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Leadership Strategies for Ensuring Workers Thrive in an AI-Driven World


Artificial intelligence (AI) is increasingly impacting businesses across industries and job roles. While AI adoption creates opportunities to enhance productivity and performance, it also brings about challenges related to changing job skills and responsibilities. As an organizational leader, helping workers navigate technological change and get the most out of AI is key.


Today we will explore leadership strategies for maximizing AI's benefits while supporting workers through its integration.


Educating Workers on AI's Capabilities and Impact

One of the most important steps leaders can take is educating workers about AI. Without proper understanding of what AI can and cannot do, fears about job loss may arise (Brynjolfsson & McAfee, 2014). Leaders must communicate openly and clearly about how AI will impact day-to-day work. This involves:


  • Sharing which job functions AI is best- and least-suited for based on research (Levy & Murnane, 2013)

  • Explaining how AI augments rather than replaces many roles by handling routine tasks (Brynjolfsson & McAfee, 2017)

  • Discussing new skills and responsibilities workers may assume as AI handles previous duties (Frey & Osborne, 2017)

  • Holding group training sessions to demonstrate AI tools and how they enhance work


For example, at Anthropic, an AI safety startup, the CEO helps research scientists understand how AI assists with automated testing, data labeling, and simulation work. This eases fears of reduced responsibilities.


Focusing education efforts upfront maximizes buy-in for AI initiatives. Workers who comprehend AI's job impacts feel more empowered and motivated to adapt successfully (McKnight et al., 2017). Leaders must serve as guides to help de-mystify new technologies.


Reskilling and Upskilling the Workforce

While AI handles routine tasks, many jobs still require human judgment, problem-solving, creativity, and empathy (World Economic Forum, 2018). Leaders therefore need workforce strategies for developing these "human skills" (Davenport & Kirby, 2016). This involves:


  • Assessing which new skills will be most valuable in an AI-driven workplace based on industry reports and forecasts (World Economic Forum, 2016)

  • Offering subsidized online courses and on-site training to help workers gain skills like analytical reasoning, complex problem-solving, and collaboration (Prabhu, 2019)

  • Rotating high-potential staff through different roles to enhance adaptability and exposure to new technologies

  • Formally recognizing reskilling achievements through promotions and pay increases as an incentive


For instance, Royal Dutch Shell invested over $1 billion to reskill its global workforce for the energy transition. Leadership rolled out a digital academy with 700 online courses covering data analytics, AI, and renewable technologies. Over 50,000 employees have graduated to date.


By proactively reskilling, leaders empower workers to tackle more cognitive tasks alongside AI. This maintains engagement and job satisfaction among employees undergoing technological change.


Encouraging an Experimental and Learning Culture

As technologies rapidly evolve, leaders must foster workplace cultures where experimenting, iterating, and continuous learning are the norm (Daugherty & Wilson, 2018). Tactics to build such cultures include:


  • Establishing an innovation fund for employees to test new ideas incorporating emerging tech

  • Hosting "hackathons" and design-thinking workshops to solve business problems with AI in hands-on, collaborative settings

  • Allowing staff to spend 10-20% of work hours on self-directed skill-building and technology exploration

  • Promoting transparent sharing of AI pilot program learnings—both successes and failures

  • Recognizing and rewarding curiosity, creativity, and risk-taking over just production metrics


For instance, Cognizant set up an AI Innovation Center allowing employees to build and test AI prototypes. Staff can access mentors, data scientists, and the latest AI toolkits. This has spurred the development of over 100 internal AI solutions.


By encouraging experimentation, leaders cultivate a growth mindset where setbacks are seen as learning opportunities. Workers remain engaged problem-solvers poised for lifelong adaptation.


