In follow-up to the first two articles in the Democratizing AI Newsletter which focused on AI as Key Exponential Technology in the Smart Technology Era and AI-driven Digital Transformation of the Business Enterprise, this article shares some text and audio extracts from Chapter 5, “AI Revolutionizing Personalized Engagement for Consumer Facing Business” in the book Democratizing Artificial Intelligence to Benefit Everyone: Shaping a Better Future in the Smart Technology Era as it pertains to the following topics, which will also be discussed on 24 March 2022 at BiCstreet‘s “AI World Series” Live event (see more details at the bottom of the article):
As we are discussing the transformative nature of AI in the Smart Technology Era, there is not a single industry that is not impacted by this exponential and disruptive technology. Like how we have seen how the internet and digitization are impacting all industries and sectors over the last twenty years, we can expect AI to have a lasting impact on the current industries as well as industries of the future such as robotics, advanced life sciences, big data, cybersecurity, and the code-ification of money, markets, and trust. As Alec Ross mentions in The Industries of the Future, these future industries with their geopolitical, generational, and cultural contexts and underpinnings are also symbiotic among each other and symbolic of larger global trends.[i] In this chapter – as part of the sense-making journey – I will be highlighting several AI use cases to get a sense of how AI solutions are being applied across multiple consumer facing industries such as financial services, retail, ecommerce, telecommunications, media, entertainment, transportation, travel and tourism. In following chapters I will cover AI uses cases across the industrial world, education, healthcare, and the public sector. In a report by McKinsey Global Institute (MGI) entitled Artificial Intelligence: The Next Digital Frontier, they show how adoption is the greatest in industries or sectors that are already digital adopters, where the characteristics of early adopters are being those that are digitally mature, larger in nature, adopting AI in core activities, having a focus on growth over savings, adopting multiple technologies, and having executive level support for AI.[ii] Although adoption has increased significantly since the 2017 MGI survey of 3,000 AI-aware executives across 10 countries and 14 sectors, the results at that time shows that only 20% said AI is core part of their business or being used at scale and only 12% of 160 use cases evaluated deployed commercially.[iii] Although things are changing fast, there still appears to be a growing gap between the early AI adopters versus the rest of the business enterprises across industries, mixed with AI startups with the intention to disrupt entire industries and sectors in various application areas via innovative business models and monetization of data and services. That performance gap is likely to increase in the future as it is clear from use cases over the last few years that AI can deliver significant value to business enterprises that are already using their strong digital capability in a proactive strategic fashion. Some of the key areas across the value chain where AI can create value is enhanced, tailored, and convenient user experience; targeted sales and marketing with products and services offered to the right customers with the right message at the right time and price; optimized production and maintenance leading to higher productivity, lower cost, and better efficiency; and smart sourcing, research and development, and real-time forecasting. As mentioned in the previous chapter, for industrial businesses, AI-driven solutions can significantly increase throughput, quality, yield, and productivity, reduce risks of process and equipment failure, and reduce costs with respect to energy, raw material, operations, and maintenance. For most consumer-facing businesses, the customer base can be grown, and revenues can be increased through AI-driven personalized engagement solutions to enhance the customer experience and more targeted sales and marketing drive recommendation, cross-selling and upselling. These consumer-facing businesses across industries can also reduce churn, fraud, waste, abuse, and cybersecurity risk, as well as lowering costs and increase productivity, operational efficiency, and effectiveness through AI-driven automation.
