, school grades, lineage records, stops in a supply chain, votes for a candidate, and more. Deloitte, a professional services network, recently surveyed 1,000 companies across seven countries about integrating blockchain into their business operations. Their survey found that 34% already had a blockchain system in production today, while another 41% expected to deploy a blockchain application within 12 months. In addition, nearly 40% of the surveyed companies reported they would invest $5 million or more on blockchain in the coming year. Here are some of the most popular applications of blockchain use cases now. Bank Use Perhaps no industry stands to benefit from integrating blockchain into its business operations more than banking. Financial institutions only operate during business hours, five days a week. That means if someone tries to deposit a check on Friday at 6 p.m., they will likely have to wait until Monday morning to see the money in their account. Even if one makes deposits during business hours, the transaction can still take one to three days to verify due to the sheer volume of transactions banks need to settle. Blockchain, on the other hand, never sleeps. By integrating blockchain into banks, consumers can see their transactions processed instantly in the time it takes to add a block to the blockchain. Blockchain allows banks to exchange funds between institutions more rapidly and securely. In stock trading, the settlement and clearing process can take up to three days (or longer if banks are trading internationally), meaning the money and shares remain frozen for that time. Given the size of the sums involved, even the few days that the money is in transit can carry high costs and risks for banks. Santander, a European bank, estimated a potential savings of $20 billion a year. Capgemini, a French consultancy, estimates that consumers could save up to $16 billion in banking and insurance fees each year through blockchain-based applications. Blockchain Use in Cryptocurrency and Open Finance Blockchain is the bedrock of cryptocurrencies like Bitcoin. As we explored earlier, currencies like the U.S. Dollar are regulated and verified by a central authority, usually a bank or government. Under the central authority system, a user’s data and currency are at the whim of their bank or government. If a user’s bank collapses or is located in a country with an unstable government, the value of their currency may be at risk, which are the solicitudes out of which Bitcoin was produced. By spreading its operations across a network of computers, blockchain allows Bitcoin and other cryptocurrencies to operate without the need for a central authority. Decentralization abates risks and omits many of the processing and transaction fees central authority is heir to. It also provides nations with volatile currencies a more durable option, more applications, and a more extensive network of individuals and institutions they can do business with, domestically and internationally, when correctly implemented. DeFi, or ‘Open Finance’ Satoshi Nakamoto constructed a dream to make impervious money and modern-day transactions feasible to all, wherever they may reside. Today’s cryptocurrency community placed that dream into action when they saw the potential in an advanced, modern-day economy. The “DeFi” Decentralized Finance, also described as ‘Open Finance,’ evolution takes our dream into a tangible reality. Envision a world of independent people logging into an open alternative to every financial service utilized today: not just payment transactions, but savings, trading, insurance, loans, data storage, and much more. Blockchain projects, such as Ethereum, allow the “future of money” fantasy to breathe life into modern society with “Smart Contract” integration. states, “A smart contract is a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Smart contracts allow the performance of credible transactions without third parties.” Blockgeeks.com A smart contract program operating on a blockchain can execute transactions automatically when coded conditions are fulfilled. This system enables developers to construct far more sophisticated functionalities than simply receiving and sending digital assets. These programs are termed “DApps” or decentralized applications. DApps are applications built on decentralized technology rather than being constructed and regulated by a single, centralized entity or company. Technologists and economists envision a future filled with decentralized applications operating effortlessly while utilizing these protocols; however, these ecosystems have been thriving since approximately 2018. One can witness automated loans negotiated directly between two foreign entities globally, without the necessity for a central banking system. Some DeFi DApps exist to allow the creation of stablecoins. What distinguishes these DeFi DApps from their conventional banking institution or Wall Street equivalents? 2020 U.S. presidential candidate Michael Bloomberg issued a financial reform plan that, among other things, advocates for more robust and secure financial practices. Part of the recommended reform also advocates the formulation of a regulatory sandbox for startups and “providing a clear regulatory framework for cryptocurrencies.” Wall Street still regards the cryptocurrency industry with a measure of skepticism. However, stablecoins frequently provide a more direct route to mass-market adoption. Stablecoins have become a significant source of liquidity in the cryptocurrency market. They provide an on-ramp to enter the crypto markets and an off-ramp to exit. The rising popularity of stablecoins is a function of their inherent constancy relative to other cryptocurrencies. This piece addresses stablecoins being free from the traditional volatility of cryptocurrencies and how they are emerging to offer diverse varieties of on-and off-ramp possibilities that will make cryptocurrencies more attractive to Wall Street. Wall Street Testing Wall Street may not be entirely on board, but numerous equities, ETFs, and classical instruments implement some level of publicity to the industry. For example, Nvidia Corporation (NASDAQ: NVDA) and Advanced Micro Devices, Inc. (NASDAQ: AMD) produced vital exposure to the crypto syndicate related to the effectiveness of their graphics processors in crypto mining procedures. Both Nvidia and Advanced Micro Systems have delivered 23% in the year-to-date period. Other stocks such as Grayscale Bitcoin Investment Trust (GBTC) provide a more direct level of exposure to the crypto industry as a publicly-traded Bitcoin fund. Overstock.com, Inc. (NASDAQ: OSTK) is one of the first established corporations to embrace crypto installments. It currently keeps about 50% of these subsidies, which ties its destiny to the fortunes and vicissitudes of the crypto market. Consequently, both Grayscale Bitcoin Trust and Overstock have produced 56% and 25% increases in the year-to-date period. When compared to the rest of the U.S. market in the year-to-date period, Wall Street assets with exposure to the cryptocurrency market have delivered double-digit gains. In contrast, the S&P 500, NASDAQ Composite, and the Dow Jones Industrial Average have only managed to deliver single-digit gains in the same period. There is the rising popularity of stablecoins across different market segments. Tether USDT is the most sizable stablecoin on the market; it has experienced a first-mover benefit since its launch in 2015. Tether exceeds 80% of the market interest for stablecoins. It has managed to maintain parity with the U.S. dollar, despite the recurrent panic episodes that accompany significant drops in the price of Bitcoin. In 2019, the supply of USDT expanded from 2 billion tokens to 4.108 billion tokens to