Will Automation and Artificial Intelligence Disrupt the Future Workplace?
The Workplace has gone through a transformation over the last century from a manual driven process to a mechanized process to an automated process. Today the buzzword in Industrial Workplace is Automation and Artificial Intelligence. Companies are deploying robots, chat bots and humanoids to get tasks accomplished that were hitherto being done by human beings. TV Mohandas Pai, Chairman of Manipal Global and ex-CFO of Infosys, has predicted that 2,25,000 jobs will be lost in next one decade due to automation.
Companies are currently using Cognitive Computing, Machine Learning and Deep Learning to maximize their output and return-on-investment (ROI) from the Workplace. Cognitive Computing is being used to handle complex, ambiguous situations and enable more “human-like” interactions with software. Here, human thought processes are simulated through data mining, intelligence, automation and natural language processing. Machine learning is used to automate the building of systems by learning from data. It identifies patterns and predicts future results with minimal human intervention. Deep Learning is where Machine Learning meets Big Data and Analytics. Deep Learning uses Neural Networks to structure a computer like the human brain — complete with neuron-like nodes connected together.
Will Automation and Artificial Intelligence disrupt the Future Workplace? How will the Workplace of Future look like? What are the skill-sets that employees of tomorrow need to acquire to face the challenges of tomorrow? How will Industrial Robots and Human Workers complement each other? This research paper will seek to understand how Artificial Intelligence and Business Analytics will reshape Future Workplace.
Keywords: Automation, Artificial Intelligence, Cognitive Computing, Machine Learning, Deep Learning
The Problem Statement
The quest for creating a great workplace that boosts productivity has been a challenge for social scientists and corporate leaders for the last hundred years. The experts have tried several approaches like creating a conducive work environment in earlier days and the use of AI (Artificial Intelligence) and IoT (Internet of Things) in recent times to create the perfect workplace.
The objective of this research paper is to understand whether Automation and Artificial Intelligence (AI) will disrupt the future workplace and what would be the positive and negative outcomes of the same.
We have done a descriptive research using secondary data. A sample study was done on 50 International Companies and 50 Indian Companies to understand the trends, patterns and insights on what role Automation and AI (Artificial Intelligence) is playing to create the perfect workplace. The Hypothesis Testing was validated using Factor Analysis. The information used is secondary data collected from journals, magazines, newspaper articles and authentic research papers.
In the late 1920 and early 1930, a group of Scientists at Western Electric’s factory at Hawthorne, USA, did a study to understand what factors influenced productivity at workplace. The experiments were conducted under the supervision of Elton Mayo, who eventually became a Professor at Harvard University. The experiments comprised of making workers work in two groups. In one group, the lighting was improved dramatically while the other group’s lighting was kept unchanged. The researchers were surprised to find that the productivity of the more highly illuminated workers increased much more than that of the control group.
The Scientists did further tests by changing the other parameters like working hours, rest breaks and so on, and in all cases their productivity improved when a change was made. Indeed, their productivity even improved when the lights were dimmed again. By the time everything had been returned to the way it was before the changes had begun, productivity at the factory was at its highest level. Absenteeism had plummeted.
After thoroughly investigating and analyzing the findings, the Scientists concluded that it was not the changes in physical conditions that were affecting the workers’ productivity. Rather, it was the fact that someone was actually concerned about their workplace, which gave the impetus to perform better.
Since then, numerous interventions have been done to design a workplace that will provide enhanced productivity. Infosys created a college campus like environment replete with cafeteria, gym, swimming pool,movie theatres, ATM and shopping malls. Google has created a workplace that features Lego stations, Ping-Pong tables, and a secret ladder that runs between floors. A key characteristic of the Google Campus that makes it so special is its playfulness and overall fun feeling. Employees at Googleplex are also treated to free healthy snacks like dried fruit, energy bars, and coconut water.
However, despite all these interventions, the challenges of productivity, attrition and loyalty to the organization has increased by the day. The attrition rate of Infosys went up to 21% in 2016. TCS has an attrition rate of 16%, Wipro has 16.5% and HCL has 18.6%. The companies have offered ESOP (Employee Stock Option Plan), Flexible Working Hours and several key benefits for better work life balance but the results have been far from being encouraging.
In sheer exasperation, the corporates have now moved towards automation and artificial intelligence to reduce dependency on human workforce. We already have ATM (Automated Teller Machines) that dispense cash, robots that work in assembly line manufacturing and chat bots that answer queries to perform customer relationship management (CRM). HDFC Life, one of India’s leading private life insurance companies, announced the launch of India’s first life insurance chatbot in collaboration with Haptik, India’s largest chatbot platform. The chatbot will act as a financial guide to help users choose the most suitable life insurance plans and solutions.
Figure-1 How Chat Bots are Adding Value to the Workplace
The Chinese Mobile Phone manufacturing company, Foxconn, who is mainly a third party manufacturer of Apples i-Phones and Samsung, has gone for automation to replace human workers. In Foxconn’s Zhengzhou factory, its largest and most capable plant that produces 500,000 i-Phones a day, it has reduced the size of workforce from 1,10,000 to 50,000. The company has reduced 60,000 factory workers with robots.
Cognitive Analytics, Deep Learning and Machine Learning
Figure-2 Advent of Artificial Intelligence (AI), Deep Learning and Machine Learning
What are the technologies that are contributing to the rise of Automation and Artificial Intelligence? Although there are several key variables and affected factors, we have identified three key variables through Factor Analysis of available data. The three key variables are Cognitive Analytics, Deep Learning and Machine Learning.
