One prevalent description of Artificial Intelligence (AI), especially among scholars, is that it is an amplified form of automation. However, it is essential to address an inconsistency in nomenclature here. When people mention AI, they often refer to a particular category known as Artificial Neural Networks (ANNs). It is important to note this distinction even though the objective in this article is not to delve into the clarification of terminology. The term AI in this article refers specifically to the prevailing paradigm, ANNs.

Describing AI merely as ‘automation on steroids’ not only provides an incomplete perspective but also tends to underestimate its genuine potential. When technologies with exponential potential, such as AI, electricity, or steam power emerge, they inevitably revolutionize how work is conducted. However, nuanced differences exist in how these technologies transform work processes. The critical imperative for managers and leaders during such transformative periods is to gain a profound understanding of the new capabilities that these technologies offer to guide their leadership efforts towards shaping the future of work.

Any substantial transformation within a company or industry irreversibly alters how work is performed within that domain. It propels the nature of work towards a future state that is markedly distinct and discontinuous from all past changes, including incremental ones, that have occurred in the industry or company.

AI can impact work in various ways, with or without automation. In envisioning the future of work, we define the impact of AI in three key categories, as depicted in Figure 1. The first category, ‘Elimination of Tasks’, involves the removal of certain tasks from human roles through automation. The second category, ‘Elevation of Expertise’, focuses on how AI can augment human capabilities, including physical strength and cognitive abilities, thus elevating expertise in various fields. The third category, ‘Exploration of New Work’, is centered on the emergence of new kinds of work, delving into previously uncharted areas of employment. This framework allows us to track the historical evolution of work and project future trends. It also provides a foundational structure for business leaders to understand and influence the AI-driven future of work.

As we navigate the unfolding landscape of the future of work, primarily driven by the advent of AI and still in its early stages, it can be instructive to draw parallels with the historical evolution of work shaped by past exponential technological innovations. Scholars often draw a parallel between AI and the transformative impact of electricity, making it a suitable point of reference for our exploration. Let us consider how electricity reshaped the world of work in the past.

Before the advent of electricity, machines harnessed natural energy sources like wind and water for their operations. For example, windmills were used to grind grain and flowing water powered spindles for various industrial processes. The introduction of electricity gradually revolutionized these tasks by eliminating the reliance on natural energy systems, leading to the loss of certain jobs, facilitating factory relocations, and giving rise to new transportation-related employment opportunities. Furthermore, the use of powerful electrical machinery increased productivity, significantly enhancing human capabilities compared to their natural energy powered counterparts. These advancements, although not immediately apparent, played a pivotal role in shaping innovations like the Ford Assembly line for mass production over a span of several decades. Managers overseeing factories before the electricity era could not have foreseen outcomes such as mass production and the associated job shifts.

Similarly, predicting the exact contours of the future of AI-driven work remains challenging, but proactive leaders and managers can incrementally transform work across the three categories and actively shape its future. To illustrate this, let us examine the impact of AI on road transportation (trucking industry).

Autonomous vehicles (AVs) in trucking offer benefits like enhanced safety, cost savings, 24/7 operations, addressing driver shortages, environmental advantages, predictive maintenance, supply chain optimization, last-mile solutions, and valuable data analytics. Several significant changes emerge when examining the trucking industry’s future through the lens of work transformation, offering opportunities for visionary leaders to anticipate and harness positive outcomes.

Task Elimination and Expertise Enhancement: The possibility of full automation in the trucking industry raises the prospect of traditional truck driver roles being phased out. Tasks that were once performed by human drivers, such as steering and braking, can be automated. While this shift may understandably raise concerns about job displacement, it is important to recognize that automation does not merely entail eliminating tasks.

Elevation of Expertise through AI Augmentation: Even before achieving full-scale automation, AI-based technologies are poised to elevate the expertise of truck drivers. For instance, AI systems can detect driver fatigue and enhance safety. This AI augmentation empowers both experienced and less-experienced drivers to perform their jobs more competently. It allows drivers to continually enhance their skills, improving safety standards and overall competence within the industry.

