Volume 2 (2022-23)

Each volume of Journal of AI, Robotics and Workplace Automation consists of four quarterly 100-page issues. 

The articles and case studies just published in Volume 2 are listed below:

Volume 2 Number 4

  • Editorial: AI and blockchain
    Prashant Sarode, Co-Founder and Managing Partner, Head of Product and Art of Possible Labs, TheoremLabs
  • Special issue papers: AI and Blockchain
    DocuXperts.SaaS | dBPO network: A decentralised automation platform
    Prashant Sarode, Co-Founder and Managing Partner, Head of Product and Art of Possible Labs, TheoremLabs

    This paper analyses the potential of decentralised BPO (dBPO) to revolutionise the business process outsourcing industry. The author argues that dBPO can address the shortcomings of traditional BPO models, such as high costs, low quality and lack of innovation. The paper begins by outlining the problems with traditional BPO models. These models are often centralised, which can make them inefficient and inflexible. They can also be expensive, as businesses have to pay for the overhead of managing a large workforce. Additionally, traditional BPO models often lack innovation, as businesses are reluctant to invest in new technologies. The paper then discusses how dBPO can address these shortcomings. dBPO is a decentralised network of businesses and individuals who can provide BPO services. This means that businesses can access a wider range of providers and services, which can lead to lower costs and higher quality. Additionally, dBPO is more flexible than traditional BPO models, as businesses can easily add or remove providers as needed. The paper then presents a case study of DocuXperts.SaaS, a dBPO platform that is being developed by TheoremLabs.io. DocuXperts.SaaS is designed to provide a variety of BPO services, such as document processing, data entry and customer support. The platform is built on top of Human Protocol, a decentralised protocol for crowdsourcing work. The paper concludes by discussing the potential of dBPO to revolutionise the BPO industry. The author argues that dBPO can make BPO more efficient, affordable and innovative. Additionally, dBPO can help to create a more distributed and equitable economy.
    Keywords: gig work; gig platform; decentralisation; BPO; document processing; AI; hybrid BPO; human in the loop; autonomous; agents; bots

  • DataUnions: A privacy-by-decentral-design
    Robin Lehmann and Mark Siebert, DataUnion Foundation

    Privacy by design suggests seven principles to embed privacy into systems, but artificial intelligence (AI) and data practice shows a ‘privacy paradox’ in the behaviour of users. The ever-evolving AI capabilities outpace privacy policies and people require more insight into the possible use of their data to make informed consent decisions. Keeping the human in the loop for reconsenting brings huge efforts and is disproportional to agile development. Decentral data management approaches promise efficient ways to handle crowd ownership, rights and their governance involvement, supporting privacy-by-design. This paper illustrates how a DataUnion approach for data-centric AI can efficiently combine decentralised, privacy-preserving features such as data non-fungible tokens (NFTs), federated learning, marketplaces, provenance or value sharing. The discussion concludes on data provenance being a key lever leading to the need for a blockchain protocol that makes privacy contributions an incentivised asset along the data value chain of enriching, verifying and governing data for AI. The quest for explainability in a model-centric approach is the quest for provenance in a data-centric approach.
    Keywords: data economy; DataUnions; collaboration; privacy; decentral; DataNFTs; tokens; blockchain

  • Blockchain technology and the potential of waqf (Islamic endowment) sector
    Ahmet Faruk Aysan and Hiba Ali Al-Saudi, Hamad Bin Khalifa University

    The waqf (Islamic endowment) sector encompasses significant untapped wealth that can contribute to socioeconomic and sustainable development. The sector suffers, however, from prevailing trust issues that have weakened its role and impaired its effective utilisation. As a trust-building technology, blockchain emerges as a potential solution to overcome waqf challenges. To demonstrate the prospects of blockchain technology in waqf revival, this paper presents the challenges faced by the waqf sector and the value that blockchain technology can bring to the sector, the challenges facing the adoption of the technology considering the prevalent governance and management structures and the current operating models. It also overviews blockchain in practice. This paper argues that the value of blockchain technology in waqf lies primarily in the development and management of waqf assets. This value, however, may only be reaped once existing waqf challenges have been resolved and a carefully architectured governance structure is developed. The waqf development and management model warrants close coordination with government agencies and waqf authorities as the current custodians of waqf assets. This renders some features of blockchain, namely decentralisation, irrelevant. Challenges related to legal and Sharia uncertainties surrounding blockchain technology and cryptocurrencies pose risks that must be carefully considered to ensure that blockchain waqf platforms are not pursued as a venture driven solely by blockchain hype.
    Keywords: blockchain technology; waqf; Islamic endowments; social finance; Islamic finance; FinTech

  • Enhancing data privacy in financial services: The role of zero-knowledge proofs and federated AI
    Alex Lyashok, Merit Protocol and Prashant Sarode, Co-Founder and Managing Partner, Head of Product and Art of Possible Labs, TheoremLabs