It is also clear that AI is fueling the future of productivity across industries. Goldman Sachs Investment Research estimates AI-driven automation and efficiency gains driving a 0.5%-1.5% reduction in labor hours which is likely to result in a 51-154 basis points impact on productivity growth by 2025.[iv] They are also of the opinion that AI-induced productivity could impact the way businesses allocate capital similar to the 1990s technology boom where we saw an amplification of capital deepening (capital stock per labor hour) and multifactor productivity (with IT-producing sectors contributing almost 50% of the productivity growth (output per labor hour)) as key components of productivity. As the use of AI technologies will become a key competitive advantage for businesses across all major industries, investment in AI through enablers such as the development of an inhouse “AI stack”, consulting services and AI-as-a-services will likely drive a growth in demand for the people, services, software, and hardware underlying AI. It was estimated by the IDC that the global spending on AI of $35.8 billion in 2019 can double to approximately $79.2 billion by 2022.[v] If we consider the global AI market, Grand View Research has estimated that its size is expected to grow at a compound annual growth rate of 42.2% from 2020 to 2027 which implies that it would to reach approximately $470 billion (USD) by 2027 projected from a market size of $39.9 billion in 2019.[vi] The market research by Fortune Business Insights shows more conservative numbers with their estimate of approximately 277 billion by 2027 at a compound annual growth rate of 33.2% given a global AI market size valued at $27.2 Billion in 2019. [vii]
According to Statista (in collaboration with Tractica), some of the top AI use cases worldwide up to 2025 will be machine or vehicular objection detection, identification and avoidance; static image recognition, classification, and tagging; healthcare or patient data processing; algorithmic trading strategy performance improvement; localization and mapping; predictive maintenance; prevention against cybersecurity threats; converting paper work into digital data; intelligent human resources systems; and medical image analysis.[viii] For business enterprises, higher AI adoption appears to be in financial services, high technology and telecommunications, automotive and assembly, followed by retail, media and entertainment and consumer packaged goods. Although relative lower AI adoption has been seen in education, healthcare and travel and tourism, that has been changing fast over the last few years with more significant changes to come in the 2020s. From an AI readiness perspective, the technology industry leads in every respect as they are also moving the fastest with regards to AI adoption across their whole business as well as their AI-driven products and services for customers. In a KPMG report Living in an AI World 2020 study almost 80% of leaders in the technology sector felt the pressure to be more aggressive when it comes to faster AI adoption even though this sector leads adoption with 63% of technology respondents saying that AI is moderately to fully functional implemented in their businesses.[ix] Whereas AI endeavors for technology companies were initially targeted at research and development, product development, and front-office work, there seems to a move towards more implementations also in the back and middle office areas.
There is no doubt that AI already has an enormous impact on the whole financial services sector. Besides AI’s process automation applications such as robotic process automation and cognitive process automation that helps to reduce costs, increase productivity, and prevent fraud losses, there is also a significant role for AI to play in digitized personalized engagement as we see the growing internet user base swiftly switching to mobile devices to perform a transaction or related actions within integrated in-app transaction systems. As this would only increase the risks for cyber-attacks and more possibly ways for financial fraud to occur, AI will also play an increasing role in assisting to address these risks. Mordor Intelligence has appraised AI’s share in the fintech market as $7.27 billion in 2019 and projected it to get to $35.40 billion by 2025 with a compound annual growth rate of 31.5% for the period 2020 to 2025.[x] Technavio reckons that the global AI market in banking, financial services and insurance sector is expected to post a compound annual growth rate of 32% during the period 2019-2023.[xi] AI has broad applications across the financial services sector from banking and insurance to investment that advance better outcomes for both consumers and businesses. On the investment side, some relevant use cases for deploying AI solutions include maximizing investment, portfolio optimization, high speed arbitrage trading, reducing compliance and regulatory costs, trade surveillance, abnormal trading pattern detection, market manipulation detection, and risk analysis and management. For commercial banking and insurance sectors, AI use cases include reducing credit risk through scoring and analysis (complementing traditional statistical scoring methods), churn mitigation, response modeling, real-time customer insight for personalized customer engagement, loyalty programs, recommendations, cross-selling, up-selling, digital advice, robo-advisor, fraud detection, smart payment systems, automated and augmented underwriting, and robo-claims adjusting. Just between maximizing investment potential, reducing credit risk and reducing compliance and regulatory costs, Goldman Sachs Global Investment Research (GS GIR) estimated that AI could, conservatively, enable access to between $34-$43 billion per year in cost savings and new revenue opportunities by 2025, with further upside as AI enable faster and more complex data leveraging and execution.[xii] AI-driven software and hardware accelerators applied to the rich, high volume, high speed, and robust data sets are providing significant advantage to enable better informed investment decisions, quicker reaction to market events, reducing costs, and breaking into new profit pools. For example, to give asset managers at high frequency trading companies a competitive advantage, execution speed and gaining access to trading information mere fractions of a second before the market (through co-located trading servers at the exchange and/or sourcing data from raw exchange feeds and retrieving best bid/offer prices faster than traditional data consolidation processes) are paramount. Fast executing AI algorithms can use this latency arbitrage to act on the price spread in a faster and more accurate fashion before the latency period has elapsed.
