Motivating a Workforce to Embrace AI

Decades ago, a leader at a company where I worked as a computer programmer tapped me on the shoulder one day and said, "We’ve noticed you like to talk to the humans; would you consider a project leader role?" A bit daunted but sensing an opportunity, I unwittingly said, "Sure." That moment marked my entry into a world of organizing tasks, building trust, and communicating in ways that would, above all, keep team members engaged and intrinsically motivated—all wholly foreign concepts to me at the time (and still fuzzy now).

Decidedly not a born leader, though fascinated by those who are, I stumbled and fumbled my way along, relying on instincts that faded in and out like an old AM radio receiver. I never really found a consistently successful approach, but I certainly gained an appreciation for leaders who are effective at not only managing, but leading members of a team or organization in positive directions that they might not otherwise have collectively pursued.

For today’s leadership practitioners, however, the challenge of motivating a workforce seems increasingly challenging due, in large part, to the complexities presented by AI—especially generative AI (a subset of artificial intelligence with significant workforce implications). By now, many leaders know that generative AI is not just an over-hyped fad that will soon pass. It’s here to stay, and many such leaders understand, quite keenly, that they must get their organizations to fully embrace and capitalize on AI, or risk being left behind.

Yet how can executive leaders, or leaders at any level of an organization, motivate its members to fully embrace artificial intelligence when, for good or for bad, AI is so rapidly transforming roles, processes, and the very nature of work itself? How can leaders entice organizational members to embrace something that seemingly poses such an existential threat to our ways of working, doing, thinking and even being? What tactics can leaders leverage to paint a vision of the positive “multiplier effect” to be gained by combining human intelligence with artificial intelligence to create something that’s greater than the sum of the individual parts?

A Brief Review of Traditional Motivational Models Adapted to the AI Era

To answer these questions, and to consider how leaders can paint a compelling AI vision, let’s first look at how traditional motivational models, those that have stood the test of time, might be leveraged to create new strategies, tailored to the AI era. After all, while organizational landscapes are changing due to artificial intelligence, human nature remains the same. These models provide insights into the underlying factors that drive human behavior and can be adapted to the modern challenges posed by AI.

These theoretical models are often broadly divided into content motivation theories (which focus on explaining and predicting behavior based on individuals' needs); process motivation theories (which attempt to understand how and why people are motivated by examining the processes through which motivation occurs), and reinforcement theory (which focuses on how behavior can be shaped by the consequences that follow it).

Content Motivation Theories

Hierarchy of Needs (Abraham Maslow, 1940s) Maslow's Hierarchy of Needs is a well-known pyramid that categorizes human needs from the most basic physiological necessities to the highest level of self-actualization. According to Maslow, individuals are motivated to fulfill these needs in a specific order:

• Physiological Needs: Basic survival needs like food, water, and shelter.

• Safety Needs: Security and protection from physical and emotional harm.

• Love/Belonging Needs: Social interactions, friendships, and relationships.

• Esteem Needs: Recognition, status, and respect from others.

• Self-Actualization: Realizing personal potential and self-fulfillment.

In the context of AI, leaders can use this model to ensure that AI tools and processes enhance employees' ability to meet these needs. For example, AI can automate routine tasks, giving employees more time to focus on fulfilling higher-level needs such as creativity and personal growth.

Two-Factor Theory (Frederick Herzberg, 1960s) Herzberg's Two-Factor Theory divides workplace factors into two categories: hygiene factors and motivators.

• Hygiene Factors: Elements that prevent dissatisfaction but do not necessarily motivate (e.g., salary, company policies, working conditions).

• Motivators: Factors that truly drive employee engagement and satisfaction (e.g., achievement, recognition, the work itself).

When implementing AI, leaders should address hygiene factors by ensuring that AI integration does not disrupt job security or working conditions. Simultaneously, they should highlight how AI can be a motivator by enabling more meaningful and engaging work.

Acquired Needs Theory (David McClelland) McClelland's theory focuses on three specific needs that drive motivation:

• Need for Achievement: The drive to excel and succeed.

• Need for Power: The desire to influence and control others.

• Need for Affiliation: The need for interpersonal relationships and social connections.

Leaders can leverage AI to fulfill these needs by providing opportunities for employees to achieve through innovative projects, enhancing their influence through AI-enabled decision-making tools, and fostering collaboration with AI-driven communication platforms.

Process Motivation Theories

Equity Theory (Stacy Adams) Adams' Equity Theory posits that employees are motivated when they perceive fairness in the workplace. They compare their inputs (effort, skill) and outputs (rewards, recognition) to those of others.

• To ensure equity in the age of AI, leaders must transparently communicate how AI will impact roles and ensure that AI-driven changes benefit all employees fairly.

Expectancy Theory (Victor Vroom) Vroom's Expectancy Theory suggests that motivation is a product of three factors:

• Expectancy: The belief that effort will lead to desired performance.

• Instrumentality: The belief that performance will lead to a specific outcome.

• Valence: The value placed on the outcome.

Leaders should use AI to create clear pathways from effort to reward. For instance, AI can provide real-time feedback and performance metrics, helping employees see the direct impact of their work.

Goal Setting Theory (Edwin Locke) Locke's theory emphasizes the importance of setting specific, challenging goals to motivate employees.

• In the context of AI, leaders can set clear objectives for AI projects and ensure that these goals are aligned with employees' personal and professional aspirations. Regular progress reviews and adjustments can keep motivation high.

Reinforcement Theory

Reinforcement Theory (B.F. Skinner) Skinner's theory focuses on how behavior can be shaped by rewards and punishments. Positive reinforcement encourages desired behaviors by rewarding them, while negative reinforcement discourages undesirable behaviors.

• Leaders can apply reinforcement theory by using AI to track and reward employee achievements, thereby promoting a culture of continuous improvement and innovation.

Conclusion

Obviously, motivating members of practically any organization to broadly adopt and adapt to the realities of artificial intelligence is a highly complex endeavor, requiring exceptional and dynamic leadership skills. Nevertheless, these traditional motivational models perhaps suggest some basic considerations vis-a-vis developing strategies to motivate a workforce in the AI era. At a minimum, these and other motivational methodologies, can assist leaders in creating environments where employees are not only willing but eager to embrace AI, seeing it as a tool that enhances their roles and supports their professional growth. As we navigate the complexities of AI integration, these and other timeless principles can guide us in fostering a motivated, engaged, and forward-thinking workforce.

About the Author: David Ragland is a former senior technology executive and an adjunct professor of management. He serves as a partner at FuturePoint Digital, a research-based AI consultancy specializing in strategy, advisory, and educational services for global clients. David earned his Doctorate in Business Administration from IE University in Madrid, Spain, and a Master of Science in Information and Telecommunications Systems from Johns Hopkins University. He also holds an undergraduate degree in Psychology from James Madison University and completed a certificate in Artificial Intelligence and Business Strategy at MIT. His research focuses on the intersection of emerging technology with organizational and societal dynamics.