Artificial intelligence (AI) is advancing rapidly and poised to transform the modern workplace in profound ways. While there is intense debate around job losses from automation, AI also presents tremendous opportunities to empower human workers and enhance organizational performance if adopted and integrated effectively. This paper aims to provide practical guidance for leaders seeking to maximize the benefits of AI for their workforce.
Today we will focus on how leaders can foster a collaborative human-AI partnership that leverages the unique strengths of both. With proper empowerment of workers, organizations stand to gain enormous competitive advantages from AI while simultaneously nurturing a fulfilling work environment.
Aligning AI Goals with Human Values
Before embarking on any AI initiative, leadership must first ensure aligning goals with core humanistic values. Too often, technology is adopted for its own sake without consideration for impact on people. For AI to truly empower workers, human well-being and dignity must be the top priority (Lake et al., 2017). Leaders must proactively assess how new technologies may negatively or positively affect job design, work-life balance, ethical conduct, and other human factors.
Specifically, leadership should involve workers directly in goal-setting to understand concerns and opportunities from their perspectives. With AI's growth still in early stages, input from those on the frontlines can help shape development and implementation pathways maximizing benefit and minimizing harm. Workers should feel empowered partners in progress, not replaceable cogs in a machine. By cultivating genuine collaboration and trust from the start, leadership sets the stage for long-term success (Daugherty & Wilson, 2018).
Investing in Reskilling and Upskilling the Workforce
While some jobs may change significantly or disappear due to automation, AI also creates new types of roles best filled by humans. However, seizing these opportunities requires continuous learning to develop novel skills alongside technology. Leadership must view reskilling and upskilling investments not as costs but as vital to fostering an adaptable, AI-empowered workforce primed for future growth (Manyika et al., 2017).
Some specific strategies include:
Offering subsidized online courses and degree/certificate programs in fields like data analytics, machine learning, AI engineering, and more. Workers gain qualifications for emerging career paths while strengthening companies' human capital.
Launching internal reskilling academies. With guidance from learning and development specialists, organizations can tailor curricula to their unique business needs. This embeds continuous learning directly into work culture.
Rotating employees to different job functions. Cross-training builds a diverse skillset applicable across changing roles. It also gives employees first-hand perspectives on various operations to inform AI applications.
With focus on long-term human capital development, companies transition smoothly alongside technological progress instead of struggling to catch up (World Economic Forum, 2018). Reskilling should benefit all willing workers, not just technical specialists, to foster truly participatory innovation culture.
Designing Jobs Optimized for AI Collaboration
As AI automates routine tasks, new kinds of jobs emerge centered around "tasks that machines are less capable of—and where humans retain an advantage" (Muro et al., 2017, p. 6). Properly understanding human-AI capabilities allows designing complementary roles empowering each. Rather than fearing technology, workers can embrace collaboration that plays to their strengths.
Leadership should examine all positions to identify:
Tasks well-suited to AI's computational abilities like processing large data, recognizing patterns, and executing programmed decisions
Tasks requiring human strengths like empathy, creativity, versatile problem-solving, and sound judgement
Job design should then optimize human-AI partnerships; for example:
Customer service representatives assisted by AI for initial questions routing to specialized human agents only for complex needs
Manufacturing workers programming and overseeing automated production lines while focusing on process improvement, quality control, and machine maintenance
Knowledge workers supported by AI for information retrieval and preliminary analyses to focus on strategic thinking, end-to-end project management, and stakeholder coordination
By thoughtfully blending the digital and human, new forms of "AI-assisted jobs" (Brynjolfsson & Mitchell, 2017) emerge that are both more engaging for employees and beneficial for business outcomes. Workers gain empowerment rather than fear from technology.
Developing an AI-Ready Management Style
While technological change requires new employee skills, leadership style must also evolve to empower human potential in an AI world. Traditional "command-and-control" structures lose relevance as jobs blend human judgment with digital automation. Instead, developing an "AI-ready" management approach focuses on:
Coaching. Rather than micromanaging task completion, managers coach teams on goal-setting, strategic planning, and exploring new opportunities through continuous learning.
Facilitating collaboration. Complex problems demand pooled human and machine intelligence. Effective managers break down silos and facilitate cross-functional, human-AI partnerships.
Empowering ownership. Delegating authority and accountability allows workers agency over their roles. With proper training and resources, employees self-manage better performance through AI.
Cultivating diversity. Diverse perspectives prevent “groupthink” increasingly challenged by AI. Inclusive cultures where all feel valued partners in progress breeds team resilience facing change.
Measuring impact, not just outputs. Beyond task-based metrics, assess how work contributes to broader strategic objectives and human/organizational well-being to maintain focus.
With an eye towards empowering human judgment, AI-ready managers unleash workforce potential through a collaborative, high-performing work environment primed for technology-driven growth.
Case Studies: AI Transformation in Practice
Call Center Ops at Anthropic (San Francisco, CA)
Traditional call centers suffer high turnover due to stress/burnout. Anthropic develops AI assistants helping operators by resolving most basic inquiries through natural conversations. This frees human time for complex issues requiring empathy while improving customer experience. Operators partner closely with engineers, providing feedback to refine AI capabilities continuously. Morale doubled as roles became more engaging while boosting throughput 30%.
Warehouse Operations at Ocado (Hatfield, UK)
Ocado uses robotics/AI across warehouse facilities to pick/pack grocery orders up to five times faster than traditional models. Rather than layoffs, roles transformed as “Operations Associates” overseeing automated systems, improving processes, and focusing on problem-solving/innovation. Turnover dropped significantly as jobs became higher-skilled while doubling fulfillment capacity with fewer total workers.
Knowledge Management at Anthropic (San Francisco, CA)
Legal/compliance teams struggle to find relevant documents among vast data stores. Anthropic’s AI search assistant ingests contracts/policies to surface key clauses upon natural language queries. This helps lawyers spend less time researching and more strategizing/mentoring junior staff. Over half of search volume now AI-handled with human oversight to iteratively boost relevance and integrate new sources seamlessly.
Conclusion
With careful planning and leadership commitment to empowering workers, AI possesses immense potential for enhancing human performance, career opportunities, and organizational success. However, this demands proactively aligning technology goals with human-centered values from the start. Continuous reskilling/upskilling strengthens an agile workforce ready for changing job roles through thoughtfully blending complementary human-AI capabilities. Above all, leaders must cultivate management approaches nurturing employee agency, collaboration, diversity of thought and trust that technology ultimately aims to augment—not replace—human judgment and relationships central to work. By fostering true partnership between humans and machines, AI’s benefits multiply exponentially for people, business and society at large. The greatest returns lie ahead given will and effort applied to bringing out the best in each through collaboration.
References
Brynjolfsson, E., & Mitchell, T. (2017, September–October). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. https://doi.org/10.1126/science.aap8062
Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/S0140525X16001837
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017, November). A future that works: Automation, employment, and productivity. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works
Muro, M., Maxim, R., Whiton, J., & Hathaway, I. (2019). Automation and artificial intelligence: How machines are affecting people and places. Metropolitan Policy Program at Brookings. https://www.brookings.edu/wp-content/uploads/2019/01/2019.01_BrookingsMetro_Automation-AI_Report_Muro-Maxim-Whiton-FINAL-version.pdf
World Economic Forum. (2018). The future of jobs report 2018. Author. https://www.weforum.org/reports/the-future-of-jobs-report-2018
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
