Human + Machine Collaboration: Work in the Age of AI – Interesting Engineering

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.

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.

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.

Re-skilling is now paramount and applies to everyone who wishes to remain relevant.Paul Daugherty recommends enterprises to help existing employees develop what he calls fusion skills.

In their book Human + Machine: Reimagining Work in the Age of AI, a must-read for business leaders looking for a practical guide on adopting AI into their organization, Paul Daugherty and H. James Wilson identify eight fusion skills for the workplace:

Rehumanizing time: People will have more time to dedicate toward more human activities, such as increasing interpersonal interactions and creativity.

Responsible normalizing: It is time to normalize the purpose and perception of human and machine interaction as it relates to individuals, businesses, and society as a whole.

Judgment integration: A machine may be uncertain about something or lack the necessary business or ethical context to make decisions. In such case, humans must be prepared to sense where, how, and when to step in and provide input.

Intelligent interrogation: Humans simply cant probe massively complex systems or predict interactions between complex layers of data on their own. It is imperative to have the ability to ask machines the right smart questions across multiple levels.

Bot-based empowerment: A variety of bots are available to help people be more productive and become better at their jobs. Using the power of AI agents can extend human's capabilities, reinvent business processes, and even boost a human's professional career.

Holistic (physical and mental) melding: In the age of human and machine fusion, holistic melding will become increasingly important. The full reimagination of business processes only becomes possible when humans create working mental models of how machines work and learn, and when machines capture user-behavior data to update their interactions.

Reciprocal apprenticing: In the past, technological education has gone in one direction: People have learned how to use machines. But with AI, machines are learning from humans, and humans, in turn, learn again from machines. In the future, humans will perform tasks alongside AI agents to learn new skills, and will receive on-the-job training to work well within AI-enhanced processes.

Relentless reimagining: This hybrid skill is the ability to reimagine how things currently areand to keep reimagining how AI can transform and improve work, organizational processes, business models, and even entire industries.

In Human + Machine, the authors propose a continuous circle of learning, an exchange of knowledge between humans and machines. Humans can work better and more efficiently with the help of AI. According to the authors, in the long term, companies will start rethinking their business processes, and as they do they will cover the needs for new humans in the new ways of doing business.

They believe that "before we rewrite the business processes, job descriptions, and business models, we need to answer these questions: What tasks do humans do best? And, what do machines do best?" The transfer of jobs is not simply one way. In many cases, AI is freeing up to creativity and human capital, letting people work more like humans and less like robots.

Giving these paramount questions and the concepts proposed by Daugherty and Wilson, giving them some thought might be crucial at the time of deciding what is the best strategy you should take as a business leader in your organization in order to change and adapt in the age of AI.

The authors highlight how embracing the new rules of AI can be beneficial at the time businesses are reimagining processes with a focus on an exchange of knowledge between humans and machines.