account for increasing adoption. Moreover, the token was relocated from the Omni-layer to the Ethereum network to expedite a quicker and more affordable transfer of value. Most strikingly, a Chainalysis summary shows that “for Chinese exchange users, Tether has replaced the yuan as the go-to fiat currency.” Data from exchanges showed that almost all fiat-crypto trades in Mainland China were amid Yuan and USDT. Dollar Neutrino USDN is an algorithmically stable USD-pegged digital asset. USDN is collateralized amidst the WAVES blockchain platform. WAVES is swiftly stimulating the adoption of DeFi commodities, and they are steadily maturing as a pilot in the business. In addition to contributing stability, USDN grants token holders additional revenue streams through staking, similar to traditional dividend stocks providing profits. Originating on January 28, 2020, within one month the token had more than $3.2 million staked, as it accouches a staking reward of roughly 8.9% per annum. Facebook intended to launch its cryptocurrency Libra, now known as Diem in 2020 but it was shelved until 2022. This occurrence resulted when firms in the European Union and the United States queried if this would be worthwhile to society. Facebook, the largest social media platform in the world, has 2.4 billion users during this time. While administration bodies globally struggle to comprehend blockchain, regulations, and laws on stablecoins are not being implemented expeditiously enough, according to the Financial Stability Board (FSB), a watchdog in global banking. In a February 2020 letter to finance ministers and central bank governors from the G-20 meeting in Riyadh, FSB Chair Randal Quarles voiced his concerns regarding how quickly digital currencies affect the global economy while regulatory action struggles to keep up. “FSB members recognise the speed of innovation in the area of digital payments, including so-called ‘stablecoins.’ We are resolved to quicken the pace of developing the necessary regulatory and supervisory responses to these new instruments.” As Wall Street and regulatory organizations continue to work on bridging the knowledge gap, sentiments like this accentuate the growing acknowledgment that these financial innovations are here to stay and will continue to expand as a bulwark for the future of the global economy. Want to read more on the history of fiat, crypto, Stablecoins and the future of money? You can check out , #1 on Amazon Financial Education & Computer and Science. Stablecoin Evolution Author Alyze Sam is a refreshing blockchain strategist, a novel educator, multi-award-winning author, serial co-founder, and a vehemently driven advocate. Sam wrote the first crypto dictionary and published the first books on stablecoins. Don Tapscott published her book ‘ ’ at The Blockchain Research Institute in January 2021. Sam’s newest book, ‘ ’ is currently the number one new release on Amazon in Computers & Technology. The Bad Crypto Podcast developed a Blockchain Hero NFT inspired by her work: After nearly losing her life a few times, Sam is a nurse and owns T with her best friends and soulmates, where they spend their days being grateful as they joyfully produce unbiased poetic technical education. Stablecoin Economy Stablecoin Evolution Mz. Stability. retired ech & Authors

The Complete History of Cryptocurrency in 2800 Words

In an era of rapidly changing technology and unexpected global catastrophes, the greatest challenge facing the insurance industry is its ability to keep up with consumer demand while being weighed down by its legacy. In February, Sophia Yen, EY Americas Insurance Strategy & Innovation Leader, wrote, "insurers experienced more change in the last 10 months than they had in the previous 10 years, with important shifts across the business. Many were long overdue and set the stage for a more disruptive future." EY had “found that the insurance industry has created only five new product classes during the last 30 years.”  There was now a need for insurers to reimagine “product development around the emerging needs of customers. To design better solutions, they must be systematic about embedding customer insight into product innovation.” According to Capgemini’s World Insurtech Report 2021, today's insurance customer wants what Capgemini dubbed its CARE equation: convenience, advice, and reach. The insurance industry is supported by decades-old technologies designed with proprietary code which cannot readily plug into many new eco-systems or interact with external interfaces. We have all seen how frustrated product managers become attempting to respond quickly to an opportunity, a crisis, or even regulatory changes on legacy systems. The costs and time required are serious limitations to innovation. Life Insurance and other long-term products are often quite complex, requiring complicated rules and transaction management. A product may have been created on a legacy system more than 20 years ago, the time and effort needed even to adapt to changing regulations becomes a real issue. Things are even more difficult when trying to meet the expectations of digital servicing for clients whose policies sit on these systems. As it stands, the insurance industry faces four critical operation issues moving forward with legacy systems: A diminishing pool of IT talent for maintenance and support results in increased costs. Decreasing support from system vendors, thereby threatening business continuity. Operational errors due to a lack of digital integration and manual data entry. The ever-increasing demands of regulatory compliance. How to Respond Insurers generally handle the legacy system issue in a predictable way, with the choices often boiling down to variations on refactor, replace, and preserve, and runoff: Refactoring is basically removing complex middle layers of the technology environment, sometimes unbundling some business process functions while leaving the central core. Preservation and run-off mean to wrap legacy systems with a new layer of modern technology to introduce new functionality or interactions while allowing the closed book of business to run itself off without adding any new business. It requires in parallel the introduction of a new greenfield system to handle new products and new business sales. Replacing the systems altogether, whether it is done in a Big Bang or as a phased approach, is often very expensive and risky, involving complex data mapping, transformation, and migration efforts that can take years to complete. A Better Way Ignatica helps insurers meet consumers’ demands with the minimum of business disruption. Our technology and methodology are tailored to our clients' needs. New products can be created, tested, and brought to market within days on our platform, which is designed to coexist side by side with the current legacy. As clients look to retire their legacy systems, we offer an efficient and low-risk approach to product migration that minimizes risk and improves time to value. A lot of opportunities are being left on the table, not because the concepts are unproven, or the risks are not understood, but because the price of implementation and the cost of administration is so huge insurers are forced to leave segments untouched and products not launched. Ignatica is confident that our suite of SaaS platform modules across product definition, policy admin, and data intelligence is extremely effective in responding to each market need and the challenges ahead. By Manuel San Miguel, CEO, and Co-Founder, Ignatica Manuel San Miguel is the CEO and Co-Founder of Ignatica, the SaaS core insurance platform which allows insurers to quickly launch products and automate their business operations. An insurance industry veteran in Asia, he is the former CTO for Manulife.