Exploration of New Work Frontiers: The introduction of AVs opens up new and exciting work frontiers. While some tasks, like maintaining and troubleshooting AVs, will remain essential, shifting towards AVs will also give rise to entirely new roles. Consider the emergence of fleet pilots who manage multiple trucks remotely from centralised control centres. This represents a significant exploration of new frontiers in work within the trucking industry.

When we analyze the potential adoption of AVs in the trucking industry in the context of the S-curve and compare it to the inverse S-curve representing the rate of job loss, a simple yet profound insight emerges. It highlights the strategic flexibility of early adopters in coping with job displacement by leveraging all three aspects of the future of work.

AV Adoption (S-curve): The adoption of AVs in the trucking industry follows an S-curve pattern (Figure 2). Initially, adoption is slow as the technology is introduced and refined. As it proves its value in terms of efficiency, safety, and cost-effectiveness, adoption accelerates, eventually reaching a saturation point where most companies have integrated AVs into their fleets.

Job Loss (Inverse S-curve): Conversely, the rate of job loss among truck drivers exhibits an inverse S-curve (Figure 2). At the outset of AV adoption, job displacement is minimal as AV technology is in its early stages. However, as AVs become more widespread and capable, job losses will begin to accelerate. This phase can be challenging for incumbent truck drivers.

Strategic Flexibility: Early adopters of AV technology, such as forward-thinking trucking companies, have a significant advantage in terms of strategic flexibility. They can proactively address job displacement by leveraging all three aspects of the future of work:

a) Task Elimination: They can prepare for the elimination of certain driving tasks by retraining drivers for new roles within the organization, such as AV monitoring or fleet management.

b) Elevation of Expertise: By investing in AI augmentation and upskilling programs, they can enhance the expertise of their workforce, enabling drivers to work alongside AVs more effectively and safely.

c) Exploration of New Frontiers: Progressive companies can explore new frontiers of work created by AVs, such as remote fleet management roles or specialized AV maintenance positions. This diversification of roles can mitigate the impact of job loss.

By strategically managing the intersection of AV adoption and job displacement through these three aspects, early adopters can navigate the challenges posed by technology adoption while creating a more adaptive and resilient workforce. This insight underscores the importance of proactive planning and flexibility in shaping the future of work within any industry experiencing technological transformation.

The preceding conversation primarily focused on firm-level opportunities. However, when we examine this from a value chain standpoint, pioneering trucking companies have the potential to benefit from the adoption of AVs by tapping into emerging economic opportunities across the value chain. These opportunities are primarily centred on the establishment of the following novel components of value creation.

High-end Maintenance Garages: Pioneering companies can establish advanced AV maintenance facilities, which include servicing and repairing the intricate components of autonomous trucks, ensuring their optimal performance.

Training of Mechanics: To support the growing AV fleet, there is a demand for skilled mechanics trained in AV technology. Pioneers can take the lead in providing training programs in collaboration with original equipment manufacturers, creating a skilled workforce, and positioning themselves as experts in AV maintenance.

Service Facilities: Pioneers maintain AVs for their own use and offer their services to other companies adopting AVs in the future. They become service providers in the evolving AV ecosystem, expanding their revenue streams.

This approach exemplifies how pioneers can set the standards and seize a large portion of the expanding future value pie, rather than merely defending their position in the old paradigm. By actively participating in the industry’s transformation, they become architects of the future, shaping the rules and benefiting from the economic opportunities that arise.

This approach does not ignore the risks and costs associated with early adoption and pioneering efforts. The objective here is to underscore that when grappling with exponential technologies like AI, adhering to a linear mindset and conventional risk management strategies may not always be the most prudent approach. Particularly, pioneers can shape the future to their advantage when technology has passed the inflection point and is generating substantial economic value across industries. In contrast, early and late followers, or those who lag behind, find themselves in a different paradigm where the pioneering competitors either establish or dictate the rules.