Choosing a research topic can be hard, it is made even harder when you are given broad topics such as poverty, the animal, the robot, the politician, the future of work and the planet and then told to do anything you find interesting. After much consideration and four spur of the moment changes to my choice I have finally settled on (I hope) looking into the robot. Going into my third year at university doing a Bachelor of Communications and Media the use of devices and media in all aspects of life (social, workplace, leisure, etc.) is a central concept to most of the work I do. Currently I plan to look into how the use of digital media and the use of robots is leading to a digital workforce and identify the prevalence of the digital workforce in business, its growth and some of the pros and cons of having a digital workforce.
Essentially, the research I am currently planning to do revolves around answering the question of “the growth and applications of the digital workforce in modern business”. I also planned to analyse the effects of automating jobs in the workplace and the consequences of doing so but I am currently somewhat reluctant to include it in the actual research because this question is one that, although has plenty of research already done into it, is still very opinionated and I hope to avoid speculation to the highest possible extent while doing my research unless it is forecasting results based on current and past trends.
Before going into the importance/relevance of my chosen topic it is important to understand the definition of the digital workforce and the major components that make it up. The digital workforce can seem somewhat confusing at first but is actually a simple concept, it is the term used to describe the use of automated processes/robots to complete tasks that would otherwise of been done by humans in a work environment (White and Grueger, 2017). There are many different types of software and robots that do various jobs and make up the digital workforce and are made up of everything from cloud computing and virtual customer assistants, to quantum computing (don’t worry this is the
for dummies super-easy to understand link) and general purpose machine learning (Link to an article on machine learning, general purpose describes how it can be used for a large variety of things (your iPhone is a general purpose piece of tech, although it doesn’t have the capacity to learn… yet)). All aspects of the digital workforce can currently be put into three categories or evolutions (with an evolution being a level of implementation by business), these categories are:
Robotic Process Automation is the most widely implemented level of the digital workforce and is seen in most businesses as it includes the most mature solutions. Things like simple chat bots that follow a predefined set of actions and processes allow them to follow specific courses of action and provide highly reliable solutions to well defined issues. But, on the down side, are limited to whatever restraints the business has put on them. Take for example a chat bot on messenger (Facebook) for a travel company which may suggest travel destinations and packages based on questions and answers that have already been pre-determined by the organisation.
Cognitive computing, as the name suggests, is the recreation of human thought processes in a robot or piece of software. Most people use the functions of cognitive computing on a daily basis when interacting with their phones through the use of predictive text, live traffic data, live weather reports and interact with digital assistants such as Siri on Apple products. Returning to Deloitte’s 2017 study it states that cognitive computing is on track to affect/transform the lives of individuals and businesses the most the most in the next 20 years. Furthermore stating that by 2020 95% of the biggest 100 software companies (based on revenue) will be making use of cognitive computing (White and Grueger, 2017).
Artificial intelligence refers to a decision making process made by machines which takes into account multiple environmental factors and then takes action/makes decisions that maximises its chances at success for a certain goal. Artificial intelligence in machines takes long periods of time to develop because unlike with RPA where predefined variables have been entered artificial intelligence learns from previous experiences (failures, success, other environmental factors, etc.) to create a more reliable database of solutions. Although it is widely believed that we will never be able to create a machine with “perfect” human intelligence it is undeniable that we are making rapid progress in the realm of AI so to rule out the possibility entirely seems ridiculous.
Most people have heard stories about how “machines are taking our jobs” or “putting people out of work” and although in many cases this is somewhat true, jobs are always changing and evolving and the people working them must be able to as well to remain relevant in a business. The best way to do this, is to understand the ideas and processes behind the digital workforce and adapt accordingly. But even with a large amount of jobs being made redundant due to machines the idea that robots are going to take over all jobs in the near future is not feasible, and even if it was there is plenty of research being done into the new types of jobs that will be required to either work with, teach, or maintain these robots (Autor, 2015). Therefore I think this study, although already done by others, is important because there is still a lot of fear around the digital workforce, a lot of which seems to come from a lack of understanding. Fear, that I hope to do my part in reducing through my research project.
Autor, D. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, [online] 29(3), pp.3-30. Available at: https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.29.3.3.
Azure.microsoft.com. (n.d.). What is cloud computing? A beginner’s guide | Microsoft Azure. [online] Available at: https://azure.microsoft.com/en-in/overview/what-is-cloud-computing/ [Accessed 17 Mar. 2019].
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Boulton, C. (2018). What is RPA? A revolution in business process automation. [online] CIO. Available at: https://www.cio.com/article/3236451/what-is-rpa-robotic-process-automation-explained.html.
Broadsoft.com. (2016). 5 steps to Build a Digital Workforce. [online] Available at: https://www.broadsoft.com/work-it/how-to-build-a-digital-workforce.
Chui, M., Manyika, J. and Miremadi, M. (2015). Four fundamentals of workplace automation. [ebook] McKinsey & Company. Available at: https://roubler.com/au/wp-content/uploads/sites/9/2016/11/Four-fundamentals-of-workplace-automation.pdf.
Dorsey, B. (2017). Machine Learning: A Journey Towards General-Purpose Algorithms. [Blog] Branger_Briz. Available at: https://brangerbriz.com/blog/machine-learning-a-journey-towards-general-purpose-algorithms/.
Marr, B. (2016). What Everyone Should Know About Cognitive Computing. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2016/03/23/what-everyone-should-know-about-cognitive-computing/#24fdbc105088.
Marr, B. (2017). What Is Quantum Computing? A Super-Easy Explanation For Anyone. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2017/07/04/what-is-quantum-computing-a-super-easy-explanation-for-anyone/#3369e6b61d3b.
Sas.com. (n.d.). Artificial Intelligence – What it is and why it matters. [online] Available at: https://www.sas.com/en_au/insights/analytics/what-is-artificial-intelligence.html [Accessed 17 Mar. 2019].
White, N. and Grueger, D. (2017). Managing The Digital Workforce. [ebook] Deloitte. Available at: https://www2.deloitte.com/content/dam/Deloitte/au/Documents/human-capital/deloitte-au-hc-managing-digital-workforce-131017.pdf.
Workplace Basics: The Essential Skills Employers Want. ASTD Best Practices Series: Training for a Changing Work Force. (1990). 1st ed. [ebook] San Fransisco: Jossey-Bass Inc. Available at: https://eric.ed.gov/?id=ED319979.