Democratizing Access to Data and AI Tools

To derive value from AI, businesses need both data science talent and widespread AI application across divisions (Davenport et al., 2020). Yet many AI initiatives remain isolated "skunkworks projects." Leaders must open access to AI capabilities organization-wide through efforts like:


  • Hiring chief data officers to ensure democratized, responsible use ofcorporate data resources

  • Provisioning self-service AI platforms that allow non-experts to deploy and refine AI models via low-code/no-code tools and pre-trained assets

  • Forming AI coalitions of diverse stakeholders to advocate employee AI needs and pilot solutions

  • Investing in data literacy training so staff can leverage AI without deep technical skills

  • Launching an internal AI marketplace to spread awareness of AI models, case studies, and collaboration opportunities


For example, Anthropic set up an internal marketplace where staff globally can propose, improve, and implement AI solutions together despite geographical separation. This has spurred over 50 cross-team partnerships.


By democratizing AI access, leaders empower innovation from all levels and divisions. Workers remain engaged problem-solvers capable of incorporating emerging technologies into their roles.


Fostering Psychological Safety and Inclusiveness

When undergoing change, workers need support managing anxiety, adapting workstyles, and learning from failures. Leaders can make or break culture through promoting:


  • Psychological safety where employees feel comfortable sharing concerns without judgment (Edmondson, 2018)

  • Inclusiveness training helping staff value diverse perspectives that lead to stronger AI solutions

  • Mentorship programs pairing experienced and new employees to navigate uncertainty together

  • Cross-training initiatives exposing staff to various roles for networking, support, and fresh viewpoints

  • Flexible work arrangements accommodating new responsibilities or reskilling needs

  • Listening tours where leaders regularly check-in on well-being, pain-points, and emerging needs


For example, Cognizant conducts empathy interviews to understand anxiety over AI and reskills support staff may require. Cross-functional teams then address top concerns. This nurtures an environment where adapting to change feels less overwhelming.


By fostering inclusion and support, leaders ease apprehension over job shifts. Workers remain focused, productive contributors maximizing AI's dividends.


Establishing Shared Success Metrics

To build collective responsibility for AI success, leaders need goals valued across the organization. Rather than emphasizing short-term efficiency metrics alone, inclusive successes may capture:


  • Employee engagement and well-being assessed via surveys addressing work-life balance, development opportunities, and inclusion

  • Upskilling participation and completion rates highlighting the workforce's long-term employability

  • Innovation culture metrics like hackathon participation, pilot program launches, and patent/IP filings

  • Customer experience and loyalty data revealing AI's impact beyond superficial metrics

  • Environmental sustainability metrics for industries undergoing eco-friendly transformation with AI's aid


For example, Schneider Electric tracks how its "ecowatt.city" AI microgrid projects boost renewable energy adoption while sustaining jobs. Outcomes are celebrated across the company to align efforts around a shared mission.


Prioritizing holistic, multi-stakeholder metrics ensures all benefit from and contribute to AI-driven transformation. Workers feel invested in long-term, sustainable business success.


Conclusion

As AI becomes increasingly intertwined with business operations, leadership holds the key to ensuring workers thrive amid disruption. Research shows the strategies outlined above maximizing AI's benefits through proper education, reskilling, culture-building, tool access, well-being support, and inclusive definitions of success. Industries like energy, technology, consulting and more demonstrate applying these approaches leads to engaged, high-performing workforces critical for any organization's adaptation and survival in an AI-empowered world. Overall, leaders guide responsible, ethical and human-centered AI integration by empowering all staff to become active participants in—not passive recipients of—technological change.


References


  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.

  • Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(7-8), 3-11.

  • Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. Harper Business.

  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.

  • Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.

  • Edmondson, A. (2018). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. John Wiley & Sons.

  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.

  • Levy, F., & Murnane, R. J. (2013). Dancing with robots: Human skills for computerized work. Third Way NEXT.

  • McKnight, L., Carter, M., Hobbs, J., & Kudenko, D. (2017). Troubling transformations: Considering ethics in AI system design. In Proceedings of the 30th International Conference on Legal Knowledge and Information Systems (JURIX 2017) (pp. 101-110). IOS Press.

  • Prabhu, V. (2019). Focusing on the role of AI in workforce transformation. Strategy & Leadership.

  • World Economic Forum. (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. World Economic Forum.

  • World Economic Forum. (2018). Towards a reskilling revolution: A future of jobs for all. World Economic Forum.

 

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.



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