    This paper analyses the challenges of balancing anonymity, utility and security in financial services. It argues that the traditional approach of using clearinghouses to enhance utility has come at the expense of anonymity. However, the advent of privacy-enhancing technologies like zero-knowledge proofs and federated AI has begun to minimise these trade-offs. The paper provides a case study of Merit Protocol, a company that is using these technologies to address the problem of predatory payday loans. Merit Protocol’s platform allows employers to pre-underwrite loans for their employees without sharing sensitive data. This approach empowers employers to support their employees’ financial needs while maintaining privacy and reducing dependency on traditional credit agencies. The paper concludes by discussing the challenges that the financial services industry must address in order to fully realise the potential of privacy-enhancing technologies. These challenges include navigating legacy compliance frameworks and improving the ease of use of these technologies. Readers can expect to gain a deeper understanding of the challenges of balancing anonymity, utility and security in financial services. They will also learn about the potential of privacy-enhancing technologies to address these challenges.
    Keywords: data privacy; ZK proof; financial services; data security; federated AI; AI; federated learning

  • Papers: AI, Robotics and Workplace Automation
    Understanding human-centred artificial intelligence in the banking sector
    Obuchettiar Krishnaraj Arul and Alan Megargel, Singapore Management University

    The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions. Consumers increasingly want hyper-personalised interactions that are more frequent and proactive, while financial institutions have a growing need to cater to consumers’ new demands. Financial institutions, such as banks, continuously adapt to the latest technologies to keep pace with evolving customer behaviours, needs, and experiences. One such emerging technology is artificial intelligence (AI). Many organisations realise the potential of AI; however, a human-centred AI system must be capable of understanding human characteristics and making decisions like humans. This paper presents an empirical study, involving survey participants from three different groups: banks, IT vendors and focus groups. Emphasis is placed on understanding the effect of practising a co-development mindset between these three key stakeholder groups on the outcome of developed human-centred AI-enabled products and services. The survey results show that capturing and processing human emotions (HE) to train an AI model improves customer experience and trustworthiness, and a co-development mindset practised between the IT vendor and the bank will positively influence the effectiveness of human-centred AI-enabled products and services.
    Keywords: AI principles; human-centred AI; AI transparency; customer experience; banking

  • Smart robots and AI: Strategic approaches to data privacy and security law for digital marketers
    Raj Sachdev, Plymouth State University and Cornell University

    Smart robots are expected to increasingly be employed by consumers to improve their everyday lives in ways not seen before. Artificial intelligence (AI) applications in smart robots are changing the way smart robots are used and the way related services are consumed. Privacy and security concerns arise, however, with the implementation of innovative technologies for consumers as these collect and use personal data. While the law has not caught up with the technology, this is further complicated by the fact that there are no clear rules in some cases. European Union (EU), UK and US law require ‘appropriate’ or ‘reasonable’ measures to be taken and require the protection of personal data of consumers generally, including marketers of smart robots using AI. This is complicated by the complex nature of AI and the need to track consumers and collect data in order to provide personalised and effective smart robot services. Marketers should employ an interdisciplinary strategy to address these concerns and requirements including employing marketing, technical and legal strategies. This has wider implications for the marketing community as it becomes very difficult to not consider or build in technical and legal considerations.
    Keywords: AI marketing; law; privacy; security; digital marketing; smart robots; data; consumer rights

  • Measuring effectiveness of AI-based virtual service assistants by user mental health improvement: A research agenda
    Devdeep Maity, Delaware State University, Juha Munnukka, University of Jyväskylä and Ashwini Gangadharan, Kutztown University of Pennsylvania

    This paper argues that traditional performance measures of artificial intelligence (AI)-based virtual service assistants (VSAs) fall short of capturing the human-like attributes of VSAs, their influences on human partners, and hence a VSA’s true potential. The current study suggests that the effectiveness of VSAs can be measured by examining the variables related to mental health improvements of the consumers or human partners as a direct result of their conversations, companionship and engagement with the VSAs. The paper presents a research agenda, highlighting the role of perceived leadership of the AI-based VSAs, consumer engagement with VSAs and the subsequent social support of the VSAs to their consumers. It suggests that the aforementioned will result in the enhancement of a variety of coping skills such as problem solving, emotional and time-focused coping, and a subsequent overall increase of trust in AI-based VSAs among their consumers.
    Keywords: virtual service assistants; artificial intelligence; mental health; coping; consumer engagement; AI leadership

  • A journey towards human-centric and AI-augmented marketing?
    Malin Lignell, Svenska Handelsbanken

    We are in the midst, or perhaps even the beginning, of transforming how banking relationships are being built and developed over time. At Handelsbanken, we took the first steps in digitisation in the 1960s. We were early to introduce Internet banking to customers in the 1990s and have, like the financial market at large, focused on digitalisation in recent decades. The activities that could previously only be done at a branch are becoming more and more accessible via digital channels, processes are being automated, yet there is still more to be done. This paper will demonstrate how a 150-year-old bank, which closed its marketing function in the 1970s and has essentially grown mainly through word of mouth, might approach using AI in marketing. In the discussion, the paper draws on previous experiences of introducing AI in other domains within the bank, performed by the Digitalisation and Innovation team in collaboration with other parts of the organisation. As always, the introduction of new technology, including in marketing and sales, starts with business principles, data, ethics, company purpose and values, rather than with the technology itself. It is still early days for the bank when it comes to AI in marketing, and until there is such a thing as attentive algorithms, humans will be very much involved in the process — especially if the aim is to continue being human-centric in our approach, where the outcome of an activity is beneficial for customers, the bank and society at large. And the meaning of ‘customer-centric’ needs to be defined as making the system work with the customer at the centre: cross-channel, relevant, timely and based on in-depth customer understanding. The paper will show that the rationale for closing the bank’s marketing function back in the 1970s might not be so different from the rationale to do things differently today.
    Keywords: marketing; AI; artificial; augmented; Handelsbanken

