Are there parallels between the Industrial Revolution of the eighteenth century and the changes now being wrought on twenty-first-century society by recent advances in AI and robot technology? And, if so, what are some of the consequences for which we should prepare ourselves this time round?
Dawn of the Service Revolution1
The industrial revolutions started in the late eighteenth century and automated blue-collar jobs in manufacturing, thereby providing massive structural benefits to our societies. They rapidly increased our standard of living by bringing high-quality, low-cost manufactured goods to the masses, and relieved people from laborious manual work.
Today, our economies seem to face a turning point similar to the industrial revolution, but this time in the service sector. Technologies rapidly become smarter and more powerful, while, at the same time, they get smaller, lighter and cheaper. These technologies include hardware such as that related to physical robots, drones and autonomous vehicles and their components (e.g., processors, sensors, cameras, chips), wearable technologies, and code or software such as analytics, speech processing, image processing, biometrics, virtual reality, augmented reality, cloud technologies, mobile technologies, geo-tagging, low-code platforms, robotic process automation (RPA) and machine learning. Together, these technologies will transform virtually all service sectors. Service robots and artificial intelligence (AI), combined with these technologies, will lead to rapid innovation that can dramatically improve the customer experience, service quality and productivity, all at the same time.2
Robot- and AI-delivered service offers unprecedented economies of scale and scope, as the bulk of the costs are incurred in their development.
Robot- and AI-delivered service offers unprecedented economies of scale and scope, as the bulk of the costs are incurred in their development. Physical robots cost a fraction of adding to the headcount, and virtual robots can be deployed at negligible incremental cost. Virtual service robots (e.g., chatbots and virtual agents) can be scaled at close to zero incremental cost. Such dramatic scalability does not apply only to virtual service robots such as chatbots, but also to ‘visible’ ones such as holograms. For example, an airport could install a hologram-based humanoid service robot every 50 metres to assist passengers and deal with common questions (e.g., provide arrival and departure information, directions to check-in counters for a particular airline, and an airport hotel) in all common languages. These holograms only require low-cost hardware (i.e., a camera, microphone, speaker and projector) and do not need to take up floor space (travellers can push their baggage carts through a hologram when it gets crowded).
Already, many firms are showing eager interest in experimenting with service robots. For example, hotels are introducing humanoid robots in their lobbies, where they welcome guests, provide information and entertain guests. At airports, they scan boarding passes and help passengers to find the right departure gate. Self-moving check-in-kiosk robots detect busy areas and autonomously go there to help passengers reduce waiting time. Particularly, the outbreak of COVID-19 has increased the demand for medical service robots that check people’s temperature or take over disinfection work3. The market size for service robots is projected to reach USD 41.5 billion by 2027.4
Such robots in hotels, airports and restaurants, chatbots and delivery bots are only the beginning of the service revolution. This means that, similar to the shift that started in the industrial revolution from craftsmen to mass production, an accelerated shift in the service sector towards robot- and AI-delivered services can be expected. The exciting prospect is that many services, including healthcare and education, are likely to become available at much lower prices and much better quality, and lead to a dramatic increase in our standard of living.
What Are Service Robots and How Are They Different from Current Self-Service Technologies?
Service robots have been defined as “system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organisation’s customers”.5 These abilities differentiate service robots from traditional self-service technologies (SSTs) that we are familiar with in the context of ticketing machines, websites and apps. As shown in table 1, service robots can deal with unstructured interactions and guide customers through their service journey. For example, a ticketing robot will not let customers get stuck, as it can ask clarifying questions (e.g., “Is your return trip today?” “Can you travel off-peak?) and can even recover customer errors (e.g., a wrong button pressed, incorrect information entered or a rejected credit card). For most standard services, customers will interact with service robots in much the same way as with service employees (e.g., “I need a same-day return ticket and can I use Apple Pay?”).
What Are the Differences Between Service Robots and Human Employees?
Robots are not able to feel and express real emotions. This can be important in some services, whereby the service management literature distinguishes between deep acting (i.e., employees displaying real emotions) and surface acting (i.e., they show superficial fake emotional responses).7 In contrast, a robot’s emotions are just displayed and not authentic. Consumers generally know this and respond accordingly. On the other hand, robots can surface-act and consistently be pleasant; they are not prone to emotional burnout. This may make robots perform better than humans in jobs that require display of surface-acted emotions. Other significant differences are summarised in table 2.
What Services Will Be Delivered by Robots?
Initial deployments of service robots focused on simple and repetitive tasks that tended to be low in their cognitive and emotional complexity (figure 1). For example, physical robots in hotels deliver room service and bring baggage to guest rooms. Text and voice-based conversational agents increasingly handle routine customer interactions. Even when interacting with a human service employee, that employee may well be supported by AI, and calls are pre-screened, preprocessed and then escalated to the human agent because of their complexity. The outcome is that customer contact staff do not have to deal with high volumes of trivial customer requests but instead can spend their time on higher-value and higher-level tasks. For example, a chatbot for the NUS MBA Programme handled 20,000 unique conversations per month right after launch and answered all the routine questions the admission team had to deal with previously (e.g., “Do I need a GMAT?” “When are the fees payable?” and “When is the application deadline?”). The admission team can now focus on top-quality candidates and the trickier and more complex discussions.9
In addition to routine tasks, services that require high cognitive and analytical skills will be delivered effectively by service robots (e.g., financial services). For example, service robots can analyse large volumes of data, integrate internal and external information, recognise patterns and relate these to customer profiles. Within minutes, these robots can propose best-fitting solutions and make recommendations.
