Human and Machine collaboration reimagines processes with AI, letting humans work more like humans and less like robots.
In this age of Artificial Intelligence (AI), we are witnessing a transformation in the way we live, work, and do business. From robots that share our environment and smart homes to supply chains that think and act in real-time, forward-thinking companies are using AI to innovate and expand their business more rapidly than ever.
Indeed, this is a time of change and change happens fast. Those able to understand that the future includes living, working, co-existing, and collaborating with AI are set to succeed in the coming years. On the other hand, those who neglect the fact that business transformation in the digital age depends on human and machine collaboration will inevitably be left behind.
Humans and machines can complement each other resulting in increasing productivity. This collaboration could increase revenue by 38 percent by 2022, according to Accenture Research. At least 61 percent of business leaders agree that the intersection of human and machine collaboration is going to help them achieve their strategic priorities faster and more efficiently.
Human and machine collaboration is paramount for organizations. Having the right mindset for AI means being at ease with the concept of human+machine, leaving the mindset of human Vs. machine behind. Thanks to AI, factories are now requiring a little more humanity; and AI is boosting the value of engineers and manufacturers.
Business transformation in the era of AI
The emergence of AI is creating brand new roles and opportunities for humans up and down the value chain. From workers in the assembly line and maintenance specialists to robot engineers and operations managers, AI is regenerating the concept and meaning of work in an industrial setting.
According to Accenture‘s Paul Daugherty, Chief Technology and Innovation Officer, and H. James Wilson, Managing Director of Information Technology and Business Research, AI is transforming business processes in five ways:
Flexibility: A change from rigid manufacturing processes with automation done in the past by dumb robots to smart individualized production following real-time customer choices brings flexibility to businesses. This is particularly visible in the automotive manufacturing industry where customers can customize their vehicle at the dealership. They can choose everything from dashboard components to the seat leather –or vegan leather– to tire valve caps. For instance, at Stuttgart’s Mercedes-Benz assembly line there are no two vehicles that are the same.
Speed: Speed is super important in many industries, including finance. The detection of credit card fraud on the spot can guarantee a card holder that a transaction will not be approved if fraud was involved, saving time and headaches if this is detected too late. According to Daugherty and Wilson, HSBC Holdings developed an AI-based solution that uses improved speed and accuracy in fraud detection. The solution can monitor millions of transactions on a daily basis seeking subtle pattern that can possibly signal fraud. This type of solution is great for financial institutions. Yet, they need the human collaboration to be continually updated. Without the updates required, soon the algorithms would become useless for combating fraud. Data analysts and financial fraud experts must keep an eye on the software at all times to assure the AI solution is at least one step ahead of criminals.
Scale: In order to accelerate its recruiting evaluation to improve diversity, Unilever adopted an AI-based hiring system that assesses candidate’s body language and personality traits. Using this solution, Unilever was able to broaden its recruiting scale; job applicants doubled to 30,000, and the average time for arriving to a hiring decision decreased to four weeks. The process used to take up to four months before the adoption of the AI system.
Decision Making: There is no secret to the fact that the best decision that people make are based on specific, tailored information received in vast amounts. Using machine learning and AI a huge amount of data can be quickly available at the fingertips of workers on the factory floor, or to service technicians solving problems out in the field. All data previously collected and analyzed brings invaluable information that helps humans solve problems much faster or even prevent such problems before they happen. Take the case of GE and its Predix application. The solution uses machine-learning algorithms to predict when a specific part in a specific machine might fail. Predix alerts workers to potential problems before they become serious. In many cases, GE could save millions of dollars thanks to this technology collaborating with fast human action.
Personalization: AI makes possible individual tailoring, on-demand brand experiences at great scale. Music streaming service Pandora, for instance, applies AI algorithms to generate personalized playlists based on preferences in songs, artists, and genres. AI can use data to personalize anything and everything delivering a more enjoyable user experience. AI brings marketing to a new level.
AI will create new roles and opportunities
Of course, some roles will come to an end as it has happened in the history of humanity every time there has been a technological revolution. However, the changes toward human and machine collaboration require the creation of new roles and the recruiting of new talent; it is not just a matter of implementing AI technology. We also need to remember that there is no evolution without change.
Robotics and AI will replace some jobs liberating humans for other kinds of tasks, many that do not yet exist as many of today’s positions and jobs did not exist a few decades ago. Since 2000, the United States has lost five million manufacturing jobs. However, Daugherty and Wilson think that things are not as clear cut as they might seem.
In the United States alone, there are going to be needed around 3.4 million more job openings covered in the manufacturing sector. One reason for this is the need to cover the Baby Boomers’ retirement plans.