  • Revolutionising content recommendation: The impact of AI in marketing 
    Ken Ip, Asia MarTech Society

    Artificial intelligence (AI) has become an integral part of the marketing industry, particularly in the area of content recommendation. By utilising data analysis and machine learning (ML) algorithms, AI systems can suggest personalised content to consumers, enhancing their browsing and viewing experiences. This paper examines the use of AI in marketing and how it is revolutionising content recommendation. It looks at the benefits and challenges of AI in marketing, the types of AI models used in content recommendation and how these models work to provide tailored content to consumers. The paper also explores the ethical implications of AI in marketing and the need for regulations to ensure consumer privacy and prevent bias. Overall, this paper aims to provide a comprehensive overview of AI in marketing and its impact on content recommendation.
    Keywords: artificial intelligence; marketing; content recommendation; machine learning; personalisation; ethics

Volume 2 Number 3

  • Editorial:
    Christopher Johannessen, Editor, Journal of AI, Robotics & Workplace Automation
  • Papers:
    AI and sustainability: The opportunity for brands
    Carly Fink, President, Provoke Insights and Jeremy Goldman, Chief Executive Office and Founder, Futureproof

    The opportunity to advance green initiatives with advanced technologies is endless, and this new area of growth will bring a number of revenue opportunities. Consumers, governments, and special interest groups are willing to reward brands for their sustainable practices, in which artificial intelligence (AI) can play a major role. AI can be a force for good and lift how a brand is perceived — but it has to be championed internally by proving its ROI, using the right vendors, and being properly implemented and well-communicated to consumers. As AI can help synthesise large amounts of data and automate tasks, it brings new possibilities for advancing green initiatives. Because of environmental concerns across the globe, such as greenhouse gas (GHG) emissions and pollution, governments and corporations are championing these initiatives. For example, over 5,000 companies enrolled in the United Nations’ Race to Zero campaign in 2020; this initiative seeks to secure commitments to reduce GHG emissions.
    Keywords: artificial intelligence, AI, marketing, sustainability, green, branding, promoting sustainability, AI sustainability

  • Sponsored by a robot? How the human likeness of virtual influencers influences purchase intentions towards sponsored products
    Haneul Jang, Teaching Assistant, Stan Richards School of Advertising & Public Relations, Moody College of Communication, The University of Texas at Austin, et al.

    With the rapid development of artificial intelligence (AI) technologies and the emerging trend of influencer marketing, virtual influencers have become promising advertising tools for marketers. Based on computers are social actors (CASA) and uncanny valley theory (UVT), this research study conducted a pilot study (N = 155) and an online experiment (N = 293) to investigate how the perceived humanness of virtual influencers affects their advertising effectiveness. The results indicate that the perceived humanness of virtual influencers increases perceptions of eeriness, attractiveness and authenticity. The increased perceived attractiveness and authenticity of virtual influencers, in turn, leads to more positive attitudes towards the virtual influencers and purchase intentions towards the advertised products. Furthermore, the current study found that such effects were moderated by the perceived similarity between consumers and virtual influencers. The theoretical and practical implications are discussed.
    Keywords: virtual influencer, uncanny valley, computers are social actors (CASA), humanness, similarity, attractiveness, authenticity, virtual influencer marketing

  • AI-powered next best actions for wealth
    Lily Li, Senior Vice President, Citi Global Wealth

    Since the author proposed the idea of ‘contextual behaviour-driven strategy’ in 2020, we have witnessed a fundamental shift in people’s behaviours. Transformative models in wealth across interactive relationship, digital engagement and artificial intelligence (AI)-automated servicing have been widely adopted by clients. AI applications, especially next best actions (NBAs) based on contextual data prediction, are offering great value to our clients and attracting adoption. Yet for financial professionals, new tools, operating models and support culture are lagging. There is tremendous need to empower them to conduct more complicated collaborations and workflows seamlessly and holistically from relationship management, planning to advisory, servicing. Furthermore, the scale of business is growing. We project a huge volume of wealth transfer in the upcoming two decades to entertain and a next generation of clients to delight. AI plays a critical role to empower. In the recent development of the AI field, predictions based on past patterns and personalisation based on trained-engine business rules are broadly accurate; however, making judgments for an uncertain future is an area where AI has not yet delivered. We can strive to forecast decision variables and overlay additional dimensions to the NBA models to complement advisers’ and bankers’ intuition and expertise, and ultimately cultivate the culture and sales support to make more relevant and appropriate judgments, prioritisation and recommendations to their clients.
    Keywords: AI, next best action, wealth, advisory, banking, digital

  • AI revolutionising customer experience through personalisation and intelligent experiences
    Benjamin Maxim, Chief Digital Strategy and Innovation Officer, MSUFCU/ Chief Technology Officer, Reseda Group