These AI-powered algorithmic trading solutions can also use alternative data such as geo-spatial (e.g., satellite imagery of areas that are relevant to equities, commodity prices and even economies), social media sentiments (e.g., blogs, reviews, brand logos, web metrics, Facebook, Twitter, Instagram, TikTok, LinkedIn, YouTube, and Sina Weibo) and credit card transactions to complement the typical pricing signals, company performance metrics and news sentiments to help optimize investment portfolios. A complementary AI use case is where image recognition machine learning techniques such as convolutional neural networks are used to extract key characteristics from real-time satellite imagery of commodity depots, storage facilities, production facilities, containers, shipping movements, store parking lots, agriculture land, etc., which in turn can be used as additional input features to models of AI-driven trading solutions. For example, a retailers’ sales can be predicted, by amongst other factors, using features extracted from real-time imagery of store parking lots and shipping patterns.[xiii] In general, the financial services industry has a great opportunity to benefit more from AI being applied to data-driven market events as also illustrated by the volatility around data releases in the commodity futures market. Hedge funds and other asset managers can also save costs by reducing labor needs at a faster rate than the growth in data procurement costs, where a 5% reduction in operating expenses is predicted for the asset management industry implying a $13 billion annual cost reduction by 2025 for the industry.[xiv]
Given new regulations that are facing the banking industry, there is also an increased compliance spend by small community banks, commercial banks as well as large investment banks (as reported by the likes of J.P. Morgan, Citigroup and others over the last decade), resulting in an estimated total of approximately $18bn in compliance-related employee costs per year.[xv] AI can be used in a proactive fashion to handle tasks such as detecting violations (e.g., such unauthorized trading, market manipulation, or wall cross violations) as well as inspecting employee emails for potentially noncompliant content. A 10% reduction in compliance employee costs because of AI-driven solutions should therefore contribute approximately $2bn per year in compliance cost reduction for banking within the next few years. As another example of reducing cost through process automation, JP Morgan has introduced an AI solution called COIN to review and interpret commercial loan agreements in seconds, which has eliminated more than 360,000 hours of work for lawyers each year.[xvi]
Intelligent virtual assistants deployed within the banking sector and powered by social messaging platforms, mobile devices, or voice assistants can help to provide much better customer service through its convenience, ubiquity, and ease of use to allow customers to quickly inquire about account balances, mortgage options, or other banking services. With state-of-the-art natural language process capabilities, personalized intelligent virtual assistants that are integrated with banking systems and related data can not only process customer queries asked with significant variations, including spell and grammar errors, and handling the context of the user interaction, but via text or voice command provide quick answers to banking queries, personalized financial advice and even carry out transactions all from the same channel. Also, amongst the emerging trends are AI-based robo-advisors that calibrate financial portfolios in accordance with the goals and risk tolerance of their users with respect to investments, trading, loans, and retirement plans. This can be achieved by optimizing how investments are spread across asset classes and financial instruments and adapting in accordance with real-time changes in the market as well as the user’s goals and objectives. These AI-based robo-advisors allow users to create individual, personalized settings for their preferences regarding investment styles and associated risk management, and also leads to fewer human errors being made and lower transaction fees.