The Insurers' Dilemma: Balancing Consumer Demands and Legacy Conventions

To weather the COVID-19 storm and navigate the rapidly changing digital world, businesses are adopting at a faster pace. To help our clients sail through digital transformation and that deliver on their promise, we took up the challenge to identify AI trends and innovations that will define 2022. Here’s what we found. artificial intelligence create artificial intelligence solutions Factors Shaping AI Trends for 2022 Economy growth drives enterprise IT spending. Nearly two years after the onset of the pandemic, the global economy . The United States, China, Germany, and South Korea to pre-pandemic GDP levels per capita by the end of this year. Subsequently, worldwide IT spending this year (an 8.4% increase from 2020). Regarding AI spending trends, half of the businesses surveyed by Appen anything between $500,000 and $5 million in AI projects (34% up from 2020). The global AI spending, meanwhile, $83.5 billion. could accelerate 5.6% in 2021 are expected to recover is projected to reach $4.072 trillion are planning to invest could soon reach The global labor market sees a rapid change. Since Q4 2019, job openings in the United States , while the official number of last August. The paradox could be explained by the fact that COVID-19 has changed multiple industries, such as travel, hospitality, education, and healthcare. The pandemic-related health risks and policy rearrangements have only aggravated the unemployment crisis. In the meantime, the demand for skilled IT workers , reaching 26,800 new tech jobs in August in the USA alone (up from 10,500 jobs in July 2021). These factors could have a profound impact on artificial intelligence trends for the upcoming year. have surged 33% unemployed Americans reached 8.38 million has skyrocketed Cybercrime hits record high. Last year marked a drastic shift to online everything. Global eCommerce sales, for example, in 2020. The number of employees who use personal devices for work purposes . And before the pandemic, only 45% of executives security as an integral part of their companies’ strategy. As a result, the FBI last year than in 2019. The USA officials estimated the damage caused by different types of cyberattacks, including malware, distributed denial of service (DDoS), and phishing events, at a whopping $4.2 billion. Could artificial intelligence innovations possibly help enterprises lead through the cybersecurity crisis? grew by 27.6% has now reached 67% had regarded received 69% more cybersecurity complaints Enterprise struggle to implement AI at scale. Globally, businesses on artificial intelligence projects in 2020. Yet, just 11% of AI adopters on their AI investments. One of the common reasons for such lackluster results is a failure to get a buy-in from the C-Suite and scale AI innovations across the enterprise. According to a survey conducted by Alegion and Dimensional Research, 78% of all AI/ML projects between proof of concept (POC) and full-scale deployment. spent $50 billion saw a sizeable return come to a halt The environmental crisis is worsening. Recent reports indicate that Earth compared to preindustrial times. The temperatures could further rise by up to 2℃ in the next 30 years, causing irreversible damage to agricultural land, forests, and biological systems. By 2050, climate change could of the global economy’s total GDP. It’s high time enterprises leveraged AI advances to address climate change. has already warmed by 1.1℃ wipe off up to 18% National AI strategies emerge. As more economies recognize AI’s potential to boost growth, resilience, and productivity across public and private sectors, national artificial strategies are beginning to take shape. A total of 50 countries, including the United States, China, and the United Kingdom, in the works. The , for example, will focus on fostering and attracting AI talent, addressing the semiconductor shortage, battling cybercrime, and ensuring the country’s leadership in the artificial intelligence race. Through 2021, the Biden administration could pour $6 billion into artificial intelligence projects and initiatives, influencing AI trends for decades on end. have national AI strategies US artificial intelligence strategy Business AI Innovations and Trends to Watch in 2022 ROI-driven AI implementation AI-augmented automation Rise of responsible AI Use of AI in cybersecurity Environmental AI innovations AI Trend #1: More Companies Could Adopt an ROI-Driven Approach to AI Development “Launching pilots is deceptively easy but deploying them into production is notoriously challenging… Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated.” — Chirag Dekate, Senior Director Analyst at Gartner Some of the common reasons for AI projects’ unclear financial outcomes include: Limited access to quality training data A lack of resources and skills to implement artificial intelligence at scale A tendency to overcomplicate things — for instance, by using deep neural networks where simpler yet highly effective machine learning techniques could produce similar results A failure to gain buy-in at the executive level and proceed to a full-blown project Here at ITRex, we believe that one of the biggest artificial intelligence trends for 2022 (and the years after!) will be taking an incremental, ROI-driven approach to AI development. The concept of ROI-driven AI engineering and deployment pivots on: Fostering collaboration between business and IT teams, which could help companies decide on the business outcomes of deploying AI innovations beforehand and ensure C-Suite support Selecting a limited number of use cases for an AI pilot while devising a broader implementation plan early on Upskilling existing IT teams and enlisting the help of experienced artificial intelligence companies — either for turnkey AI system engineering or , such as discovery, cleansing, and labeling data preparation tasks Utilizing a powerful combo of SaaS, open-source, and custom-made AI solution components to speed up development cycles Ditching vast piles of historical data, which has become less relevant since the COVID-19 outbreak, in favor of wide and small data; the former is aggregated from a variety of sources, while the latter is easier to interpret and delivers instant insights Learning from failures, retraining algorithms on new data, and creating a continuous loop for machine learning model redeployment AI Trend #2: Enterprises Will Aim for AI-Augmented Automation “Last year, IT spending took the form of a knee-jerk reaction to enable a remote workforce in a matter of weeks. As hybrid work takes hold, CIOs will focus on spending that enables innovation, not just task completion.” — John-David Lovelock, Research Vice President at Gartner In the pandemic-ridden world, the role of information technology has shifted from supporting business operations to delivering business value. This change has had a profound impact on AI innovations and trends. A breakdown of AI use cases in business. Source Fueled by 5G rollouts, more powerful computer chips, the diminishing sensor prices, and flexible , the convergence of , artificial intelligence, and the technologies has become a viable reality. This powerful trio brings companies one step closer to and pervasive intelligence. cloud services robotic process automation (RPA) Internet of Things (IoT) AI-augmented automation From lightweight RPA robots that to self-checkout solutions that , recent advances in artificial intelligence will help enterprises achieve resilience and significant cost reductions. Here are some stats to prove it: take over payment processing tasks in healthcare know precisely which items a customer has put in the basket 80% of executives are their business process automation efforts (World Economic Forum) currently accelerating 25% of companies in workflow automation, while 51% of enterprises are planning to do so shortly (Salesforce) already use AI By 2023, 40% of infrastructure and operations (I&O) teams will use in large enterprises, freeing up IT personnel’s time for strategic work (Gartner) AI-augmented automation 66% of businesses by deploying artificial intelligence systems (McKinsey) have increased their revenue Through 2021, enterprise AI budgets have increased, with 74% of companies or more for their artificial intelligence projects, which marks a 55% increase from 2020 (Appen) allocating $500,000 AI Trend #3: Responsible AI Takes Center Stage “Responsible AI helps achieve fairness, even though biases are baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, while grappling with AI’s probabilistic nature.” — Svetlana Sicular, Research VP at Gartner In their recent , David De Cremer and Garry Kasparov described AI-based machines as “fast, more accurate, and consistently rational,” noting, however, that present-day AI innovations often lack intuition, emotional intelligence, and cultural sensitivity. article for Harvard Business Review While end-to-end automation and subsequent workforce reductions are not yet among the artificial intelligence trends for 2022, governments will need to address the moral implications of artificial intelligence adoption in their national AI strategies, ensuring positive automation outcomes for all parties involved. When