Artificial intelligence (AI) and other advanced technologies are transforming organizations in profound ways. While AI brings opportunities that can boost productivity and efficiency, it also poses challenges that require leadership to rethink how work gets done and how employees are valued and developed. For organizations to thrive in this new landscape, leaders must rebuild their cultures and structures around human potential unlocked by AI.
Today we will explore what rebuilding organizations for AI entails, based on research and offers practical recommendations tailored to different industries.
Understanding AI's Impact on Work
To effectively rebuild for AI, leaders must understand how it is changing the nature of work. Extensive research confirms that while AI will primarily augment rather than replace human work, job disruption will be widespread. According to McKinsey Global Institute, around 30% of workplace activities globally are automatable through current AI technologies. Many jobs will remain but be significantly redefined as tasks are automated, integrated or enhanced by AI capabilities (Brynjolfsson & McAfee, 2014; McKinsey Global Institute, 2017). Both cognitive and physical work will be impacted, from knowledge-intensive roles in healthcare and legal work to jobs requiring dexterity like manufacturing and food preparation. In some cases, entirely new job categories will emerge that blend human skills with AI tools.
The Skills Organization for AI
Given AI's effects on job composition, leaders must carefully assess both current and future skills requirements. Research from the World Economic Forum suggests core human competencies like creativity, originality, initiative-taking, critical thinking, persuasion and collaboration will remain highly valuable for jobs augmented by machines (World Economic Forum, 2018). Technical skills specific to emerging AI technologies like machine learning and natural language processing will also grow in demand. However, "soft skills" that center on uniquely human abilities like leadership, emotional intelligence, communication and problem-solving are poised to become even more important differentiators in the AI-infused workplace.
To position employees and organizations for success with AI, leadership must thoughtfully rebuild skills frameworks. This involves:
Conducting skills audits to understand existing vs. needed competencies
Developing robust reskilling/upskilling programs emphasizing "human" abilities strengthenable through experience
Investing strategically in continuing education and lifelong learning paths aligned to organizational goals
Embedding skills cultivation as a core part of work redesign and job evolution processes with AI
Collaborating externally through programs like apprenticeships, industry certifications and retraining partnerships
The Structure for Empowerment with AI
In addition to skills, leaders must examine organizational structure in light of AI. Traditional hierarchies built around specialized functional roles will become less effective as jobs transform and cross-functional collaboration rises in importance (Manyika et al., 2017). Research from Bain & Company advocates a network model where departments dissolve into agile, cross-functional teams empowered to pursue opportunities at the intersection of business, technology and customer needs with support from a platform of shared services (Daugherty & Wilson, 2018).
To structure for empowerment with AI:
Break down silos by establishing multi-skilled, autonomous teams focused on outcomes rather than processes
Deploy a shared services platform for functions like HR, finance and IT to free frontline teams for innovation
Distribute decision-making to flatten hierarchies and speed responses in a fast-paced AI environment
Institutionalize rotation programs to broaden perspectives and nurture a flexible, adaptive workforce
Rethink performance metrics to evaluate teams based on impact, adaptability and development over strict outputs
The Culture for Human Potential with AI
Even with the right skills and structure, leadership must purposefully shape culture for the human-AI partnership to thrive. Extensive research has shown culture to be the ultimate driver of organizational performance, especially in VUCA (volatility, uncertainty, complexity, ambiguity) periods like those ushered in by AI (Schein, 2017; Deloitte, 2018). Leaders must ensure the culture empowers employees to realize their full potential working alongside AI.
To build this empowering culture:
Communicate a compelling vision where human judgment, qualities and creativity are prized alongside responsible AI deployment
Champion experimentation, continual learning and intrapreneurship over risk avoidance
Reward collaboration, adaptability and ingenuity over individual outputs or siloed thinking
Develop psychologically safe environments where humans and machines can learn from inevitable mistakes
Invest in wellbeing with progressive benefits, flexible work and emphasis on whole-person development
Rebuilding for Financial Services
To illustrate how one industry might rebuild, consider financial services where AI is automating processes like underwriting, streamlining customer service, and powering digital advice engines. For financial institutions, the skills organization could involve:
Conducting skills surveys across roles like loan officers, customer support reps and wealth managers
Developing bootcamps in programming, data literacy and design thinking to expand the talent pool for AI product development
Launching online micro-credentialing platforms for continuing education in areas fused with AI like behavioral finance
Structure may pivot to interdisciplinary pods handling end-to-end client journeys rather than segmented roles. For example, a pod manages onboarding new customers from initial outreach and application through ongoing relationship management. Performance moves from siloed metrics to outcomes like customer lifetime value and socioeconomic impact.
As for culture, leaders champion experimentation through programs like internal startups and encourage risk-taking within guardrails. They evaluate not just quarterly sales but employee wellbeing, learning and diversity of ideas. An overarching focus on ethical, inclusive and explainable AI fosters the essential human-centered partnership with machines.
Rebuilding for Manufacturing
In manufacturing, AI is augmenting roles across the value chain from product design through production planning and quality control. Skills rebuilding involves reskilling the existing workforce in robotics, automation, data analytics and modeling while also cultivating creative, problem-solving graduates through apprenticeships and continuing education programs.
Structure shifts away from functional verticals towards agile, cross-trained pods responsible for entire processes or product families. Multi-skilled "planners" oversee balanced production schedules optimized by AI while maintaining a human hand. Knowledge workers become "augmentation engineers" designing workflows and training algorithms.
A startup culture inspires experimentation through internal "factory of the future" projects. Leaders reward ingenuity, teaching AI and continuous improvement over static goals. Wellness initiatives address lifestyle changes from hybrid human-robot work while flexibility supports ongoing education. An emphasis on explainable, trustworthy and equitable AI fosters acceptance as machines augment workforce capacity and capabilities.
Conclusion
By thoughtfully rebuilding skills frameworks, structures and cultures centered on human potential, leadership can successfully drive organizations through disruption unleashed by AI. With preparation and focus on the uniquely human qualities augmentation cannot replace, businesses across sectors can leverage new technologies to open opportunity for employees and empower innovation. Although AI brings uncertainty, leaders who embrace talent development, empowerment, lifelong learning and a collaborative human-AI partnership will be best equipped to thrive in the technology-infused future of work. Overall, a human-centered approach to rebuilding organizations can realize AI's full benefits while nurturing teams, careers and enterprises for sustainable long-term success.
References
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.
Deloitte. (2018). 2018 Deloitte Global Human Capital Trends. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/gccta-2018-human-capital-trends.pdf
Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. McKinsey Global Institute.
McKinsey Global Institute. (2017, November). A future that works: Automation, employment, and productivity. https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works
Schein, E. H. (2017). Organizational culture and leadership (5th ed.). Jossey-Bass.
World Economic Forum. (2018). The future of jobs report 2018. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf
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.