From the plethora of AI use cases in retail and ecommerce it is clear that AI can help to capture significant gains across this sector’s value chain by for example delivering a better shopping experience, predicting demand, and automating operations. These smart technology solutions can specifically help to address some of the biggest challenges in retail such as not having accurate forecasting models to predict trends and levels of demand, poor customer support and services, ineffective targeting, recommendations not being in line with customer preferences and needs, non-optimal store footprints, and having overstock and out-of-stock inventory problems. According to Meticulous Research, AI’s growth in the retail market has been predominantly driven by the increasing adoption of a multichannel or omnichannel retailing strategy to enhance the personalized engagement and end-user customer experience. They projected a compound annual growth rate of 35.9% to reach $15.3 billion by 2025.[i] In order to unlock business value, serve their customers better and capture these gains, retailers and ecommerce businesses must leverage technology infrastructure and all available data. Although traditional retailers have in general struggled to make the offline-to-online transition, the growth of advertising technology and ecommerce that generates massive customer data have allowed retailers to optimize their advertising and to target customers in a more effective and efficient fashion. With the wealth of data available in disparate data sets such as loyalty, clickstream, browsing behavior, promotions, in-store, location, customer profiles, customer life stages, channel interaction, pricing, and billing data, a wide range of AI applications are possible that includes demand prediction, offering the right products and services to the right customer at the right time; inventory management; customer management; merchandising and market basket analysis; campaign management and customer loyalty programs; supply-chain management, analytics, and optimization; event- and behavior-based targeting; market and customer segmentations; recommendation engines that help to increase the average order size by recommending complementary products based on predictive analysis for cross-selling; cross-channel analytics; and trend extrapolation. Accenture reports that investment in AI and human-machine collaboration could boost retail store revenues by 38% by 2022.[ii] As a result of the growing AI use cases in retail and ecommerce, GS GIR predicted the associated demand and driving labor efficiencies worth $54bn annually while price optimization and annual sales increase in discretionary categories such as clothing and footwear can amount to $41bn globally by 2025.[iii] A MGI analysis has highlighted some use cases that include optimized pricing, personalized promotions and recommendations, intelligent virtual assistants, automated in-store checkout, completing last-mile delivery by drones, real-time tailoring of website displays, anticipating of demand trends whilst optimizing and automating supplier negotiation and contracting, automating warehouse and store operations, as well as optimizing merchandising, product assortment, and microspace.[iv] Some example AI use cases with specific benefits include a 1–2% EBIT (earnings before interest and taxes) improvement using machine learning to anticipate fruit and vegetable sales by a European retailer, a 20% stock reduction of a German e-commerce merchant Otto using deep learning on billions of transactional records to predict e-commerce purchases and 2 million fewer product returns per year, a 30% reduction of stocking time using autonomous vehicles in warehouses of Swisslog, a 50% improvement of assortment efficiency, 4–6% sales increase using geospatial modeling to improve micro market attractiveness, and 30% online sales increase by using dynamic pricing and personalization.[v]
Amazon is a prime example of an extraordinarily successful company in retail and ecommerce that uses AI to develop and maintain their competitive advantage in scalable customer service and experience as illustrated with their usability, recommendations, and membership benefits. To drive product recommendations, they apply a combination of collaborative filtering and next-in-sequence AI models on their colossal consumer purchases behavior database to make recommendations on the next likely products and/or services that a customer might be interested in purchasing. Amazon also uses state-of-the-art natural language processing to power their digital assistant Alexa, optimizes their logistics with respect to delivery arrival times, accuracy, efficiency, rerouting, and even drone delivery. With smart speaker devices such as Amazon’ Echo and Google Home, shoppers can also complete retail orders through express and third-party retailers. Amazon Go, a cashier-less grocery store and mobile application that lets shoppers buy items without human interaction, swiping a card or paying cash, is using AI and sensor technology to create a check-out free shopping experience that automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart and handles the payment automatically via the mobile application.[vi] There are also similar cashier-free convenience stores in Sweden and Finland as well as in China where customers can pay via WeChat or other mobile applications.