Man and Machine Work Together: Collaboration increases Productivity

Collaboration between machine and man has been explained here. As automation, cognitive technology and artificial intelligence have attracted much attention, companies may need to re-adjust their division of labor, such as assigning part of the work to manual completion, part of the work to be completed by machines, and of course some are done by humans and machines , Use technology to improve staff efficiency.

At the same time, managing people and machines will give the human resources department new challenges, which involve how to simultaneously manage “enhanced employees” and create new human resources processes to manage virtual employees, cognitive subjects, robots and other artificial intelligence components “Collarless” labor force.

Based on the principle of automation, the original practice methods, systems and talent models are redesigned. The human resources team can begin to transform itself into a flexible, fast-responsive and dynamic department, so as to better serve future talents—no matter it is Machines or humans—provide support.

As the wisdom advances, automation is steadily moving towards the prospect of widespread application, and media reports on this historic disruptive technology have become more alarmist. A business news media recently reported that “the latest research shows that the jobs of millennials will be taken away by artificial intelligence”. Another media said, “The risk of American workers being replaced by robots is increasing.”

These hyped news may get extremely high click-through rates, but they did not consider a more desirable and possible situation: in the near future, humans and machines will achieve seamless collaboration, complement each other, and jointly increase productivity.

As a result, the human resources department will also develop new strategies and tools to recruit, manage, and train a workforce that is a mixture of people and machines.

A new thinking model for uncollared employees
A new thinking model for uncollared employees

Despite the above-mentioned unfounded predictions, it is extremely unlikely that robots, cognitive technologies, and artificial intelligence (“AI”) will replace most human workers. Of course, these technical tools can help automate certain repetitive and lower-level tasks.

But perhaps more importantly, by automating specific parts of a job, intelligent automation solutions can improve human work efficiency, so that human workers have more time to pay attention to the more “human” level.

Namely; those who need to use emotions, social skills and emotional intelligence to deal with all aspects. For example, after retail banking transactions are automated, bank tellers will have more time to communicate with customers, provide customers with advice, and sell products.

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Regarding the impact that automation may have on future jobs, a survey conducted by the 2017 Deloitte Global Human Capital Trends Report interviewed more than 10,000 human resources and business leaders in more than 140 countries. Only 20% of respondents said they would reduce the number of positions within their companies.

The vast majority of interviewees (77%) said that they would train existing personnel to use learned techniques, or redesign positions to make better use of “human” skills. A recent Deloitte UK study showed that 30% of high-income groups in the future will be concentrated in socially related and essentially related areas.

The future predicted by this research has actually arrived. In the next 18 to 24 months, it is expected that more companies will embrace the development trend of uncollared labor by recreating the division of labor and work completion methods in a human-machine hybrid environment.

This requires a brand-new thinking to comprehensively think about work, corporate culture, technology, and the most important resource and talent for the company. Despite the many challenges and challenges mentioned above, the collarless trend is so promising and opportunities that no one can ignore it.

Human-machine collaboration changes the way to implement, interact and experience enterprise solutions

Enterprise applications are one of the areas most affected by human-machine collaboration. Most large enterprises have transformed from standardized, locally deployed huge ERP systems to more rapid and modern cloud-based enterprise applications.

As most companies realize that cloud-based enterprise applications are a core part of their digital transformation process, their pace of transformation is accelerating. But what’s interesting is that as artificial intelligence and robots become mainstream and part of digital transformation tools, they are no longer only used to automate simple repetitive tasks, but also trigger our understanding of how to implement, interact and experience cloud-based enterprise applications Innovation.


Customers no longer want scarce team members to perform repetitive tasks such as configuration and regression testing of enterprise applications. Humans are now training robots to complete these necessary but low-value-added tasks, while humans spend their time on more critical tasks such as designing solutions and improving management.


Users now rely on robots to directly perform certain non-value-added tasks in the system. The closing process at the end of the month is a typical example: Financial analysts spend time reconciling accounts and rely on robots to coordinate specific system activities such as the closing period and running month-end closing reports.


Nowadays, virtual assistant robots enable enterprise applications to operate beyond simple user interfaces such as web pages or mobile applications. Service technicians can spend more time repairing equipment, while virtual assistants can provide them with a seamless and personalized experience in ordering parts and reports/upgrades, or just on the paperwork of the tasks they have completed.

The collarless trend is not limited to the deployment and use of artificial intelligence and robots, but also includes the establishment of a new way of working in a human-machine collaborative cultural environment. Employees who are accustomed to providing standardized responses under strict process constraints will be freed from mechanical “colleagues” because they can not only automate the entire process, but also help improve the efficiency of human employees when performing higher-level tasks.

A key to changing the corporate structure and culture is to gradually improve existing processes and governance structures to meet the challenges posed by the requirements for supporting human-machine collaboration. Some companies that lead in human-machine collaboration have established centralized or federal centers of excellence (CoE). The construction of a center of excellence helps ensure that the most influential and strategic opportunities are seized and utilized, and it also avoids the proliferation of various types of robots in the enterprise. Advanced centralized robot governance framework 

Corporate data sovereignty

Every enterprise regards data as a key asset. However, the need to “let go” for the freedom granted by data is becoming stronger-letting these data information be known, understood and used by business units, departments and regions. This requires the use of modern methods, namely, the use of machine learning, natural language processing, and automation to dynamically understand data relationships, guide data storage and manage data rights, so as to carry out data construction and management. The global data privacy and protection related laws are constantly changing, and the above capabilities are also required.

As the data contained in information and valuable insights related to customers, strategy and operations increase, we have entered a new era of digital enlightenment. In this new era, there is not only an unprecedented amount of data-source channels are wider, but the data is more open.

Over the years, for companies that have embarked on the road of digital enlightenment, they have become more and more aware of the need to fully tap the potential of data and should “Free” data—this does not mean free in the sense of money, but data Can be obtained, and universal. As the traditional boundaries that demarcate enterprise domains gradually disappear, extensive and open use of data becomes increasingly important, which is conducive to analysts using this data to create value.

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Even when the data is public, we still need to sort out the data. In the traditional sense, “clarifying data” means assigning data specification definitions and access levels from top to bottom, and establishing layers of in-depth management protocols. This Dewey decimal classification method is essentially a formal method that attempts to use “brute force” to control confusion.

In the next 18 to 24 months, we expect that more companies will use modern methods for data management in order to strike a balance between data management and data access. As an indispensable part of the development trend of corporate data sovereignty, companies will develop mature technologies to manage and “release” the increasingly important asset value of the company and profit from it.

Data Management Related challenges

These companies can deal with data-related challenges from three aspects: management and architecture, global legal compliance, and data ownership. Most companies face different and continuous challenges in these three areas, such as:

  1. How can companies manage the data carefully and efficiently when they disclose data across agencies and functions?
  2. How do companies automate heavy and repetitive data classification and management tasks?
  3. As a global company, how does an enterprise comply with the huge differences in privacy protection requirements between countries?
  4. In the enterprise, who is ultimately responsible for all data? Chief Information Officer? Chief Operating Officer? Or other personnel?

When a company develops into an insight-driven enterprise, the trend of corporate data sovereignty can help it sort out the above and other issues. There is no doubt that this type of transformation requires long-term investment in data integration, data logging, data security, data lineage, enhanced management, and other aspects. But through these investments, companies can create a dynamic data management structure that is constantly evolving, learning, and expanding.

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