Cognitive Analytics is a field of analytics that tries to mimic the human brain by drawing inferences from existing data and patterns, drawing conclusions based on existing knowledge bases and then inserting this back into the knowledge base for future inferences. Cognitive Computing is being used to handle complex, ambiguous situations and enable more “human-like” interactions with software. Here, human thought processes are simulated through data mining, intelligence, automation and natural language processing.
Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is used to automate the building of systems by learning from data. It identifies patterns and predicts future results with minimal human intervention. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. As per data given by SAS, Machine learning is now being used by Financial Services, Healthcare, Oil & Gas, Logistic and Government companies.
Deep Learning is where Machine Learning meets Big Data and Analytics. Deep Learning uses Neural Networks to structure a computer like the human brain — complete with neuron-like nodes connected together. Deep Learning represents the true bleeding edge of Machine Learning. Today, image recognition by machines trained via deep learning in some scenarios is better than humans, like identifying indicators for cancer in blood and tumors in MRI scans.
IBM recently came out with their disruptive technology called Watson, which is attempting to replicate the human brain through ANN (Artificial Neural Networks). Watson is revolutionizing the way decisions are made across industries by uncovering patterns in new and intelligent ways. Watson uses natural language processing and machine learning, among other artificial intelligence technologies, to reveal insights from large amounts of unstructured data. It employs reasoning strategies that evolve to become more sophisticated and handle increasingly complex problems.
IBM Watson, the world’s broadest platform of cognitive technologies, is effectively combining an understanding of how humans communicate – through metaphors, idioms and natural language – with machine capabilities such as deep learning and large-scale mathematics, to provide useful insights from vast amounts of unstructured data. Watson can analyse and understand natural language such as tweets, text, articles, studies and reports and at the same time, make contextual sense out of videos and images.
Watson’s cognitive computing capabilities address the needs of industries ranging from healthcare and scientific research to customer service, retail and financial services. Watson is at work scaling human expertise and drawing insights from data in both established businesses that need design innovative solutions for recurring issues, and with start-ups that need to crystallize their creativity into real businesses.
These developments are now acting as a threat as well as opportunity. The fear is that there will be massive job losses due to Automation and Artificial Intelligence. TV Mohandas Pai, Chairman of Manipal Global and ex-CFO of Infosys, has predicted that 2,25,000 jobs will be lost in next one decade due to automation (31st July, 2016, Economic Times).
The opportunity lies in improved productivity and shifting of the labor to high quality jobs with better pay. Automation and Artificial Intelligence can also improve quality or increased predictability of quality, improved robustness (consistency) of processes or product, increased consistency of output and reduced direct human labor costs and expenses.
Our exhaustive study on the effect of Automation and Artificial Intelligence indicates that the workplace will radically change both in features and in constituents. The future workplace will comprise of both types of workforce – humans and robots – both in proportionate quantum to the nature of work with a viable synergy between themselves to achieve the organizational goals.
Will there be job losses? The answer is yes. Which jobs are most vulnerable? Our study shows that most workers in transport and logistics (such as taxi and delivery drivers) and office support (such as receptionists and security guards) are likely to be substituted by IoT (Internet of Things), and that many workers in sales and services (such as cashiers, counter and rental clerks, telemarketers and accountants) also faced a high risk of getting replaced by Chat Bots. The recent developments in machine learning will put a substantial share of employment, across a wide range of occupations, at risk in the near future.
Table-1 Probability of Job Loss due to Automation
|Word Processors and Typists||0.81|
|Real Estate Sales Agents||0.86|
|Accountants and Auditors||0.96|
There is also a threat of job polarization, where middle-skill jobs (such as those in manufacturing) are declining but both low-skill and high-skill jobs are expanding. The stagnation of median wages in many Western countries is evidence that automation is already having an effect. Figures published by the Federal Reserve Bank of St Louis show that in America, employment in non-routine cognitive and non-routine manual jobs has grown steadily since the 1980s, whereas employment in routine jobs has been broadly flat.
As more jobs are automated, this trend seems likely to continue.
The positive and negative outcomes of Automation and Artificial Intelligence, as inferred from our study are:
1.Reduction in production time
2.Increase in accuracy and repeatability
3.Less human error
4.Less employee costs
6.Higher volume production
7.Reduced Production Cost
8.Decrease in Part Cycle Time
9.Improved Quality and Reliability
10.Better Floor Space Utilization
3.Large initial investment
4.Increase in unemployment
Conclusion and Recommendations
In the long term, robots are cheaper than human labor. However, the initial investment can be costly. It’s also difficult, expensive, and time consuming to program robots to perform multiple tasks, or to reprogram a robot to perform tasks outside its original function. That is why, in labor markets like India and China, human workers have thus far been cheaper than robots.
Former McDonald’s chief executive Ed Rensi recently told the US’s Fox Business that a minimum-wage increase to $15 an hour would make companies consider robot workers. “It’s cheaper to buy a $35,000 robotic arm than it is to hire an employee who is inefficient, making $15 an hour bagging French fries.
Robots will help companies and brands move manufacturing closer to markets. Customer needs and increasing scale will decelerate the global search for cheap labor. With scale the prices of robots come down. A Study done by McKinsey projects that growth in the global installed base of advanced robotics will ccelerate from around 2% to 3% annually today to around 10% annually during the next decade.
While automation has become a resource for remaining competitive in the manufacturing industry, there are definitely some factors to be considered in order to be competitive and to get a return on the investment. Depending on the operations, automation may or may not be a good fit. If it is a small operation with low production quantities, the initial investment of purchasing an automated machine would not be economical. On the other hand, if the operation has a larger facility with many employees on the shop floor two
fabricate medium to large runs, automated machines would be better suited.
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