    Artificial intelligence (AI) has existed in some fashion for almost seven decades, but its advancement has been limited to large companies and prestigious universities due to the cost of computing power. With the ubiquity of modern computing creating 97 zettabytes of data in the world by the end of 2022, companies are tapping into this new commodity to deliver a seamless, omnichannel customer experience using AI to compete above their weight class. The surge in online interactions since the onset of the COVID-19 pandemic has escalated consumer expectations, giving them more exposure to the personalisation practices of e-commerce leaders and raising the bar for everyone else. From web to mobile and in-person interactions, consumers now view personalisation as the default standard for engagement, making personalisation matter more than ever before as three-quarters of consumers switched to a new store, product or buying method during the pandemic. This paper sets out to highlight companies that are using AI to produce a stellar customer experience and discusses pitfalls that have been discovered along the way.
    Keywords: artificial intelligence, customer experience, phygital, intelligent experience, data privacy

  • How AI can move customer personalisation to customer contextualisation
    Brian Sathianathan, Co-Founder and Chief Technology Officer and Solomon Ray, Senior Director of Strategy and Special Projects, Iterate.ai

    Reaching beyond personalisation to achieve contextualisation is the holy grail for brands and retailers — a challenge that countless businesses hope to crack by applying emerging artificial intelligence (AI) technologies. International business leaders are increasing their investments in technology and data analytics to drive performance. This includes building out marketing teams with data scientists and acquiring tech start-ups to transform their businesses’ capabilities to more accurately predict customer needs and wants. McDonald’s US$300m acquisition of AI start-up Dynamic Yield is a clear example, described as fuelling the business’s vision to create more personalised customer experiences. In the race to tailor communications and customise recommendations, however, few businesses can deliver contextualisation — the ability to understand what a customer wants, immediately, at a particular moment in time. Advances in voice interfaces and consumer use of personal assistants only accentuate the need for greater sophistication in analysing situational context. This paper shares a view of the emerging AI-fuelled voice and virtual assistant ecosystem, how businesses can drive demonstrable value by solving the contextualisation challenge, and how new low-code development practices can accelerate and democratise these coming technological advances.
    Keywords: personalisation, artificial intelligence, low-code, natural language understanding, stable diffusion, voice technology, chatbot, Voice 2.0

  • Building AI solutions that deliver a better customer experience
    Jo Causon, Chief Executive Officer, Institute of Customer Service

    Technology, including artificial intelligence (AI), has the potential to improve the customer experience significantly. However, there are several challenges of which that organisations should be aware in order to ensure that AI is used effectively. One of the key challenges is ensuring that AI is used in a way that complements, rather than replaces, human interaction. AI can be used to automate tasks that are repetitive or time-consuming, freeing up customer service representatives to focus on more complex issues. However, AI should not be used to replace human interaction entirely. Customers still value the ability to speak to a real person when they need help, and this is critical for complex or sensitive situations. Organisations need to take steps to ensure that their AI algorithms are trained on data that is representative of the population they serve, to avoid biased or discriminatory outcomes. They also need to be transparent about how they are using AI and a suitable regulatory framework should be developed. Customers need to understand how AI is being used to collect and use their data. Businesses should provide clear privacy policies and give customers the ability to opt out of AI-based services. By addressing these challenges, organisations can use AI to deliver a better customer experience. AI can automate tasks, improve efficiency and aid personalisation, but it should do so in a way that is fair and unbiased.
    Keywords: AI, customer experience, customer service, technology, regulation

  • Before design and development: Approaching intelligent automation from multiple perspectives
    Luciana Blaha, Assistant Professor, Edinburgh Business School, Heriot-Watt University

    The purpose of this paper is to provide an overview of current practices in researching, designing and developing intelligent automation (IA) technologies, including artificial intelligence (AI) and robotic process automation (RPA) to match stakeholder needs and values. Consequently, these technologies are reviewed in the context of organisations and digital transformation (DX), with a focus on challenges and perspectives in relation to the groups affected by their use. The paper reviews different perspectives on technology introduction in organisations and the implications for value-driven design and multi-stakeholder approaches to DX. Finally, a series of practical recommendations is made available to developers, managers, and other industry practitioners in relation to improving the meaningfulness and effectiveness of IA systems design, development and adoption.
    Keywords: intelligent automation, digital transformation, stakeholders, value-driven design, AI, RPA

  • Machine learning applications in central banking
    Douglas Araujo, Economist, Bank for International Settlements (BIS), et al.

