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Abstract: General artificial intelligence (GenAI) shows immense promise for enhancing human capabilities and transforming organizational operations, but also presents significant challenges that leaders must begin addressing now. As GenAI systems rapidly advance, they offer opportunities to augment human knowledge work, but also carry risks around job displacement and workforce transformation. Leaders must proactively manage this shift by reskilling workers, fostering human-AI collaboration through new organizational models, and cultivating a culture that harnesses GenAI's benefits while preserving human dignity. Continuous learning, experiments with new business models, and holistic risk management will be critical as organizations navigate the unpredictable progression of this paradigm-shifting technology. By taking a strategic, human-centric approach, leaders can position their organizations to thrive in the emerging era of artificial general intelligence.
General artificial intelligence, commonly referred to as GenAI, shows unprecedented promise for both enhancing human capabilities and fundamentally transforming how organizations operate. While societal implications will take time to unfold, leadership must begin planning now for the paradigm shift GenAI represents.
Today we will explore how GenAI capabilities may evolve, the opportunities and challenges they present, and specific actions leaders can take to strategically position their organizations.
GenAI and Human Augmentation
GenAI refers to systems that can perform any intellectual task that a human can. While we are still years away from achieving true human-level artificial general intelligence, progress is rapid. Neural networks like AlphaGo have achieved superhuman abilities in strategic reasoning domains through self-supervised learning from massive datasets (Silver et al., 2016). Advances like self-driving vehicles show AI that can perceive and navigate complex, real-world environments once thought only within human capacity (Bojarski et al., 2016).
As capabilities multiply, organizational leaders must consider how GenAI may augment human workers. Researchers foresee AI assisting with knowledge work like legal research, medical diagnosis, financial analysis and more (Brynjolfsson and McAfee, 2014). GenAI systems interacted in natural language could automate many customer support roles while also enhancing human customer service representatives with just-in-time information and insights. AI personal assistants show early promise to streamline workflows and information access for knowledge workers of all types.
Managing Workforce Transformation
While human augmentation capabilities present opportunities to enhance productivity, they also carry workforce risks that require strategic leadership. As AI assumes routine tasks, many current jobs will be eliminated or redefined (Frey and Osborne, 2017). Leaders must help workers reskill and transition to roles best matching unique human strengths like flexibility, creativity and socio-emotional skills. Significant cultural change and ongoing reskilling will be needed across organizations.
Proactive strategic workforce and change management will be critical. Leaders could offer job rotations, skill assessments and personalized development roadmaps tied to organizational priorities. Reskilling programs demonstrating a commitment to lifelong learning will build trust as work changes. Strategic internal mobility and outplacement partnerships can help transition displaced workers to new opportunities. Transparent communication and inclusion of workers in strategic planning strengthens change management efforts.
Business Model Innovation
GenAI will enable new business models that leaders must explore now. AI personal assistants could offer consumers on-demand access to services via subscription models. Financial services firms are using AI to offer customized investment products accessible via app interfaces. Manufacturers leverage AI to provide predictive maintenance services alongside physical products.
Leaders must experiment now to understand where GenAI enables new value propositions. This may involve cross-functional pilots partnering technology, product management and customer-facing teams. Leaders should foster an environment tolerating failure during experiments focused on learning rather than short-term gains. Open-minded partners across industries can provide outside perspectives on new possibilities. Investing in strong customer insights capabilities will be vital to design GenAI-powered offerings customers truly need and value.
Organizational Design for GenAI Partnership
GenAI systems will be most impactful partnering with - not replacing - human talent. Leaders must redesign how work gets done to foster collaboration between humans and AI. Matrix structures blurring traditional functions could pair data scientists, product managers and subject matter experts on cross-functional teams. These teams would be empowered to rapidly design, launch and iterate on GenAI-enhanced offerings.
Autonomous work groups should have dedicated GenAI systems as trusted advisors and coworkers. Open communication and consensus-building between humans and AI partners will be needed given GenAI's potential for unforeseen behaviors and outcomes. Interactive training methods help socialize AI systems to understand organizational culture and priorities. Regular performance reviews involving human-AI teams fosters continuous learning and improvement journeys together.
Culture and the Human-GenAI Relationship
A organizational culture valuing human dignity will shape appropriate and ethical deployment of GenAI. Clear principles around transparency in AI decision-making and appropriate human oversight must be socialized. Skill-building programs help all employees understand GenAI capabilities and limitations while fostering co-creativity between human judgment and AI insights.
Regular training in areas like identifying AI bias and periodic impact assessments keeps the human-GenAI relationship dynamic. Leaders must role model emphasizing unique human qualities like empathy that AI cannot replace. Together, clear cultural ground rules and ongoing education cultivate an environment where GenAI enhances rather than compromises what it means to be human in the workplace. The goal is for humans and AI to view each other as peers working interdependently toward shared missions.
Managing Risk with a Learning Mindset
While opportunities abound, the rapid progression of GenAI naturally introduces risks leaders must actively manage. Potential areas include:
Systemic risks if skills become obsolete faster than reskilling can occur at scale;
Societal risks if certain populations are systematically disadvantaged as work changes;
Legal/regulatory risks as liability and privacy concerns emerge from autonomous systems; and
Reputational risks if human-GenAI relationships are not handled ethically and transparently.
Proactive risk management demands continuous learning and adapting to an unpredictable future state. Scenario planning and regular "what if" discussions prepare for potential issues. Multi-stakeholder engagement including regulators and social groups provides outside perspective. Frameworks like the OECD AI Principles offer guidance around issues like privacy, bias and economic/social disruption to future-proof strategies. Leaders must balance risk oversight with an experimental, growth-oriented mindset.
Conclusion
The intelligent technologies of GenAI portend profound opportunities to enhance human capabilities and transform industries. With strategic leadership and focus on organizational readiness throughout this evolution, businesses and society can harness AI's full promise. Key actions include workforce reskilling, experimenting with new business models, organizational design empowering human-AI teams, cultivating strong yet flexible governance and prioritizing continuous learning in navigating both promise and uncertainty ahead. Leaders taking a holistic, culture-first perspective will best position their organizations to thrive in a new technological paradigm with artificial general intelligence at the fore.
References
Bojarski, M., Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Freeman, C., & Osborne, M. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
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.
Suggested Citation: Westover, J. H. (2025). The Evolving Role of Leadership in a Technological Age: Preparing Organizations for the Rapid Advancement of General Artificial Intelligence. Human Capital Leadership Review, 19(1). doi.org/10.70175/hclreview.2020.19.1.1