it comes to business AI, companies should focus on creating intelligent systems that and based on their ethnicity, age, gender, religion, or place of residence. Additionally, stakeholders need to clearly communicate automation benefits to get employees on board and encourage them to participate in AI deployment and training. explain the rationale behind their decisions do not discriminate against people These numbers indicate that ethical, responsible artificial intelligence usage will be one of the defining AI trends for the upcoming year: Through 2022, every company could have in development (CIO, Gartner) 35 AI projects 65% of companies how their AI systems make decisions and predictions (FICO, Corinium) cannot explain 78% of enterprises are poorly equipped to consider the ethical implications of using artificial intelligence (FICO, Corinium) Only 45% of businesses engage in ethical AI monitoring (FICO, Corinium) By 2023, all AI professionals a good grasp of responsible AI principles to secure a job (Gartner) will have to demonstrate AI Trend #4: Artificial Intelligence Will Lead the Cybersecurity Revolution “We’ve seen a lot of the ransomware attacks and what it’s done. And in particular, it has impacted business resiliency. It’s no longer the case of encrypted computer and, you know, Reimage, and carry on. It’s impacting massive amounts of business and costing hundreds of millions of dollars.” — George Kurtz, CEO at CrowdStrike Holdings, Inc. Using intelligent algorithms for cyberattack prevention became one of the most significant AI innovations of the past years. Forward-thinking enterprises leverage artificial intelligence to detect suspicious traffic within corporate IT networks, identify malicious software programs, spot infected links in employees’ emails, and even model cyberattack scenarios based on vulnerability assessments. The trouble is, the other side (i.e., cybercriminals) can do the magic too. For example, hackers can now manipulate the data used for AI model training, causing adversarial attacks. Other AI breakthroughs in the cybercrime field include inference, which allows attackers to access sensitive data by reversing AI engineer systems, and advanced social engineering techniques driven by behavior analytics. Additionally, hackers may employ AI to pinpoint security vulnerabilities in corporate IT systems. To address these looming threats, companies looking to deploy AI innovations should closely monitor all the data presented to AI models, review real-world data used for model training, and build the elements of randomness into their artificial intelligence models. Below you will find several statistics shedding light on artificial intelligence trends in the cybersecurity domain: Over use artificial intelligence to detect cybersecurity threats (Capgemini) 51% of enterprises Through 2021, 30% of organizations have already fallen victim to adversarial attacks — compared to just 12% in 2020 (FICO, Corinium) Hacking, customer fraud, and data breaches have grown in 2021 (FICO, Corinium) by 5–12% Automated cybersecurity solutions could increase the global revenue opportunities in the next five years (Accenture) by $5.2 trillion Between 2020 and 2027, the global AI cybersecurity market could be growing at a , topping $46 billion by the end of the given period (Meticulous Market Research) CAGR of 23.6% AI Trend #5: Artificial Intelligence Will Drive Sustainability in Enterprises “We cannot reduce what we cannot measure. Corporations ‘ climate action does not match our ambitions: one of the main reasons is that corporations don’t accurately measure their climate impact and track it enough over time. Fortunately, tools and techniques to help us exist, one of the most powerful ones is artificial intelligence.” — Charlotte Degot, Managing Director and Partner at BCG GAMMA Through 2022 and beyond, governments and businesses alike could turn to artificial intelligence to reduce reliance on fossil fuels, combat deforestation, and cut CO2 emissions. And while green AI innovations are still scarce at national levels, we’ve seen several commercial AI deployments that assist businesses in achieving their sustainability goals. Google, for instance, coupled their data center cooling technology with deep learning and reduced energy consumption . And CO2 AI, a novel sustainable artificial intelligence solution by Boston Consulting Group, helps enterprises by accurately measuring their carbon footprint and making intelligent recommendations. by 40% cut CO2 emissions by 30–40% These numbers prove that environmental sustainability is one of the hottest business AI trends for 2022: Artificial intelligence helps companies by up to 10% (BCG) reduce greenhouse gas (GHG) emissions The use of AI for GHG emission monitoring could result in $208–424 billion savings for global companies in the coming years (BCG) Artificial intelligence could in achieving 93% of the environmental sustainability goals defined by the United Nations (Springer Nature Sustainability Community) aid the world The growing adoption of sustainable AI systems could create up to globally (PwC) 38.2 million jobs The economic impact of AI-driven environmental solutions could reach $5.1 trillion by 2030 (PwC) If you need help navigating the emerging artificial intelligence trends or implementing AI innovations, please ! Together you’ll build a brighter, safer, and greener future! contact the ITRex team Also published . here

Top 5 Business AI Innovations and Trends to Watch in 2022

This is the first post in our series, “Getting APIs on the Blockchain”. There is a famous David Foster Wallace commencement speech titled “This is Water” where he offers a little parable of fish discussing water and briefly explicates the title with: “The most obvious important realities are often the ones that are hardest to see and talk about.” In the first blog post of this series, we will be talking about APIs.¹ APIs are everywhere — they permeate our digital world. The concept of an API is so inescapable in computer software that it’s ironically difficult to appropriately define it. To further this difficulty, the term (API) has superseded its original meaning on quite a few occasions. Application Programming Interface What is an API? (Or: a very brief history of APIs) One could argue that the concept of an API was independently discovered several times, its early history being intrinsically linked to the instantly ubiquitous concept of a . That is, it’s difficult to discuss APIs separately from the (very fundamental) concepts of and in computer science. subroutine library modular programming abstraction This idea of a subroutine library was first formalized in 1948 by Herman Goldstine and John von Neumann in their widely-circulated, multi-volume treatise : Planning and Coding of Problems for an Electronic Computing Instrument² The importance of being able to do this [permit ] is very great. It is likely to have a decisive influence on the ease and the efficiency with which a computing automat of the type that we contemplate will be operable. This possibility should, more than anything else, remove a bottleneck at the preparing, setting up, and coding of problems … we envisage that properly organized automatic, high speed establishment will include an extensive collection of such subroutines… I.e. a “library” of records … in memory. the use of a coded sequence of a problem as a single entity, when that problem occurs as part of a more complicated one Essentially, Goldstine and von Neumann argued that most programs would have to utilize existing subroutine libraries in order for computers to grow to the capacity envisaged at the time (and would otherwise, due to errors and other issues, “be quite dangerous”). Subroutines were proposed as an essential method for speeding up development and decreasing errors by way of decreasing the amount of new code to be written. The treatise, unfortunately, didn’t offer much in the way of implementation specifics, instead opting for more of a theoretical and high-level description of subroutine construction and use. In a well-known presentation delivered by Joshua Bloch entitled “A Brief, Opinionated History of the API,” Bloch bluntly puts it: “they were peddling vapourware”.³ Goldstine and von Neumann’s preparatory routine … was a surprisingly pedestrian piece of work. It would have required extensive operator intervention and it is difficult to imagine that it would have ever been a satisfactory procedure in practice. — from Theory to Practice: The Invention of Programming, 1947–51 That is why, in that same presentation, Bloch opines that Maurice Wilkes, David Wheeler, and Stanley Gill are the true creators/discoverers of the API. Or — more accurately — the proto-API, given that the term was never actually used by them. The researchers built the EDSAC (an early British computer) and later authored Preparation of Programs for Electronic Digital Computers in 1951, in which subroutine libraries were effectively introduced.