[vii] Some examples of where traditional retailers have instrumented their stores with Bluetooth beacons to collect data about customer behaviors and purchasing patterns include Target and Walmart in the US and Carrefour in France, which in turn allows them to apply AI-based models to this data in order to send personalized promotion to their customers whilst they are shopping in store.[viii] In the case of Carrefour, the deployment of this smart technology solution in 28 stores has led to a 600% increase in their mobile application users.[ix] Another example of improving the shopping experience is IKEA’s augmented reality application, called Place, that allows customers to visualize how IKEA furniture fits into customer’s household spaces.[x] Apart from eBay’s Shopbot that provides recommendations and advice, they are also experimenting with consumer psychology to better understand consumers using subconscious techniques (such as color psychology, persuasive pricing, trust symbols and marketing triggers) for better marketing and helping to optimize conversions.[xi] Pinterest introduced a Chrome extension which allows users to search for any product online through an image and allows them to browse similar products by the same retail store. Furniture retailer West Elm scans customers’ Pinterest boards to understand their personal style and then generates a matching recommended furniture and home décor item list.[xii] Macy’s in-store experience has been given a boost with their AI-driven On Call application and chatbot that addresses individual store related questions about a specific item, directions or if an item is in stock, but can also pick up if a customer gets frustrated and human intervention is needed. Another in-store example is clothing retailer Uniqlo who has several stores with UMood kiosks that show customers a variety of products and measure their reactions to the style and color via brain signals.[xiii]
The increasing adoption of AI for various applications in telecommunication, social media and entertainment markets, the utilization of AI-enabled smartphones, and growing investments in the 5G network are all expected to be contributing significantly to the growth of the AI across these markets. Apart from 5G being the next generation technology standard for cellular networks that provides significantly greater bandwidth and download speeds of up to 10 gigabits, it can also be seen as a software-defined network governed by AI with respect to its design, planning, monitoring, management and optimization – making it effectively the current largest deployment of distributed AI. According to Technavio the global AI market size in the telecommunication industry will grow by $2.54 billion during 2019-2023[i], whereas Tractica projects $11.2 billion in 2025[ii] and Juniper Research $15 billion by 2024.[iii] Technavio also predicted that the global telecom IoT market to post a compound annual growth rate of more than 42% by 2020, with intelligent transportation systems one of the major factors.[iv] Some of the key AI use cases in telecommunications include predictive maintenance, monitoring and optimization of network operations, real-time analysis of call direct records and internet protocol detail records for networks, fraud identification and mitigation, cybersecurity, robotic process automation, intelligent virtual assistants for customer service and marketing, intelligent customer relationship management systems, revenue assurance and dynamic pricing, customer churn prevention, customer experience management and service delivery, sentiment analysis, social network analysis, campaign management, customer loyalty, mobile user local analysis, and video compression.
Telecommunications service providers are experiencing an increase in demand for improved customer experience and better quality services. By using an AI-driven approach to mine all available relational and non-relational data, a 360-degree view of the customer can be built to provide real-time analytics and insights to provide next best actions that address these customer demands in a personalized fashion. AI-driven solutions can also improve operations and increase revenue through new products and services. Due to nature of telecommunications and massive customer bases, telecommunication companies have access to vast amounts of mobile network data, voice network data, subscriber data, social network data, and machine generated data that provides details about mobile internet usage, call detailed records, geolocation, smart device usage, channel interaction, billing, network, set top box, and machine-to-machine communications. Real-time analytics on these data involves, for example, understanding device usage, customer behavior and preferences, the right channels and social circles, influences, segments, and hotspots in the network performance. The insights generated can drive contextualized, dynamic, and personalized decision recommendations; identify communities, leaders, and followers; and provide a real-time view of the network load heat-map and customer experience. As with other customer facing businesses, the next best action delivers the right offer to the target at the right time to the right location. It also provides customer services representatives and intelligent virtual assistants with the right profile and preferences, manages real-time customer interactions through integrated channels and offers highly differentiated and customized products and services.