    Central banks are deploying machine learning (ML) across a variety of use cases, reflecting its potential and usefulness in dealing with an increasingly complex environment. The new techniques can help gather more and better information, which is essential for central banks that rely heavily on data. In addition, a key issue is to make sense of the wealth of data available to derive useful analyses on specific economic and financial situations and, in turn, ensure that the insights gained can effectively back the conduct of evidence-based policies. Yet the deployment of the new tools requires further modifications in central banks’ current operational processes and collaboration models, calling for close cooperation between core IT experts, data scientists and business specialists. It also puts a premium on promoting cooperation between central banks through the sharing of national use cases and on drawing relevant lessons from the experiences observed outside the public community.
    Keywords: central banks, machine learning, big data, evidence-based policy, information management, international cooperation

Volume 2 Number 2

Special Issue: AI and Marketing

  • Editorial: Change is the only constant
    Jim Sterne, Founder, Marketing Analytics Summit and Director Emeritus, Digital Analytics Association
  • Papers:
    The rise of CreAItives: Using AI to enable and speed up the creative process
    Andrew Pearson, Managing Director, Intelligencia

    The ancient Greeks invented the concept of the muse goddess to be a vessel that would enter a human’s life and spark long-desired creativity. The Greek gods did not want to give humans credit for coming up with creativity; however, humans may have invented something as good as a muse — artificial intelligence (AI). Today, AI, machine learning (ML), deep learning (DL) and natural language processing (NLP) could be taking up that ‘muse role’ as well as becoming the workhorse for many a visual artist’s grunge work, and possibly even their creative work as well. In this paper, we will look at how modern creative AI technologies can be viewed through the lens of five groups. We will also explore how recent advances in creative AI offer users the ability to create images, compose music, animation and even video in ways never before possible — and then wrap up with final takeaways on the future of artistic creativity in the era of AI.
    Keywords: artificial intelligence, personalisation, machine learning, deep learning, natural language processing, text-to-image, generative AI, customer relationship management, behavioural marketing, personalisation, creativity, visual art, music, animation, video, video gaming, images

  • Leveraging decision-making AI within marketing operations transformation
    Patrick C. Leary, Creative Director, Publicis Sapient and Michael Misischia, Senior Digital Designer, AllianceBernstein

    Artificial intelligence (AI) digital marketing assists marketing executives in the analysis of targeted audiences and personalising service offerings to customers. The use of data metrics and advanced predictive analytics helps to build efficiently a holistic understanding of targeted customer leads as well as aiding businesses, regardless of size, to run successful digital campaigns focused on targeted audience groups. This paper explores the foundation of targeted AI approaches and areas of potential integration. There is ever-increased opportunity to improve upon digital marketing transformation, being mindful of tailoring an overall strategic approach to speak to business and stakeholder needs as well as in how best to syndicate with multiple discipline teams.
    Keywords: marketing operations, artificial intelligence (AI), big data, automation, user experience (UX), transformation

  • Job sharing between human professionals and chatbots: How should ‘handovers’ happen?
    Alistair Knott, Professor of Artifical Intelligence, Victoria University of Wellington

    Job sharing between humans and artificial intelligence (AI) systems is likely to become increasingly common in several domains of work. In this paper, we examine mechanisms for managing job sharing for one particular class of AI system: human–machine dialogue systems, or chatbots. This is a useful case to consider, as several mechanisms for managing job sharing are already emerging in these systems, and these mechanisms draw on those that human professionals already use to share work among themselves. A key concept in this domain is that of a ‘handover’, where a client is passed from one worker to another. We identify different types of handover, for human and AI workers, and discuss a range of issues that govern how these should take place for effective job sharing. We identify several questions that arise for engineers designing dialogue systems that support handover functionality, relating to timing and transparency. We conclude by arguing that handovers provide a useful way for structuring discussions of job sharing between humans and AI systems, in dialogue-based domains and perhaps beyond.
    Keywords: chatbots, dialogue systems, workplace AI, human–AI job sharing

  • Artificial intelligence and marketing: Driving more ethical strategies
    Tara DeZao, Director of Product Marketing, AdTech and MarTech, Pegasystems Inc

    The advent of machine learning (ML) and artificial intelligence (AI) has driven unimaginable benefits and opportunities across every industry. But just like any technology or innovation, once in the hands of human beings there is an increased potential for ethical abuse — so much so that governments around the globe have leaned in hard to regulate these technologies. Marketers and customer engagement practitioners who leverage these technologies to improve customer engagement can deliver more ethical AI-driven outcomes by governing their strategy with four components: transparency, robustness, fairness and empathy.
    Keywords: artificial intelligence (AI), marketing, ethics, customer engagement, transparency, accountability

  • AI, marketing technology and personalisation at scale
    Allan Tinkler, Head of Platform Development, Quantcast

    From increased consumer demand for privacy and the deprecation of third-party cookies to expectations for more relevant and personalised advertising, the dynamics of the digital advertising industry are quickly evolving. Marketing technology today is challenged with bringing the digital ecosystem together responsibly, aligning what consumers want with what brands can offer in real time. Artificial intelligence (AI) has proven to be an effective tool for synthesising the data needed to accomplish these tasks and deliver results. How exactly is AI bridging the gap between attitudes about privacy and the need for personalisation at scale? What role do deterministic and probabilistic data have in creating a more trustworthy and relevant digital experience? And how can marketers use AI to respond to real-time events and shifts in consumer preferences? This paper will address how AI can help marketers achieve scale without third-party cookies, and why consumer preferences, varied types of data and real-time measurement are central to achieving personalisation in advertising today.
    Keywords: artificial intelligence (AI,) machine learning (ML), digital advertising, data privacy, personalised advertising

  • Smart retail: How AI and IoT are revolutionising the retail industry
    Sandeep Shekhawat, Director of Engineering, Walmart Global Tech