⁴ A few months after writing the initial orders and upon completing his first “real program”, Wilkes would write: By June 1949 … I was trying to get working my first non-trivial program … the realization came over me with full force that a good part of the remainder of my life was going to be spent in finding errors in my own programs. people had begun to realize that it was not so easy to get programs right — from Memoirs of a Computer Pioneer, MIT Press, 1985, p. 145 Wilkes’ fear of errors in his own programs largely inspired his development of subroutines as an organizing principle. The first time the term Application Programming Interface (API) was actually used was in 1968, in a paper⁵ about designing remote computer graphics libraries. In the abstract, the authors state: “it has been adequately demonstrated that computer graphics systems need not require the dedication of a large scale computer for their operation. Computer graphics has followed the trends of computing in general, where … has become a key phrase”. That is, the authors note that (by the late 1960s) it became clear to computer scientists that computer graphics systems . (The authors address how to properly interface with such systems in their paper.) remote access encapsulated and modular made more sense When searching for “Application Programming Interface” in Google Scholar for the years 1970–1995, we find the term used in numerous contexts (far beyond a simple wrapper for a subroutine): APIs for interacting with external systems (e.g. storage devices) APIs for accessing network functions nd, an extensive literature on APIs for databases By the early 1990s, APIs quickly evolved to very generally denote the communication layer between software abstractions (local or remote). Indeed, one definition from 1990 states an API is “a set of services available to a programmer for performing certain tasks”.⁶ Another from 1993 states an API is “a term for any language and format used by one computer program to help it communicate with another program”.⁷ The notion of an API expanded its meaning yet again with the dawn of web APIs. As opposed to what we just previously discussed—APIs that can be roughly categorized as and needing to be hardware/software compatible with whichever system they are used by — APIs are interfaces that connect publicly exposed endpoints of a network, typically via a predefined message-passing system and most commonly accessed using the HTTP protocol. library APIs web In his thesis (in which Representational State Transfer⁸ is introduced), Roy Fielding distinguishes between APIs and : library-based network-based APIs The design of the Web had to shift from the development of a reference protocol library to the development of a network-based API, extending the desired semantics of the Web across multiple platforms and implementations. Basically, . It didn’t matter what kind of machine the service operated on or which language(s) it was implemented in, it now only mattered that it’s services were accessible via the web (most commonly by means of an HTTP-based web server). Today, a web API is the most common connotation of “API”. network-based APIs offered a further abstraction from library-based APIs, because the implementation of the interfaced service was further abstracted and black-boxed from the interfacing user The earliest uses of web APIs were for sales and commerce management.⁹ SalesForce launched the first web API in 2000 under the umbrella of arguably the first SaaS product. Later in the same year, eBay launched the eBay API in order to “openly provide the tools that developers need to create applications based on eBay technology”¹⁰ by way of standardizing how applications would interface with eBay (since developers were already hacking away at eBay’s website and using the data in their own applications). In 2003, del.icio.us — an early social media site — offered users a simple API for sharing and tagging their internet bookmarks. Users had read/write access to bookmarks on del.icio.us via REST-like urls (e.g. http://del.icio.us/tag/[tag name]). In 2004, Flickr launched their API which allowed users/developers to, among other things: upload images, manage their “favourites” lists, query for users, and search photos. The launch of the API was motivated by the fact that Flickr couldn’t keep up with demands for their services and “established the API as a self-service way to deal with business development” which ultimately resulted in “Flickr quickly becom[ing] the image platform of choice for the early blogging and social media movement by allowing users to easily embed their Flickr photos into their blogs and social network streams”.¹² The launch of the Flickr API was the catalyst for “BizDev2.0”. … a small startup … wanted an introduction, but I said we (that is, Flickr) probably didn’t have the space on our schedules to engage with them in any meaningful way, but that they should feel free to apply for a Commercial API key and build something off the API. “BizDev 2.0” I call it. — Caterina Fake, co-founder of Flickr In 2006, Facebook, Twitter, and Google followed suit, launching their own APIs in hopes of encouraging BizDev2.0 on their respective platforms. And then came cloud computing … Amazon S3 is storage for the Internet. It’s designed to make web-scale computing easier for developers … It gives any developer access to the same highly scalable, reliable, fast, inexpensive data storage infrastructure that Amazon uses to run its own global network of web sites. The service aims to maximize benefits of scale and to pass those benefits on to developers. — from the AWS press release (March 14, 2006) The capability of web APIs did a quantum leap with the introduction of Amazon Web Services (AWS). AWS and its three initial offerings (S3, SQS, and EC2)¹² launched in mid 2006. This was the dawn of cloud computing. Amazon Web Services succeeded in abstracting away much of the day-to-day work of software engineers, making essential and complex services (like computing, storage, networking, and deployment) available via simple web API calls (albeit for a fee). With AWS, a developer could set up and deploy a server (for example) in minutes, something that would have previously taken days or weeks. Cloud computing quickly revolutionized the way developers build applications on the web. Where are we now? The role of software engineering has changed quite a bit, and coding isn’t even a huge part of it anymore … You know how in the old days people used to write in assembly code? And then we abstracted upon that, so more people started writing C++? And then people started abstracting upon that, and writing Java. And now we have Python, and we’ve abstracted upon that too… Most top apps [these days] support so much functionality, that it’s pretty much beyond the knowledge of a single person to handle all of those anymore. Now programmers are gluing pieces together through API endpoints and frameworks. — Ex-Facebook/Ex-Google tech lead and famous YouTuber, TechLead¹³ Nowadays we take for granted that software applications can and do communicate with each other with relative ease via each other’s APIs. Developers can now build complex applications at a rate never before seen. Integrating existing services to their applications through APIs has allowed developers to build increasingly complex and capable applications, which has led to the rise of giant Web services and mobile applications. Because of this, APIs are often referred to as the glue that holds the digital world together. Providing in-demand APIs has become quite the lucrative business, by way of businesses monetizing their data and services. As a result**, the concept of an API has transcended its meaning yet again.** The term no longer refers to the technical implementation of an interface, but to a full-fledged product that often includes the service it wraps.¹⁵ Global enterprises that provide APIs generate 25% of their organizational revenue from APIs on average.¹⁶ Companies such as Salesforce, Expedia, and eBay have reported to generate the majority of their revenue through APIs¹⁷, and even entrenched industries such as banking are expected to be disrupted by this movement.¹⁸ We are now at the cusp of fully API-centric business models.¹⁹ As APIs continue to encapsulate increasingly higher and higher abstractions of software services, the meaning of “API” will continue to evolve. What’s to come with the rest of this blog series? You might be wondering at this point when are we to address topics suggested by the second half of our series title (“… on the Blockchain”). Well, there is a very critical problem we introduce in our next blog post called “The API Connectivity Problem” which will take the remainder of this series to fully explicate and map out a solution to. Briefly, existing APIs are not natively compatible with decentralized applications — a blockchain network is essentially walled-off from communicating with these systems and protocols that permeate the rest of our digital world. How do we bring these interfaces onto the blockchain? How do we get blockchain applications to safely interact with the richness offered by existing APIs? Stay tuned for the rest of our series! Footnotes
[1]: Of course, Wallace’s speech was tender and moving whereas we’ll just be discussing APIs (for now), so please excuse the abrupt change in tone…
[2]: Goldstine, Herman Heine, John Von Neumann, and John Von Neumann. “Planning and coding of problems for an electronic computing instrument.” (1947).