It is difficult for robots to deal with emotions that go beyond a pleasant display of surface demeanour.
It is difficult for robots to deal with emotions that go beyond a pleasant display of surface demeanour. Especially complex and emotionally demanding tasks are still better handled by service employees, as they can bring genuine emotions such as excitement and joy or empathy and compassion to the service encounter. For example, in complaint and service recovery situations, humans can respond better to the individual context and show understanding.
Human-robot teams will increasingly deliver tasks that require high cognitive and emotional skills. For example, in a healthcare context, service robots will do the analytical work (e.g., analyse symptoms and compare them with databases to identify possible diagnoses), and humans will make the final recommendations and decisions and take over the social and emotional tasks (e.g., advising and persuading patients). For example, the first author’s daughter returned from Singapore to Munich with dengue fever; the symptoms only showed a week after her return. General practitioners in Germany may never see a dengue fever patient in their professional life and may not be effective in diagnosing it. On the other hand, a service robot compares patient data and symptoms and provides a ‘hit list’ of possible diseases with a fit index. The general practitioner can then work down the list and discuss with the patient (e.g., “Have you been in the tropics in the last two weeks?”) and then identify the most likely diagnosis and test for it.
Implications for Service Organisations
This revolution of the service sector will have enormous implications for business. Some of the most pressing issues for service organisations to tackle include11:
Implication 1. Restructure the Service Frontline.
With the implementation of service robots, organisations will inevitably transform and be dramatically reorganised. This requires strong leadership and support, and the willingness and ability of employees to change. That is, employees will be assigned to new tasks and responsibilities and will need to develop the required skills (incl., RPA, basic programming and technology troubleshooting).
Implication 2. See Robots as a Long-Term Investment.
The deployment of service robots comes with investments, including acquisition costs, development of IT specialists and programmers, and building virtual networks and maintenance of systems. It takes some time for these investments to be recouped; typically less than 12 months for successful implementations.12
Implication 3. Al as an opportunity for Cost-Effective Service Excellence.
We predict that hybrid human-robot teams and collaboration will be the service model of the future for many more complex service contexts. These hybrid teams will be able to realise productivity and service quality gains for the company by combining the advantages of AI and human employees. Robots’ enormous knowledge and data is an undeniable advantage for creating customised services. Organisations should focus on implementing, managing and fine-tuning the deployment of robot-employee-customer co-creation teams to deliver unprecedented quality of interaction for their customers.13
Implication 4. Mitigate Potential Risks of Robot Deployment.
Organisations need to mitigate potential misconceptions, prejudice and anxieties related to customer-facing service robots, such as algorithm aversion, perceived loss of the human touch, and consumer privacy. This requires organisations to embrace corporate digital responsibility (CDR) and develop a set of shared values, norms and actionable guidelines on the responsible use of technology along the full cycle. For example, related to data, it includes their capturing (e.g., using biometrics or social media accounts), their use (e.g., building variables such as a healthiness index or financial score), decision-making (e.g., approving loans and setting interest rates), and their retirement (e.g., when information on a bounced payment should be deleted from the firm’s database).14
In summary, service robots and AI will transform our service sector and bring unprecedented improvements to the customer experience, service quality and productivity, all at the same time.15 In turn, this service revolution has the potential to dramatically increase our standard of living, very much as the industrial revolution did for manufactured goods. The difference is that, this time, it is services such as financial, logistics, healthcare and education that are being industrialised.
About the Authors
Jochen Wirtz is Vice Dean MBA Programmes and Professor of Marketing at the National University of Singapore. He is also an international fellow of the Service Research Center at Karlstad University, Sweden, an academic scholar at the Cornell Institute for Healthy Futures (CIHF) at Cornell University, US, and at the Global Faculty of the Center for Services Leadership (CSL) at Arizona State University, USA. Dr Wirtz is a leading authority on service management and has over 200 academic publications, including six features in Harvard Business Review and over 20 books. His latest books include Intelligent Automation – Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human (2021) and Services Marketing: People, Technology, Strategy (9th edition, 2021). Watch Jochen’s Master Class to better understand service robots and their implications – https://www.youtube.com/c/ProfessorJochenWirtz
Werner H. Kunz is Professor of Marketing and director of the digital media lab at the University of Massachusetts Boston. His research interests are in AI, robots, digital and social media, social networks, innovation and service research. His work has been published, amongst others, in the International Journal of Research in Marketing, Journal of Retailing, British Journal of Management, Journal of Medical Internet Research, Journal of Business Research, Journal of Service Management and Computational Statistics and has been awarded multiple times. He is founder and host of the Social Media Days at UMass Boston and current board member of the Service Research Special Interest Group (SERVSIG) of the American Marketing Association (AMA), the primary professional association of service research, with over 2,000 community members worldwide.