As telecommunication companies are adopting technologies such as software defined networks, network function virtualization, and orchestration, AI is earmarked to play a key role in the integration of these technologies and the automation of networks. According to the IDC, almost two thirds of network mobile operators are investing in AI systems to improve their infrastructure.[i] One key AI use case is the building of self-optimizing networks to enable operators to automatically optimize the network quality based on traffic and time zone. As an example, Aira Networks provides AI-based solutions for automating and optimizing the supply chain that delivers services for digital economy service providers and data center operators.[ii] They use AI to dynamically determine the optimal network configuration for a given service request or traffic demand. According to Sedona Systems, their NetFusion Platform automatically discovers, aggregates, and analyzes network data from multiple online systems, optical and IP sources to provide a unified, real-time and accurate network-wide data model. The latter is then consumed via products that reduce the cost, time, complexity, and resources that are needed to forecast, plan, and operate the optimal network infrastructure for delivering the required services, reliability and performance.[iii] Nokia also offers an AI-as-a-service offering delivered through the Microsoft Azure cloud to automate network operations and provide predictive and proactive services across operations and care for improved customer experience, service assurance, increased agility and reduced costs.[iv] As with the other equipment-rich industries, predictive maintenance is another major AI application area for telecommunications companies with communications hardware such as cell towers, data center servers, power lines, and setup boxes. Machine learning based solutions are utilized to provide real-time monitoring of the equipment state, prevent equipment failure, determine causes for deviation in equipment performance, and assist in solving problems proactively. Predictive maintenance solutions aim to understand the process of service degradation before failure and predicting which network elements have a high likelihood of failure within a specified time frame. Avanseus describes a predictive maintenance use case with a major European Telecom service provider that is in process of transforming its network operations to get ready for 5G and IoT deployment at scale and to deliver better network experience to customers. The outcomes of their AI-driven Cognitive Assistant for Networks solution include prediction of 50%+ of the potential impact incidents with greater than 75% accuracy and 35%+ reduction in service impacting incidents and a 0.17% increase in network availability.[v] Affine is another AI-powered predictive maintenance solution that can accurately predict the propensity of telecom tower components due to factors such as fatigue, manufacturing defects, or unsuitable environment in order to prevent business disruption through planned and scheduled maintenance.[vi] KPN is a Dutch telecommunications company that is using predictive maintenance to proactively identify issues on the customer’s side by tracking and tracing behavior such as channel switching on a modem that could cause a WI-FI issue.[vii] Another significant AI use case is the detection of fraudulent activities such as illegal access or authorization, stealing or faking profiles, or behavioral fraud that can be prevented by applying anomaly detection solutions that typically make use of unsupervised machine learning to provide real-time alerts when the system sees abnormal or suspicious behavior.
