With robots potentially as not only your coworkers but also your competition, what capabilities and unique talents are essential to keep your job?
We asked the co-chairs of IAOP’s Global Human Capital Chapter: What skills do humans need to compete with robots?
A recent KPMG white paper titled Rise of the Humans states that automation and robotics will transform jobs according to two main dimensions – Cognitive Automation and Cognitive Processing & Robotic Automation.
The authors said Cognitive Automation changes fall into two main areas: the Leveraged Professional, which enables the people of lesser qualifications to perform at substantially higher levels, e.g., a paralegal giving attorney-level advice, or allowing a lower qualified professional deliver a world-class output. Second is the Connected Worker, which affords everyone in a specific role to access technologies and the best ideas and knowledge on a topic. An example would be surgeons learning the latest techniques from the world leader in a certain surgical procedure.
Cognitive Processing & Robotic Automation also has two areas: Working at the Speed of Thought says that augmented professionals can work faster, more efficiently with much greater throughput and effectiveness, such as delivering better company reporting and analytical judgment making. The Digital Worker describes the use of technologies to replace entire roles and job types. In particular, jobs in front-line and middle-ranking occupations are likely to see the biggest impact. Digital labor can fully replace or work alongside humans – in a call center, for example.
So it begs the question, what are the skills and talents one needs in the future to be a valued and irreplaceable resource? Like all technology, there are limitations. Jobs which require things like empathy, compassion, or those activities where Emotional Intelligence is needed are expected to remain relevant. There have been numerous discussions and research studies performed debating which is better to have, Intellectual Quotient (IQ) versus Emotional Quotient (EQ).
Robotics is not able to replace emotionally driven activities that are involved in some industries and jobs – such as strategists, performance managers, motivators, coaches, teachers, mentors, etc. As a CEO or manager, a computer can help inform you which decisions to make based on the data, but not how to personally connect and motivate employees to entice and inspire action. Those activities and roles will always be there and we should develop our strategic thinking and look at was to strengthen our EQ. Another human characteristic not easily replicated by machines is creativity or ingenuity.
Professions such as musicians, artists benefit from creativity, while many companies and organizations look to a Chief Strategy Officer or strategic arms of their business to develop new innovative ways of doing things to help grow and meet organization’s objectives. There is a movement among millennials to challenge the “status quo” and their ambition in these areas will serve them well in the future.
Lastly, if we think about leveraging the robots themselves as they join the workforce, the technical skills associated with robotics and automation will become increasingly important. Both making these technologies work, and also how to get them to work together with humans will be crucial. There will be a constant need to configure, deploy, redesign and redeploy these in each organization. The ramifications of robotics and automation are significant, but it is nothing we should fear.
While some jobs will be lost, many others will be created. The more quickly we come to understand how these machines will work and how to leverage them to improve each of our individual jobs and realize what tasks we can perform that are unique to humans, the better we are at becoming irreplaceable.
At Accenture, we believe that automation represents an entirely new factor of production that enables people to make more efficient use of their time and do what humans do best – create, imagine and innovate new things. Machines offer strengths and capabilities that are different from – but crucially complementary to – human skills.
Beyond simply eliminating repetitive tasks, automation is supporting humans in complex and creative problem-solving by enabling analysis of dauntingly vast amounts of data and the identification of trends previously impossible to detect. As such, it’s improving the pace and scale of risk analysis and business decision-making at an enterprise level, while enabling employees to achieve significant productivity gains – as much as 30 percent to 40 percent – even in functions that are already automated.
Automation, like any other emerging technology, will change an organization’s demand for specific skills, but it won’t reduce the demand for skilled people.
In the Accenture Technology Vision 2017 companion survey of more than 5400 business and IT executives, 85 percent of executives report they will invest extensively in artificial technology-related technologies over the next three years. So there’s no doubt that as automation and AI adoption continue to rapidly accelerate, so too will the demand for skilled professionals.
Specific skill sets are needed around implementing robotics technology, integrating it with existing systems, and managing change management aspects in order to maximize its business impact. As more advanced capabilities are implemented around the broader constellation of technologies that make up artificial intelligence, skills will also be needed in the areas of computer vision, speech recognition, natural language processing, knowledge representation and reasoning, virtual/augmented reality, machine learning, deep learning, expert systems, biometrics and video analytics in general.
Judgment-based skills and general business acumen to best apply the technology in a broader business context are also needed. Intelligent automation and artificial intelligence feed off data; in fact, they can’t exist without it, so content and data curators, data scientists and analytics experts are also crucial in order for algorithms to learn.