[3]: [4]: Wheeler, Maurice V., and Stanley Gill. The preparation of programs for an electronic digital computer: with special reference to the EDSAC and the use of a library of subroutines. Addison-Wesley, 1951.
[5]: Cotton, Ira W., and Frank S. Greatorex Jr. “Data structures and techniques for remote computer graphics.” Proceedings of the December 9–11, 1968, fall joint computer conference, part I. 1968.
[6]: Malamud, Carl. Analyzing Novell Networks. John Wiley & Sons, Inc., 1991.
[7]: Bardige, Stephen I. “A Glossary of Computer Terms and Definitions.” Judges J. 32 (1993): 72.
[8]: REST is a very common software architecture style that defines a set of constraints to be used for creating web services.
[9]: [10]: [11]: [11]: [12]: S3, SQS, and EC2 are Amazon’s storage, queue, and compute services offered remotely via API (also known as “cloud computing services”).
[13]: [14]: MuleSoft, “Connectivity benchmark report,” 2020.
[15]: Collins, G., and D. Sisk. “API economy: From systems to business services.” TechTrends (2015).
[16]: MuleSoft, “Connectivity benchmark report,” 2019.
[17]: Iyer, Bala, and Mohan Subramaniam. “The strategic value of apis.” Harvard Business Review 1.7 (2015): 2015.
[18]: R. Narain, A. Merrill, and E. Lesser, “Evolution of the API economy: Adopting new business models to drive future innovation,” IBM Institute for Business Value, 2016.
[19]: Capgemini, Efma. “World FinTech Report 2019.” (2019). https://www.infoq.com/presentations/history-api/ https://history.apievangelist.com/ https://www.ecommercetimes.com/story/5403.html https://history.apievangelist.com/#Social https://history.apievangelist.com/#Flickr https://youtu.be/yzhnRt6ZDKM Previously published . here

What is the Genuine Value of APIs

The manufacturing sector faces numerous challenges, including difficulties in forecasting demands, skilled worker shortage, and keeping the equipment up and running. Simultaneously, this sector generates plenty of data, which makes employing artificial intelligence in manufacturing a given. Many organizations have already realized this and started implementing to enhance operations. Markets and Markets expect the global AI in the manufacturing market to reach , growing at a CAGR of 57.2%. AI-powered business solutions $16.7 billion by 2026 Interested in finding out how AI can help your company and which steps to take for successful deployment? Then keep reading this article. Why Investing in AI Makes Sense for the Manufacturing Industry? According to a recent study by Deloitte, the manufacturing sector is ahead of other industries when it comes to data generation. Artificial intelligence technologies possess an unmatched ability to analyze large amounts of data, so it is only natural for manufacturers to adopt this technology. Confirming the above, a recent published a graph showing the percentage of AI-enhanced business processes among different industries. Manufacturing is proudly located towards the top of the list, second only to the financial services sector. MIT Technology Review So, How Can AI Improve Manufacturing Efficiency? Minimizing or even preventing equipment outage. AI-powered software can spot malfunctioning in factory devices before it causes actual damage and delays production. Enforcing quality standards. AI in manufacturing can monitor items on the production line, identify defects, and prevent low-quality products from reaching the market. Supporting human employees. AI assistants can change the manufacturing sector by freeing up a considerable amount of employees’ time. AI can take over routine inspections and . This technology can even perform creative tasks, such as generating product designs. automate repetitive duties Top 5 AI Examples in Manufacturing Artificial intelligence enables predictive maintenance. AI forecasts demand and raw material prices AI supports generative design Artificial intelligence helps build digital twins AI inspects product quality 1. Artificial Intelligence Enables Predictive Maintenance Predictive maintenance is in the manufacturing industry. one of the most funded applications of AI Equipment fault can cause significant disruptions, delays on production lines, and increase production costs. One minute of downtime at large factories can . Additionally, regular diagnostics by human experts are relatively expensive. cost as much as $20,000 AI-powered solutions analyze equipment’s historical performance data to spot anomalies and predict when it will need maintenance before it malfunctions or comes to a halt. This allows employees to choose a suitable time for fixing the device instead of stopping everything in the middle of the production process when this machine is out of service. General Motors gives one example of AI implementation in manufacturing. The company mounted cameras on its assembly robots and trained AI algorithms to analyze the data streaming from these cameras to identify signs of component malfunctioning. In a pilot test of this solution, it and identified 72 instances of component damage before they resulted in an unplanned outage. worked on 7,000 robots 2. AI Forecasts Demand and Raw Material Prices Forecasting Prices of Raw Material Raw materials costs are volatile in nature. When manufacturers have this information in advance, they can adapt their operations to minimize expenses. A UK-based startup uses machine learning to forecast price fluctuation of raw materials, such as aluminum, oil, and copper, among others. The company was founded in 2017, and it in seed financing in 2020. ChAI targets Fortune 100 companies, including manufacturers, who rely on these materials as a part of their supply chain. ChAI secured €1.5 million Predicting Demand AI analyzes behavioral patterns, socioeconomic data, location, and weather forecast to determine which products will be in demand, allowing manufacturers to focus on what matters and cease producing items that no one would purchase. AI can even predict which product will be a hit before they go to the market. Danone deploys machine learning in manufacturing to foresee variability in demand and adjust its production plan accordingly. Thanks to this approach, the company . decreased its lost sales by 30% 3. AI Supports Generative Design Generative design is a program that relies on to mimic a human engineer’s approach to designing products. Engineers feed different design parameters, such as size, materials, and cost constraints, into generative design algorithms, which generate different design options for one product. This method allows manufacturers to create hundreds of alternative designs for one item and experiment with how adjusting parameters reflect on the outcome. A human designer would not be able to come up with so many ways of building one item. AI technologies The resulting designs can be further tested using machine learning to determine which options work best. Considering AI’s recommendations, a specialized workforce will select the design they want to pass to the development stage. For example, Nissan experimented with letting AI propose car designs hoping it would come up with something different. , their algorithms put forward a design that no one has ever seen before. It was not perfect, but it’s a good start. According to the company AI and ML in manufacturing can also assist designers with user experience. Typically, designers try to imagine possible ways the user might use a particular product. With its learning potential, AI can analyze data on how people utilize such products historically to come up with optimal designs. 