The essence of work is changed by artificial intelligence (AI). AI will intensify the change we have seen over the past 15 years in the required workforce skills. It has found that the most significant increase in demand for technical skills is today the smallest group, which is 55% higher and 17% more worked hours by 2030 than 11% in 2016.

As well as specialized technical skills such as programming, this surge will impact primary digital skills demand. Demand for emotional and social skills such as leadership and others’ management would increase by 24% to 22% of working hours. Demand for higher cognitive abilities will increase moderately overall, but for some of these abilities, especially creativity, will increase dramatically.

Some types of expertise would be less in demand. Basic cognitive abilities, including the input and retrieval of basic data, would decrease by 15 per cent, dropping from 18% to 14 % of working hours.

Demand for physical and manual skills, including general machinery operation, will also decline by 14% but will remain the largest class of workforce skills in many countries in 2030, accounting for 25% of total hours employed. Across markets, capability transitions will play out differently. For example, healthcare will see a growing need for physical skills, even as the demand for manufacturing and sectors declines.

To remain to compete, Tonisations would need to make crucial operational adjustments while addressing these capability shifts. A study of more than 3,000 business leaders in seven countries highlights a new emphasis on continuous employee learning and a move towards more cross-functional and team-based work.

Jobs will need to be redefined as roles shift and businesses say they will need to become more agile. Independent work will probably require human resources to adapt: almost 20% of businesses say there is inadequate management team expertise to drive automation and artificial intelligence.

Nearly one in three businesses are worried that their potential financial success will be hurt by lacking the expertise they need for automation adoption. High-skill workers’ competitiveness will increase, while displacement will focus largely on low-skill workers, continuing a pattern that has increased income inequality and decreased medium-skilled jobs. Companies affirmed that they are more likely to recruit and retain high-skilled employees and to see rising salaries.

AI and automation

Companies at the forefront of automation adoption hope to draw the talent they need, but slower adopters believe they may be more constrained in their choices. Nearly half of the businesses we surveyed claim they plan to take the lead in developing the future workforce. Still, all stakeholders will need to work together to navigate the future of large-scale retraining and other transformation challenges.

To reshape school and college curricula, businesses should partner with educators. Talent pipelines can be created by business organisations, while trade unions can assist with cross-sector mobility.

In the new century, protections for workers in transition will need to be improved and mobility facilitated, including moving to portable benefits, as ways of working and the workplace itself are changed.

How will demand workforce skills change with automation? 

The introduction of automation and artificial intelligence technology will change the workplace over the next 10 to 15 years, as individuals gradually communicate with ever-smarter machines. In the form of higher economic growth, better business efficiency, and new prosperity, these innovations and the interaction between humans and machines would bring multiple benefits. At a time when the working-age population is shrinking in many nations, automation can replace ageing jobs. It would also help solve social issues. 

Machines already operated by AI are more adaptive at diagnosing certain X-ray and MRI diseases than expert physicians. 

Productivity growth, which has declined in advanced countries, could offer a boost. Over the past decade, they have created significant value for businesses across industries, from agriculture and media to healthcare and pharmaceuticals. Companies use these innovations, among other applications, to perform predictive maintenance in development, configure “next product to buy” recommendations, optimise real-time pricing, and detect fraudulent transactions.

Such technology would also alter the skills required by human workers. Ability changes in the workforce are not new; in fact, skill requirements have changed since the first Industrial Revolution reconfigured machines and workers in many countries complain that they have difficulty finding the talent they need.

Employees also complain that they are underqualified or even overqualified for their employment. Increased labour costs, lost production due to unfilled positions, slower implementation of new technology and higher unemployment rates will result in implied and explicit costs.

AUTOMATION is comparable to the historical tendency to accelerate capability changes.

In terms of technical skills, the greatest transition will occur, both in specialised skills such as programming, advanced data processing and application design, and more fundamental digital skills related to the growing proliferation of digital technologies in all workplaces. Other abilities, including different social and emotional abilities, would also see a major rise in demand. From basic to higher cognitive abilities, a transition will take place. As a prevailing skill set, demand for physical and manual skills will decline, although these skills will remain a major component of the future workplace.

How can humans and machines work together for greater efficiency and productivity?

Medical diagnosis is one case. A diagnosis is a judgement of how a patient’s knowledge falls into a disease-characteristic pattern. This is something well done by computers. Machines trained with the digital records and results of millions of previous patients, along with suggestions for treatment and even more tests, may create a diagnosis for a sick patient. Machines will take even more data into account and keep up faster than any doctor with the latest study. The doctor’s primary function will be to explain the outcome to the patient and assist the patient in understanding it and embracing it so that the patient goes through with the treatment plan.

Research demonstrates that the best outcomes in many areas will come from people who are assisted by intelligent devices, a mixture of a doctor and a computer, a teacher and a machine, etc. Machines will continue to do the analytical work they do well in the workplace shortly while leaving other things to see the big picture and have interpersonal skills.