    Technological advancements mean the retail industry is expected to spend US$7.3bn per year on artificial intelligence (AI) from 2022. Retailers that are looking to stay one step ahead are increasingly turning to AI and the Internet of Things (IoT) to help them understand the millions of data points in-store and turn them into insightful information that they can use to improve the speed and effectiveness of their business decisions. This paper demonstrates how AI+IoT (AIoT) can combine to provide a powerful digital transformation experience in retail stores. It also describes some examples of current AIoT implementations in retail stores and explains some of the latest ways retailers can adopt AIoT to drive operational excellence. We will also deep dive into one such example, electronic shelf labels (ESL), showing how these are revolutionising pricing in stores and offering some real-world examples where ESLs are driving efficiency.
    Keywords: smart retail, AI, IoT, Internet of Things, labels, ESLs, electronic shelf labels, wireless, BLE, iBeacon

  • Artificial intelligence for smart bidding
    Pratyush Shandilya, Data Scientist and Laura Murphy Chief Executive Officer, Amplify Analytix and Fernando Perales, Head of the Research Lab and Strategic Partnerships, JOT Internet Media

    Growth in industry digitisation in recent years has resulted in consumers, manufacturers and service providers succumbing to the allure of the online platform, leading to the erosion of the long-standing supremacy of traditional businesses. With increasing competition among manufacturers both at local and global scope, it becomes crucial for them to make intelligent digital marketing decisions which could help get ahead of their competitors. Artificial intelligence (AI) has proven to be an effective medium for supporting decision-making in digital marketing. This paper discusses the application of contextual multi-armed bandits to optimise the bidding strategy used by digital advertisers in main search platforms. The highly scalable algorithm, apart from suggesting a winning strategy in an advertising auction, also enables clients to improve return on investment (ROI) using digital advertising.
    Keywords: ad exchange, advertiser, contextual multi-armed bandits, digital marketing, publisher, reinforcement learning, AI (artificial intelligence), data-driven decision making

  • AI-driven influencer marketing: Comparing the effects of virtual and human influencers on consumer perceptions
    Marvin Böhndel, Junior Product Manager, About You SE & Co. KG, Martin Jastorff, Professor and Christian Rudeloff, Professor, Macromedia University of Applied Sciences

    Computer-generated virtual influencers are currently one of the most important brand communication trends driven by artificial intelligence (AI). While numerous studies on human social media influencers already exist, the field of virtual influencers is still largely unexplored, which is especially true regarding their impact on consumer perceptions. Against this background, the aim of this paper is to empirically investigate consumer perceptions of virtual influencers in comparison with traditional social media influencers. We conduct an exploratory experiment to test the effect of virtual and human influencers on perceived credibility, competence and likeability as well as on purchase intentions. The results show no significant differences between virtual and human influencers, except for the variable likeability. Implications for management and future research are discussed.
    Keywords: virtual influencers, CGI influencers, AI-driven influencers, social media influencers, SMI, influencer marketing, consumer perceptions, purchase intentions

  • Is artificial intelligence the future of the customer experience?
    Chelsea Perino, Managing Director, Global Marketing & Communications, The Executive Centre

    Conversations around customer experience have dramatically evolved since the advent of the Internet and big data. Not only are one-dimensional brand-to-consumer communications now reason to abandon loyalty, but personalised experiences have become an integral part of competitive advantage. An organisation’s ability to derive actional insights from consumer data at scale — including, now, text-based conversation data from customer interactions — is now a driving factor in efficient use of resources and increased profitability. This paper explores the use of artificial intelligence (AI) to build customised and automated user journeys, and how it changes the dynamic of customer service, and perhaps marketing as well, in the modern era.
    Keywords: AI, big data, artificial intelligence (AI), customer experience, automation, marketing, UX

  • Adopting a dynamic AI price optimisation model to encourage retail customer engagement
    Steven Keith Platt, Director of Analytics and Lecturer of Statistics and Applied AI, Quinlan School of Business, Loyola University Chicago and Martin Paul Block, Professor, Medill School of Journalism, Media and Integrated Marketing Communications, Northwestern University

    Technology innovation, changing consumer preferences and behaviours and competition compel successful enterprises to embrace change. Nowhere are these pressures more acute than in the retail industry and, in particular, for those engaged in the sale of fashion merchandise. As this paper will demonstrate, customer engagement (CE) strategies that leverage artificial intelligence (AI) afford retailers the ability to connect with customers in unique ways. The paper focuses on an AI optimisation model that was built for a fashion retailer. The objective was to build a demand prediction price optimisation model to increase margins realised on the clearance of fashion products. While our discussion will focus on that work, we also present techniques whereby such a model can be employed by CE enthusiasts in their businesses. More specifically, we advance that our model can enhance a company’s CE efforts as a method by which it enables a collaborative customer/company value creation system.
    Keywords: customer engagement, artificial intelligence, demand prediction, price optimisation, retail management, retail promotion

Volume 2 Number 1

  • Editorial
    Christopher Johannessen, Editor, Journal of AI, Robotics & Workplace Automation
  • Papers:
    Artificial intelligence (AI) from a regulator’s perspective: The future of AI in central banking and financial services
    Melvin Lopez-Corleone, Senior Delivery Manager, Bank of England, Sholthana Begum, Head of Data and Strategy, Financial Conduct Authority, (FCA) and Gracie Sixuan Li, Innovation Associate, Bank of England