4. AI Helps Build Digital Twins Products IBM as a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making. To set up a digital twin, you need to collect data from sensors attached to the physical item and project this data onto the digital twin. This way, when you look at the virtual item, you can see what is happening to its real-world counterpart. defines a digital twin For example, if you create a plane engine’s digital twin, it will receive data from the real engine upon landing and takeoff. You will be able to evaluate the condition of the actual engine by examining the digital twin. Researchers can use this technology to conduct simulations and anticipate malfunctioning. Manufacturers can also use digital twins to make design modifications tailoring to customer preferences. Manufacturing Processes Digital twin technology is not limited to products. You can create a digital twin of the whole production line to optimize the manufacturing process. You will need to position sensors along the production line and use the generated data to analyze performance indicators. Unilever partnered with the Marsden Group and used Microsoft Azure to set up eight digital twins of its factories. Algorithms embedded into the digital twins can suggest improvements to production based on the data they receive. For instance, one of the digital twins analyzed the shampoo production process data and could predict the correct order of processes to get the best batch time. Also, using this technology, Unilever reduced the number of production-related , freeing up operators’ time. alerts by 90% 5. AI Inspects Product Quality To make sure that products are up to par with quality standards, manufacturers use in-line visual inspection. However, it is time-consuming for human employees to examine all products manually. Cameras, , and other AI technologies for manufacturing can perform a fast inspection in real time, detecting flaws at the earlier manufacturing stages allowing engineers to make adjustments before the product can cause further delays. computer vision Audi installed an image recognition system at its Ingolstadt press shop to capture and evaluate the quality of pressed sheets. This AI-powered system was trained on millions of test images and can identify even the finest cracks that could easily escape the human eye. Another example of AI in production comes from a large food processing organization, which produces over 200,000 eggs per hour. Human operators used to inspect these eggs employing the sampling method, but it was prone to errors as inspectors couldn’t spot every damaged egg. Realizing this problem, the company switched to an AI-enabled quality control system. It was trained to identify several defects, including holes, leakage, and cracking in eggshells. This innovative solution can scan one egg in and spot any of the classified defects. less than 40 milliseconds 8 Steps to Successfully Implement AI in Manufacturing Recently, Deloitte surveyed the manufacturing sector. The respondents confessed that to meet timely expectations. There are things that you can do to minimize the chances of your AI project joining the deck. 91% of their AI projects failed 1. Align AI With Strategic Objectives It is best if the AI applications you are planning to adopt are in line with your business goals, be it cutting down costs, finding new revenue streams, increasing operations efficiency, etc. This tactic will ensure that business units are involved. AI efforts also need to match your established business goals timeline. Before employing a more advanced AI in the manufacturing industry, check if your schedule can handle the likely delays. Highlight the business goals you want to achieve with AI in manufacturing and specify how to measure improvements. For example, increasing operations efficiency by reducing equipment downtime by 20%. It can help compose a roadmap with the business applications where you want to use AI in the short, mid, and long terms. 2. Prioritize Use Cases Even if you have ambitious plans regarding AI, it is a good practice to start with a few carefully selected use cases. As the company’s capabilities and experience grow, it can expand its AI in manufacturing efforts to more applications. You can prioritize use cases based on their feasibility, total value, and time needed to achieve this value. In his interview with Capgemini, Luis Miguel del Saz Rodriguez, Head of Digital, Design, and Manufacturing Services at Airbus, explained how he approaches use case selection at his company: “First, we organize a team workshop where we discover the pain points and the opportunities. We also consider the scale and impact in the business. Next, we take these pain points, or opportunities, and work on the digital solutions, analyze budget and the associated business case.” 3. Organize Your Data Data is the main foundation of any AI-related endeavor. Your system needs to be able to capture data from different sources in various formats. It must be clean and accessible. Discrepancies are large between what companies want and what they can afford data-wise. In its recent study, Forrester Research discovered that decision-makers view deriving insights from data as a business priority, while 91% described this task as rather challenging. Before starting with AI in manufacturing, it is advisable to examine your data and determine your level of maturity. This will show which opportunities you can explore with AI and prevent you from targeting solutions that your data foundation can’t adequately support. 90% of the surveyed 4. Think About Integrating AI in Manufacturing Solutions With Legacy Systems You probably have some legacy manufacturing systems, such as enterprise resource planning (ERP) and product lifecycle management apps that can generate valuable data. Discuss with your vendor the possibility of integrating such software in your AI solutions. You can consider placing a standardized equipment purchase policy. Neeraj Tiwari, Director of Manufacturing JV Organization at Fiat Chrysler, how this is done at his company: “We have a centralized process for purchase of equipment, their subsystems, and associated software. This brings a level of standardization and makes integrating AI applications much easier and results in far fewer issues.” explained It is also a good practice to examine your manufacturing devices and attached sensors. Some of them might be generating data in formats that you cannot use. Forrester Research Analyst, Paul Miller, about such equipment: spoke “Many [devices] may have been in use for a decade or more, and they either have no sensors at all or they have proprietary sensors that send commercially sensitive data in proprietary formats, which can be hard to decode.” Miller also added that such problems have a solution. Some companies sell specialized sensors that manufacturers can fit into their old devices if they know what they want to measure. Deloitte highlights five data maturity levels: Level 1: key business data is lacking Level 2: the basic data is available but located in isolated data silos Level 3: the data is highly integrated but can’t support decision-making activities Level 4: it is possible to make data-driven decisions, but the system can’t reflect real-time changes Level 5: all the previous points, in addition to supporting real-time recommendations If your data is not at the maturity level you need to support AI; it is worth investing in a reliable data foundation. It is paramount for the long-term success of AI and will allow you to roll out new AI-powered applications in the future. Furthermore, you might want to establish strong . This includes determining: data governance practices Which business functions and devices generate relevant data Who is the data owner How this data is stored Which data formats are acceptable 5. Recruit Talent and Build Expertise When moving towards machine learning and AI in manufacturing systems, you will need to hire people with specific analytical skills. Limiting talent search to might not suffice. Your organization will need other specializations, such as data engineers and data stewards. Also, make sure your data experts collaborate with internal domain experts who have a deep understanding of the business problems AI in manufacturing is intended to solve. Some companies initiate upskilling programs for their in-house employees by teaming up with academia and startups. data scientists Manufacturers typically begin with fragmented uses of AI experts and slowly move to more coordinated centralized efforts. Some end up establishing AI labs or centers for excellence, which will define best practices of using AI in the company. 