Recent empirical research by the MIT-IBM Watson AI Lab provides new insight into how work is changing in the face of AI. Based on this research, the author provides a roadmap for leaders intent on adapting their workforces and reallocating capital, while also delivering profitability. They argue that the key to unlocking the productivity potential while delivering on business objectives lies in three key strategies: rebalancing resources, investing in workforce reskilling and, on a larger scale, advancing new models of education and lifelong learning.
As AI is increasingly incorporated into our workplaces and daily lives, it is poised to fundamentally upend the way we live and work. Concern over this looming shift is widespread. A recent survey of 5,700 Harvard Business School alumni found that 52% of even this elite group believe the typical company will employ fewer workers three years from now.
The advent of AI poses new and unique challenges for business leaders. They must continue to deliver financial performance, while simultaneously making significant investments in hiring, workforce training, and new technologies that support productivity and growth. These seemingly competing business objectives can make for difficult, often agonizing, leadership decisions.
Against this backdrop, recent empirical research by our team at the MIT-IBM Watson AI Lab provides new insight into how work is changing in the face of AI. By examining these findings, we can create a roadmap for leaders intent on adapting their workforces and reallocating capital, while also delivering profitability.
The stakes are high. AI is an entirely new kind of technology, one that has the ability to anticipate future needs and provide recommendations to its users. For business leaders, that unique capability has the potential to increase employee productivity — by taking on administrative tasks, providing better pricing recommendations to sellers, and streamlining recruitment, to name a few examples.
For business leaders navigating the AI workforce transition, the key to unlocking the productivity potential while delivering on business objectives lies in three key strategies: rebalancing resources, investing in workforce reskilling and, on a larger scale, advancing new models of education and lifelong learning.
Solution #1: Reallocate Capital Resources
Our research report, offers a window into how AI will change workplaces through the rebalancing and restructuring of occupations. Using AI and machine learning techniques, our MIT-IBM Watson AI Lab team analyzed 170 million online job posts between 2010 and 2017. The study’s first implication: While occupations change slowly — over years and even decades — tasks become reorganized at a much faster pace.
Jobs are a collection of tasks. As workers take on jobs in various professions and industries, it is the tasks they perform that create value. With the advancement of technology, some existing tasks will be replaced by AI and machine learning. But our research shows that only 2.5% of jobs include a high proportion of tasks suitable for machine learning. These include positions like usher, lobby attendant, and ticket taker, where the main tasks involve verifying credentials and allowing only authorized people to enter a restricted space.
Most tasks will still be best performed by humans — whether craft workers like plumbers, electricians and carpenters, or those who do design or analysis requiring industry knowledge. And new tasks will emerge that require workers to exercise new skills.
As this shift occurs, business leaders will need to reallocate capital accordingly. Broad adoption of AI may require additional research and development spending. Training and reskilling employees will very likely require temporarily removing workers from revenue-generating activities.
More broadly, salaries and other forms of employee compensation will need to reflect the shifting value of tasks all along the organization chart. Our research shows that as technology reduces the cost of some tasks because they can be done in part by AI, the value workers bring to the remaining tasks increases. Those tasks tend to require grounding in intellectual skill and insight—something AI isn’t as good at as people.
In high-wage business and finance occupations, for example, compensation for tasks requiring industry knowledge increased by more than $6,000, on average, between 2010 and 2017. By contrast, average compensation for manufacturing and production tasks fell by more than $5,000 during that period. As AI continues to reshape the workplace, business leaders who are mindful of this shifting calculus will come out ahead.
Solution # 2: Invest in Workforce Training
Companies today are held accountable not only for delivering shareholder value, but for positively impacting stakeholders such as customers, suppliers, communities and employees. Moreover, investment in talent and other stakeholders is increasingly considered essential to delivering long-term financial results. These new expectations are reflected in the Business Roundtable’s recently revised statement on corporate governance, which underscores corporations’ obligation to support employees through training and education “that help develop new skills for a rapidly changing world.”
Millions of workers will need to be retrained or reskilled as a result of AI over the next three years, according to a recent IBM Institute for Business Value study. Technical training will certainly be a necessary component. As tasks requiring intellectual skill, insight and other uniquely human attributes rise in value, executives and managers will also need to focus on preparing workers for the future by fostering and growing “people skills” such as judgement, creativity and the ability to communicate effectively. Through such efforts, leaders can help their employees make the shift to partnering with intelligent machines as tasks transform and change in value.
Solution #3: Educate for the Future Today
As AI continues to scale within businesses and across industries, it is incumbent upon innovators and business leaders to understand not only the business process implications, but also the societal impact. Beyond the need for investment in reskilling within organizations today, executives should work alongside policymakers and other public and private stakeholders to provide support for education and job training, encouraging investment in training and reskilling programs for all workers.
Our research shows that technology can disproportionately impact the demand and earning potential for mid-wage workers, causing a squeeze on the middle class. For every five tasks that shifted out of mid-wage jobs, we found, four tasks moved to low-wage jobs and one moved to a high-wage job. As a result, wages are rising faster in the low- and high-wage tiers than in the mid-wage tier.
New models of education and pathways to continuous learning can help address the growing skills gap, providing members of the middle class, as well as students and a broad array of mid-career professionals, with opportunities to build in-demand skills. Investment in all forms of education is key: community college, online learning, apprenticeships, or programs like P-TECH, a public-private partnership designed to prepare high school students for “new collar” technical jobs like cloud computing and cybersecurity.
Whether it is workers who are asked to transform their skills and ways of working, or leaders who must rethink everything from resource allocation to workforce training, fundamental economic shifts are never easy. But if AI is to fulfill its promise of improving our work lives and raising living standards, senior leaders must be ready to embrace the challenges ahead.
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