    Artificial intelligence (AI) is unlocking enormous opportunities. For central banks, AI has the potential to enhance regulatory efficiency and improve the data basis for monetary policy decisions. Machine learning (ML) can provide comprehensive, instant, granular information to complement existing macroeconomic indicators as well as having the capability to analyse big data efficiently, which can facilitate monetary policy decisions. As countries and companies conduct AI research and deploy the technology to the public, several financial authorities have recently begun developing frameworks, outlining their expectations on AI governance and use by financial institutions. This paper illustrates the current advancements in ML techniques and highlights the future trends in the adoption of AI by central banks and companies in financial services. It looks at the use of cloud computing and ML by companies and regulators to develop cost-efficient automation tools that better understand user needs, and presents how this will likely enable companies to adapt to rising trends in customer expectation in the future. The paper also explores the growing use of AI in anti-money laundering (AML) procedures, blockchain technology, and the development of Central Bank Digital Currencies (CBDC).
    Keywords: artificial intelligence (AI), CBDC, central banking, regulation, fintech, diversity and inclusion (DEI), COVID-19, machine learning (ML), data, Bank of England (BoE)

  • Using AI to minimise bias in an employee performance review
    Liz Melton, Strategic Partnerships Manager, Coco and Grant Riewe, Chief Technology Officer, Vibrant Emotional Health and Executive Fellow, University of St Thomas – Opus College of Business

    Performance reviews are intended to be objective, but all humans experience bias. While many companies opt for group reviews as a way to de-bias and challenge the status quo, what is being said in those meetings, how those comments are said and the context for those remarks are just as important. At the same time, most people’s attention span is of shorter duration than a review and being promoted depends on what bosses remember about their direct reports, their subjective measure of employee success, and their ability to convince others that employee accomplishments are deserving of a reward. As a result of these compounding factors, meta-bias patterns emerge in company culture. Combine those limitations with the fact that reviews are often a breeding ground for subtle — and not-so-subtle — bias, and it begs the question: Why are we not using technology to help? With developments in natural language processing (NLP) and conversational AI (CAI), computers can identify biased phrases in real time. Although these technologies have a long way to go to match human nuance, we can at least flag problematic phrases during something as significant as performance reviews. And with the right inputs rooted in social science and normalised based on geography, contextual relationships and culture, we could be surfacing insidious bias throughout organisations. This paper examines how a future CAI tool could reduce bias and, eventually, teach people to re-evaluate and reframe their thinking. In a performance review setting, the system would flag problematic phrases as they are said, and committee heads would stop the conversation. The committee would then evaluate the comment, ask the presenter for further information, and only continue once there is sufficient clarity. Once the discussion concludes, the review cycle would continue until another phrase is identified. The system serves to be persistently aware throughout all conversations and highlight potential bias for everyone to learn from. Beyond pointing out biased phrases during a performance review, a combination of NLP and CAI can serve as a foundation for company-wide analytics. Organisations can track who is speaking in a majority of meetings, what was said, who challenges biased phrases, whether or not certain types of people are misrepresented in reviews more or less frequently, and so on. All this information gives a fundamentally new picture of what is happening inside a company, laying the groundwork for human resource (HR)-related metrics that individuals (and the company as a whole) can improve over time.
    Keywords: bias, bias detection tool, bias detection system, AI, performance evaluations, review process, performance reviews, performance review

  • Human–machine collaboration in transcription
    Corey Miller, ASR Research Manager and Migüel Jetté, Vice President of AI, Rev and Dan Kokotov, Chief Technology Officer, CNaught

    As automatic speech recognition (ASR) has improved, it has become a viable tool for content transcription. Prior to the use of ASR for this task, content transcription was achieved through human effort alone. Despite improvements, ASR performance is as yet imperfect, especially in more challenging conditions (eg multiple speakers, noise, nonstandard accents). Given this, a promising way forward is a human-in-the-loop (HIL) approach. This contribution describes our work with HIL ASR on the transcription task. Traditionally, ASR performance has been measured using word error rate (WER). This measure may not be sufficient to describe the full set of errors that a speech-to-text (STT) pipeline designed for transcription can make, such as those involving capitalisation, punctuation, and inverse text normalisation (ITN). It is therefore the case that improved WER does not always lead to increased productivity, and the inclusion of ASR in HIL may adversely affect productivity if it contains too many errors. Rev.com provides a convenient laboratory to explore these questions. Originally, the company provided transcriptions of audio and video content executed solely by humans (known as Revvers). More recently, ASR was introduced in an HIL workflow where Revvers postedited an ASR first draft. We provide an analysis of the interaction between metrics of ASR accuracy and the productivity of our 72,000+ Revvers transcribing more than 15,000 hours of media every week. To do this, we utilise two measures of transcriptionist productivity: transcriber real time factor (RTF) and words per minute (WPM). Through our work, we hope to focus attention on the human productivity and quality of experience (QoE) aspects of improvements in ASR and related technologies. Given the broad scope of content transcription applications and the still elusive objective of perfect machine performance, keeping the human in the loop in both practice and mind is critical.
    Keywords: speech recognition, transcription, accuracy, productivity, artificial intelligence, postediting

  • High-impact AI: How to achieve business goals while making the world a better place
    Michael Griffin, Chief Data Scientist, Insight, et al.