Whereas transportation and logistics have seen a relatively high adoption of AI, travel and tourism have, relative to other sectors, been lagging in both adoption and the future AI demand trajectory. Even though this is the case, there are a variety of impactful AI use cases that will be highlighted here. The transportation industry clearly has high expectations for the impact of smart technology. Transportation’s interest in AI is predominantly driven by autonomous vehicles, and the transformative impact they are expected to deliver on how metropolitan areas and cities operate. Not only can machine learning powered smart technology solutions provide safe and efficient transportation, but also improve traffic flow, expand the capacity of existing road infrastructure, as well as reduce carbon emissions and facilitate greater inclusiveness. Given that transportation is at the intersection of smart technology and public life, a significant majority of transportation decision-makers also are of the opinion that the government should be to some extent involved in AI regulation.[i] According to the same KPMG study, these industry leaders are not only almost unanimous about the expected benefits of AI-driven solutions, but also aware of the risks with most of them feeling that there is also a threat to consumer data privacy or security. The market size for AI-driven solutions in transportation is expected to grow from $1.2 Billion in 2017 to $10.3 Billion by 2030 which translates to a compound aggregate growth rate of 17.8% over the period.[ii] Some relevant use cases for deploying AI solutions in the logistics and transportation sector include streamlining decision-making in transport management driven by AI and IoT, optimizing transport operations, managing warehouses, decreasing downtime and repairs through monitoring and predictive maintenance, going driverless, changing supply chain logistics into automated trading, and demand planning. Freight and logistics are a $4.5 trillion global sector that is highly fragmented, complex, and manual with a high dependence and cost of human coordination. This market is ripe for disruption with several startups delivering AI-driven solutions to address these issues. One such startup is FERO which is focused on eliminating human coordination in freight and logistics and envision bringing AI enabled coordination to the freight ecosystem through optimization and automation of freight transactions globally.[iii] They have implemented a voice-enabled AI-driven virtual freight intelligent virtual assistant that not only coordinates pricing, quotes, customer services and operations, but also manages sales, bidding, and customer service across various freight functions. Machine learning models can for example be used to predict whether a product will be shipped on time and find the most optimal shipping routes. In addition, intelligent systems can help identify problematic incidents and solve them in time. One of the use cases that Cortex Logic has been involved in was to help reduce the risk in transport and logistics for a provider of fleet and mobile asset management solutions. The machine learning powered application was aimed at improving the safety and security of people and assets, reducing cost of operations, and enabling behavioral change of drivers. Such a solution does not only help the fleet management provider to enhance its customer service, but also improve driver safety by predicting a potential accident or harm from driver behavior and also flag accident prone behavior for human review and management. The cloud-based AI-driven solution uses existing camera footage to monitor camera obstruction, identify anomalous behavior (e.g., driver distraction, cell phone usage; driver fatigue; driver safety belt usage; and smoking) as well as the number of passengers in the vehicle and integrating the outputs with the current alert processes.
This Democratizing AI Newsletter coincides with the launch of BiCstreet‘s “AI World Series” Live event, which kicked off both virtually and in-person (limited) from 10 March 2022, where Democratizing AI to Benefit Everyone is discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. (To book your tickets to this global event click the link below and enter this Coupon Code to get 5% Discount: Enter Coupon Code: JACQUES001 (Purchase Tickets here: https://www.BiCflix.com; See the 10 Weekly Program here: https://www.BiCstreet.com)).
The audio book version of “Democratizing Artificial Intelligence to Benefit Everyone” is also available via major audio book market place world-wide. See details on my website as well as below. You can also listen to audio content of this book on the Jacques Ludik YouTube Channel or Jacques Ludik Podcasts. This release is in follow-up to the e-book (Kindle) and paperback version of the book that was released earlier this year on Amazon with some further updates recently.
For some background, see also the following introductory articles Democratizing AI to Benefit Everyone and AI Perspectives, Democratizing Human-centric AI in Africa, and Acknowledgements – Democratizing AI to Benefit Everyone (as well as United Nations & Democratizing AI to Benefit Everyone; World Economic Forum and Democratizing AI to Benefit Everyone; OECD and Democratizing AI to Benefit Everyone; AI for Good and Democratizing AI to Benefit Everyone).
For further details, see jacquesludik.com.
[i] Alex Ross, The Industries of the Future.
[ix] Shubhi Mittal, “25 retailers nailing it with their proximity marketing campaigns,” Beaconstac.com, February 11, 2016