Ten things it solves

Companies and governments should take advantage of automation and AI to benefit from increased contributions to efficiency and productivity and social benefits. Such innovations will produce economic surpluses that will help communities navigate changes in the workforce. The emphasis should instead be on ways to ensure that the workforce changes are as smooth as possible. In many main areas, this is likely to require more actionable and scalable solutions:

  • Ensuring robust economic growth and productivity. For all the challenges presented by automation, strong growth is not the magic solution. But for job growth and growing stability, it is a pre-requisite. A major contributor to economic growth is productivity growth. The unlocking of investment and demand, thus, and it is important to accept automation for its contribution to productivity.
  • Fostering dynamism for the industry. Entrepreneurship and faster growth of new companies would increase not only efficiency but also drive job creation. As well as a favourable environment for large businesses, a thriving environment for small businesses promotes business dynamism and, with it, job creation. More straightforward and more advanced legislation, taxes, and other incentives would be needed to increase the pace of new business creation and companies’ growth and productivity, large and small.
  • For a changed workplace, emerging education systems and learning. Policymakers partnering with education providers (traditional and non-traditional) and employers themselves should do more across school systems to improve basic STEM skills and improve on-the-job preparation. Creativity, analytical and systematic thought, and adaptive and life-long learning require a new focus. There would have to be solutions on a wide scale.
  • Investing in money for humans. Reversing the low trend and declining public investment in worker training in some countries is important. Policymakers may encourage businesses to invest in human resources, including job development, learning and capacity building, and wage growth, through tax benefits and other incentives, similar to incentives for the private sector to invest in other forms of capital, including R&D.
  • Improving dynamism in the labour market. Data signals that allow employee matching to work, credentialing, could all work better in most economies. Online channels can also help to connect individuals with jobs and restore labour market vitality. When more employees change jobs, even within a company, evidence indicates that salaries As more types of jobs and income-earning opportunities emerge, like the gig economy, we will need to address problems such as benefit portability, classification of workers, and fluctuations in wages.
  • Job Redesigning. The design of workflows and the design of workspaces would have to adjust to a modern age in which people work with computers more closely. In terms of maintaining a healthy and sustainable climate, this is both an opportunity and a challenge. As work becomes more collaborative, and businesses aim to become more flexible and non-hierarchical, organisations are also evolving.
  • Rethinking profits. Suppose automation (full or partial) results in a substantial reduction in jobs and higher wage pressure. In that case, it is possible to consider and test such proposals, such as conditional transfers, mobility assistance, universal basic income, and adapted social safety nets. The key will be to find economically feasible solutions that integrate the multiple roles that function for employees, including payment and sense, intent, purpose, and dignity.
  • Reconsideration of relocation assistance and safety nets for affected staff. As work progresses at higher rates of change between industries, places, operations, and specifications for skills, many employees will need to adapt. There is a range of best-practice approaches to transitional safety nets, which should be implemented and adapted, while new perspectives should be considered and evaluated.
  • Investing in the drivers of job demand. Governments would need to consider rising investments that are advantageous in their own right and lead to job demand (e.g. infrastructure, climate change adaptation). From construction to renovation of buildings and solar panels’ installation, such employment types are mostly middle-wage jobs, the ones most affected by automation.
  • Safely embracing AI and automation. Even as the efficiency advantages of these rapidly emerging technologies are captured, we need to defend against threats and minimise any risks constantly. Data use must always consider data protection, privacy, malicious use, possible bias issues, governments, technology, and other businesses. People would need to find successful ways to tackle.

How can society best workers and prepare them for this new future?

The challenge is to encourage transfers from new skills to new occupations. 

Furthermore, even as conventional jobs vanish, new jobs will be created. Over the 200 years after the Luddite revolt, a movement led by workers in England in the 19th century who protested the implementation of weaving technology, productivity advances through technology development have led to new industries and new employment over time. It might continue, or it could be different this time. 

Moreover, according to the Department of Labor, there are more than 6 million unfilled work vacancies today. Employers cannot find suitable applicants for many well-paying positions, meaning that there are potential openings for adequately skilled unemployed workers. These new positions will not generally be in nearby areas, nor, at least initially, are they likely to pay as well. New positions need new expertise. Others propose a guaranteed basic income. My feeling is that there is no lack of work to be done.

Money is not the only thing; it’s also important to have a sense of self-worth and status in society. Instead of paying people not to work, it is easier to help the transition to new jobs. The current adjustment assistance programme did nothing to offset the effects of globalization-related job losses; it should be extended to include income and training and relocation assistance for layoffs due to both automation and international competition. 

Finally, the quick speed of transition confirms the advantage of a life-long schooling habit. Community colleges and internet courses offer low-cost education and training on a wide range of subjects.

Today, there is work for everyone and tomorrow there will be work for everyone, even with automation. Nevertheless, the job will be different, demanding new skills and greater workforce adaptability than we have seen. It will be imperative to prepare and retrain both mid-career staff and new generations for the coming challenges. To better align public and private programmes, governments, private sector leaders and innovators all need to work together, including providing the right conditions to invest more in human capital. If we use the technology with aplomb and minimise the negative, the future of automation and AI can be complicated, but a far richer one.

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About the Author


Hi. My name is Chahat. I am an architecture student by profession and a writer by heart. It wouldn’t be wrong to call myself hard liquor mixed with a little bit of intellect. I welcome all my readers to my world via a screen. Thank you.

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