    A vast amount of wealth has been generated by the explosive growth of artificial intelligence (AI)-enabled applications, and that wealth generation is accelerating. This paper describes how both monetary and non-monetary wealth — the kind of wealth that makes the world a better place — can be generated by the same intelligent application. The paper introduces the term ‘high-impact AI’, which is the practice of achieving business goals while simultaneously benefiting society. Novel and practical examples of achieving this lofty goal are described in the areas of manufacturing, where AI-powered software enables people with disabilities to perform jobs they would otherwise not be qualified to perform; healthcare, where the incredible complexity of practising medicine has been simplified to potentially improve the health and prolong the lives of millions of people; and psychology, where a tremendous variety of human interactions can be quantified and optimised in real time to assist strengthening and enhancing relationships of all kinds. Finally, this paper makes the case that practising high-impact AI will help businesses attract and retain talented people by giving them meaningful work that matters.
    Keywords: ethical artificial intelligence (AI), assisted communication, manufacturing, healthcare, psychology, enhanced order fulfilment, automated inventorying

  • Improved credit default prediction using machine learning and its impact on risk-weighted assets of banks
    Martin Neisen, Partner and Petr Geraskin, Senior Manager, PricewaterhouseCoopers

    The use of risk models, especially credit risk models, has been a standard for banks for many years. Banks use models not only for business decision purposes but also for regulatory purposes when comparing their risk with the available regulatory capital. Furthermore, banks need to efficiently allocate their capital in the current competitive and regulatory environments. As part of this process, they develop models to predict the probability of default (PD), which are further used to calculate risk-weighted assets (RWA). This paper gives an overview of how banks calculate RWA for credit risk. We compare the performance of traditional PD models based on logistic regression with a machine learning (ML) algorithm based on gradient boosting. This shows that an improvement in PD model performance by using ML algorithms can also lead to a decrease of RWA, therefore releasing additional capital for the banks. We developed and calibrated PD models based on logistic regression and light gradient boosting machine (GBM) approaches and compared them in terms of discriminatory power and the impact on RWA to prove this statement. This paper shows that the use of ML leads in our case study to an improvement in the model’s discriminatory power of 5 per cent in terms of Gini and releasing RWA of approximately 6.5 per cent.
    Keywords: Basel IV, artificial intelligence, machine learning, credit risk, banking regulation

  • Six ways in which AI is delivering value for organisations drawing on real-world case studies and offering actionable insights
    Nitin Mittal, US AI Co-leader, Deloitte Consulting and Irfan Saif, US AI Co-leader, Deloitte Risk & Financial Advisory

    After decades as science fiction fantasy, artificial intelligence (AI) has made the leap to practical reality and is quickly becoming a competitive necessity. Yet, amid the current frenzy of AI advancement and adoption, many leaders and decision makers still have significant questions about what AI can actually do for their businesses. This paper highlights compelling, business-ready use cases for AI across six major industries: consumer; energy, resources and industrials; financial services; government and public services; life sciences and health care; and technology, media and telecommunications. The goal is to give readers a much clearer sense of what AI is capable of achieving in a business context — now, and over the next several years — so that business leaders and AI practitioners can make smart decisions about when, where and how to deploy AI within their own organisations and how much time, money and attention they should be investing in it today.
    Keywords: artificial intelligence (AI), machine learning (ML), innovation, automation, technology adoption

  • On the predictability of long-term stock market returns: Design configuration of deep neural networks
    Manfred Herdt, Research Assistant and Hermann Schulte-Mattler, Professor, Dortmund University of Applied Sciences and Arts

    In 1998, Robert J. Shiller and John Y. Campbell proposed that long-term stock market returns are not random walks and can be predicted by a valuation measure called the cyclically adjusted price-to-earnings (CAPE) ratio. This paper is set to identify the predictive power of long-term stock market returns with deep neural networks and trace the impact of different architectural components of deep neural networks. We present three network types — recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent units (GRU) neural network — to ascertain what impact the different networks have on predicting long-term stock market returns and whether a parsimonious neural network model (PNNM) can be identified for practical application. The networks above have different design features that allow returns to be predicted and the effects of the various elements of the networks to be understood. For our study, we use monthly CAPE ratios and real ten-year annualised excess returns of the S&P 500 from 1881-01 to 2012-06, with data from 1876-06 (real earnings) to 2022-06 (real total return price) needed to determine the two datasets. Our results show improved forecasting accuracy over linear regression for all analysed neural networks. Only the complex trial-and-error procedure leads to the network design with the optimal result of minimising the root-mean-squared error (RMSE). This approach is usually associated with a considerable time and cost factor. Therefore, for time series studies of the present type, we propose a parsimonious GRU architecture with low complexity and comparatively low out-of-sample error, which we call ‘GRU-101010’.
    Keywords: cyclically adjusted price-to-earnings (CAPE) ratio, gated recurrent units (GRU) neural network, long short-term memory (LSTM) neural network, neural network’s architecture, neural network’s hyperparameters, recurrent neural network (RNN), time series analysis.