Stefanie Paluch is Professor for Services and Technology Marketing at RWTH Aachen University. She is a research fellow at King’s College in London and she was appointed Senior Fellow at Hanken School of Economics in Helsinki. Her research focuses on the perception and acceptance of AI, e.g., service robots and smart services, by consumers, and their implementation in an organisational context. She publishes her research in leading international journals, such as the Journal of Service Research, Journal of Business Research, Journal of Service Management, Journal of Service Marketing and the Journal of Service Theory and Practice.
1 This article draws on Jochen Wirtz, Paul Patterson, Werner Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, and Antje Martins (2018), “Brave New World: Service Robots in the Frontline”, Journal of Service Management, Vol. 29, No. 5, p. 909, https://doi.org/10.1108/JOSM-04-2018-0119; Pascal Bornet, Ian Barkin and Jochen Wirtz (2021), Intelligent Automation – Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human, https://intelligentautomationbook.com; Jochen Wirtz (2020), “Organizational Ambidexterity: Cost-Effective Service Excellence, Service Robots, and Artificial Intelligence”, Organizational Dynamics, Vol. 49, No. 3, https://doi.org/10.1016/j.orgdyn.2019.04.005.
2 Jochen Wirtz and Valarie Zeithaml (2018), “Cost-Effective Service Excellence”, Journal of the Academy of Marketing Science, Vol. 46, No. 1, pp. 59-80. https://link.springer.com/article/10.1007/s11747-017-0560-7
3 Paluch, Stephanie, Wirtz, Jochen and Kunz, Werner H. (2020), “Service Robots and the Future of Service”, in Marketing Weiterdenken – Zukunftspfade für eine marktorientierte Unternehmensführung, 2nd ed., Bruhn, M. and Kirchgeorg, M., and Burmann, C., eds., Springer Gabler-Verlag, pp. 423-435, https://doi.org/10.1007/978-3-658-31563-4
4 Service Robotics Market Size Report and Industry Forecast, Fortune Business Insights, 2020. https://www.fortunebusinessinsights.com/industry-reports/service-robotics-market-101805
5 Jochen Wirtz, Paul Patterson, Werner Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, and Antje Martins (2018), “Brave New World: Service Robots in the Frontline”, Journal of Service Management, Vol. 29, No. 5, p. 909, https://doi.org/10.1108/JOSM-04-2018-0119;
6 Adapted from Jochen Wirtz, Paul Patterson, Werner Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, and Antje Martins (2018), “Brave New World: Service Robots in the Frontline”, Journal of Service Management, Vol. 29, No. 5, p. 909, https://doi.org/10.1108/JOSM-04-2018-0119.
7 Jochen Wirtz and Christina Jerger (2017), “Managing Service Employees: Literature Review, Expert Opinions, and Research Directions”, Service Industries Journal, 36(15-16), 757-788.
8 Adapted from Jochen Wirtz, Paul Patterson, Werner Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, and Antje Martins (2018), “Brave New World: Service Robots in the Frontline”, Journal of Service Management, Vol. 29, No. 5, p. 909, https://doi.org/10.1108/JOSM-04-2018-0119.
9 Try this chatbot at https://mba.nus.edu.sg/.
10 Adapted from Jochen Wirtz, Paul Patterson, Werner Kunz, Thorsten Gruber, Vinh Nhat Lu, Stefanie Paluch, and Antje Martins (2018), “Brave New World: Service Robots in the Frontline”, Journal of Service Management, Vol. 29, No. 5, pp. 907-931, https://doi.org/10.1108/JOSM-04-2018-0119.
11This section is based on Paluch S., Wirtz J., Kunz W.H. (2020) Service Robots and the Future of Services. In: Bruhn M., Burmann C., Kirchgeorg M. (eds) Marketing Weiterdenken. Springer Gabler, Wiesbaden. S. 423-435 https://doi.org/10.1007/978-3-658-31563-4_21
12 Pascal Bornet, Ian Barkin and Jochen Wirtz (2021), Intelligent Automation – Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human, https://intelligentautomationbook.com;
13 Jochen Wirtz (2020), “Organizational Ambidexterity: Cost-Effective Service Excellence, Service Robots, and Artificial Intelligence”, Organizational Dynamics, Vol. 49, No. 3, https://doi.org/10.1016/j.orgdyn.2019.04.005.
14Lara Lobschat, Benjamin Müller, Felix Eggers, Laura Brandimarte, Sarah Diefenbach, Mirja Kroschke and Jochen Wirtz (2020), “Corporate Digital Responsibility”, Journal of Business Research, published online first.
15 Jochen Wirtz and Valarie Zeithaml (2018), “Cost-Effective Service Excellence”, Journal of the Academy of Marketing Science, Vol. 46, No. 1, pp. 59-80. https://link.springer.com/article/10.1007/s11747-017-0560-7