6. Conduct a PoC and Scale Up Your AI solution When your data is at the desired maturity level, run a proof of concept with your vendor of choice. This will help you better understand what to expect and what you still can fix before a large-scale adoption. Do not forget to integrate AI solutions into the end users’ workflow. According to McKinsey’s research, overlooking this step is to AI adoption. one of the major obstacles 7. Monitor AI Algorithm’s Performance When your AI solutions are fully up and running, it is advisable to keep monitoring the results. Assign dedicated staff members to make sure that ML in manufacturing is delivering on expectations, and if not, find out why and what to do to improve the situation. Also, someone will need to adjust AI to any change in your operations. AI algorithms will need retraining with new data categories. Or, if you installed the AI system in a different location, it might need to be retrained with location-specific data. 8. Be Patient and Adjust Your Expectations Both your employees and AI need to learn how to do their job together optimally. There is a good possibility that this technology will produce false results frustrating everyone involved. Especially people who were not that excited about adopting artificial intelligence in manufacturing. Siddharth Verma, Global Head and VP  —  IoT Services at Siemens, shared his AI adoption experience with Capgemini. Here is what he said: “In the early days, when the accuracy of the system was low, it predicted a few failures which turned out to be false alarms. At these points, it is important to remind everyone that it is a prediction which has a probability of being right or wrong. As accuracy improved, the system was able to predict many failures in advance and saved a lot of cost and downtime, proving its worth.” Want to use AI to enhance your manufacturing operations? They will help you build the right solution and integrate it into your existing system. Contact ITRex AI experts!

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In the short term, COVID-19 has unquestionably changed our lives. The virus has spread and mutated exponentially, leaving little time for corporations, organizations, and countries to adapt, survive, and prosper. As we all try to cope with the disruptions the outbreak causes in our daily life, societies, and companies, the COVID-19 pandemic also has massively driven individuals, organizations, and governments to digitalization and digital platforms. Learn about Mobile App Industry Amidst COVID-19 Mobile apps here have taken center stage. They have shown the potential to reinforce a variety of innovations to keep the world running at a time when corporations are stagnant in a climate of uncertainty and difficulties. Hundreds of different mobile apps helped us bravely deal with the outbreak and educated us to embrace the new normal. Downloads in Mobile App Stores App downloads have increased steadily over recent years, according to stats released by Sensor Tower. The total number of globally downloaded apps per quarter has doubled in the five years since 2015, reflecting both increased smartphone penetration and the highly powerful role of apps in our lives. We saw a sharp spike in app downloads in the first two quarters of 2020, with Q2's total of nearly 38 billion app downloads representing a 9 billion rise over Q4 2019; an increase of almost a third (to be precise, 32 percent). On the left side, you can see country-levels downloads and on the right side worldwide downloads. Source: Sensor Tower User Spending on Mobile Apps is Rising During the Lockdown User spending on mobile apps is expected to exceed 171 billion dollars worldwide by 2024, more than double that of 85 billion dollars in 2019. That figure is the result of continuous growth of 17-18 billion annually over half a decade, which could be even greater, given that the impact of COVID-19 could be more unpredictable than we anticipated. One thing for certain: in the next 5-6 years, it won't reduce user spending. Source: Sensor Tower It is impressive, however, that even the slowest-growing regions in both app stores, Apple's App Store and Google Play, will see revenues by 2024 that are more than 80%higher than their 2019 levels. Over the next five years, app stores will also go through several milestones. For example, for the first time in 2020, global spending on mobile apps will hit $100 billion, growing to $102 billion year-over-year to about 20%. Mobile App Testing Trends Influenced by the Outbreak It is important to note that the demand for testing services for mobile apps is estimated at $14.7 billion by 2026 and significant growth in revenue is expected to occur between 2016 and 2026. A recent study published by an eminent market research analyst shows that a remarkable 19.5%CAGR value will be reported by the market for mobile app testing services between the forecast period (2016 to 2026). At the same time companies are not well equipped to cope with the growing complexity and variety of testing multiple mobile networks, devices, and services, according to Capgemini. The company announced that there was a change in focus from traditional 'functional testing' to safety testing, performance testing, and compatibility in the mobile testing solution. Therefore, the industry is expected to depend increasingly on testing services as services provide businesses with the opportunity to maintain high quality while reducing cost and time-to-market. This has driven the growth of crowdsourced testing in 2020 as it allows businesses of any scale to leverage the power of experienced testers who have access to virtually unlimited devices at any time and in virtually any place. And in addition, as "remote" becomes the latest trend, it is another reason why the popularity of crowd testing is growing in parallel. The pandemic undermined all the preconceived assumptions of the static requirement of an Agile-driven software development lifecycle. Crowdsourced testing of mobile apps is estimated at 6.04 billion by the end of 2022. It is worth mentioning that the rise in 2020 is the most noticeable growth due to the pandemic, with 42%relative to other years. During the forecast period, mobile testing services in the device category segment are expected to report the highest growth rate in the crowdsourced testing industry, as the number of mobile-based software tailored to different operating systems (OS), such as Android and iOS, has increased significantly. Customers, therefore, expect the apps to run flawlessly on all forms of devices, OS, and network on their cell phones. Source: Maia Research Final Thoughts Throughout a range of industries and most mobile verticals, the effect of the Coronavirus was disastrous, although some of them thrived on it. Although the worst affected ones have been companies such as travel and manufacturing, the software industry has still benefited to some extent. The pandemic has increased the need for a system where it is possible to remotely execute any operation that previously needed your real presence. The current climate has in many ways been unpredictable for customers, dependence on mobile apps as a means of commerce, communication, education, shopping, and entertainment, etc will continue to grow. Also published on: https://nurlan-41085.medium.com/mobile-app-industry-in-the-face-of-the-pandemic-opportunity-or-threat-53893df3c8a7

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