Creating Value with Artificial Intelligence

Embracing and integrating Artificial Intelligence (AI) is a challenge for any organization, but for large companies the culture change required to implement AI is often daunting. What some organizations may view as an essential means of remaining competitive, streamlining production processes, and cutting costs, others may view as part of a larger organizational transformation process, meant to reinvent a company. Some, of course, view it as both. There are as many potential permutations associated with embracing AI in the manufacturing process as there are potential applications for doing so.

As the AI revolution gathers real steam in the next decade, business leaders will be forced to examine a series of questions about sources of growth, innovation, competitiveness, skills, jobs, and larger questions around governance and sustainability. Speed, agility, adaptation, and adoption are the ultimate defining factors in successfully transforming businesses from the analog to the digital era. If companies cannot develop at a pace that allows them to effectively compete, they will fall further behind very quickly.

Long-lasting transformation in this context requires an immediate, intense focus on understanding existing and emerging technologies, and how they can create value within a business, while developing the necessary culture and skills to execute the transformation. Just because a job can be automated does not mean that it will be, for relative costs can matter a great deal. Japanese car manufacturers rely heavily on robots while Indian textile manufacturers rely heavily on cheap labor. Even though machine capabilities are rapidly improving, seeking ever cheaper supplies of increasingly skilled labor still makes sense for the majority of businesses. Although the share of US employment in manufacturing has declined sharply since the 1950s (from almost 30% then to less than 10% today), American jobs in services have soared, from less than 50% of employment then to about 70% today.

Evidence is mounting that rapid technological progress, which accounted for the long era of rapid productivity growth from the 19th century to the 1970s, has returned. The exponential growth in chip processing speed, memory capacity, and other computer metrics is so great that the amount of progress computers will make in the next few years will surpass the progress they have made since the very beginning of the computer age. The primary innovation bottleneck is, ultimately, the time it takes society to adjust to the many combinations and permutations of new technologies and business models. At the turn of this century, technologically minded economists pointed to driving cars in traffic as the type of human accomplishment that computers were highly unlikely to master. Now, of course, autonomous vehicles are rolling off the assembly lines and being tested.

The productivity gains from future automation will be real, even if they mostly accrue to the owners of the machines. Some will be spent on goods and services, but most of the rest will be invested in firms that are seeking to expand, and presumably hire, more labor. Though inequality could soar even further in such a world, unemployment will not necessarily spike. The current doldrum in real wages may, as was the case in the early industrial era, prove to be a temporary issue, and be supplanted by higher wages for all.

The jobs of the future may look distinctly different from those they replace. Just as past mechanization freed - or forced - workers into jobs requiring more cognitive dexterity, leaps in AI and Machine Learning could create space for people to specialize in more emotive occupations, as yet unsuited to machines: a world of artists, therapists, and yoga instructors. Such work could prove to be as critical to the future as metal work was in the past. Cultural norms change slowly. Manufacturing jobs are still often treated as “better” to some than paper-pushing is to others. To some 18th-century observers, working in the fields was inherently more noble than manufacturing just about anything, and yet, the tides became completely reversed with the passage of time.

Technological progress squeezes some incomes in the short-term before making more people wealthier in the long-term, and can drive up the costs of some things even more than it eventually increases earnings. Yet, as innovation continues, automation may bring down costs in some of those areas as well. That said, the gains of the 19th and 20th centuries will be hard to duplicate. Boosting the skills and earning power of the children of 19th-century farmers and laborers took little more than providing schools where they could learn to read and write.

The transition from the analog to the digital world, from already educated lesser skilled workers to highly skilled workers, and from conventional manufacturing to smart manufacturing, is unlikely to be simple or painless. Modern society may find itself sorely tested if, as seems possible, growth and innovation deliver handsome gains to the skilled, while the rest cling to dwindling employment opportunities at stagnant wages. Herein lies the core challenge for business leaders and policy makers alike – to find the right mix of carrots and sticks to encourage those who may not otherwise understand or be inclined to take advantage of the opportunity that is staring them in the face, to do so. Some will indeed have a choice; others will be pushed into the fire.

In that respect, the coming AI revolution in manufacturing will imply deploying some of the same basis management skills and orientations toward crafting high rates of employee utilization and satisfaction that have always been a requirement of profitable, thriving businesses, effective managers, and happy employees. AI will not replace the basics of business, but, rather, will require that organizations become even better at perfecting efficiency, streamlining processes, and managing all aspects of the organization well. Perhaps we should be thinking about AI not as a threat to business, but rather as a catalyst propelling businesses to enhance what they do well, even better. 

*Daniel Wagner is CEO of Country Risk Solutions and co-author of the new book AI Supremacy.