By Jonathan H. Westover, PhD
Abstract: Artificial intelligence (AI) is increasingly disrupting traditional business models and creating new growth opportunities across industries. In human resources (HR), generative AI technologies, such as natural language processing, offer significant potential to enhance productivity and support human decision-making. To effectively harness these technologies, HR leaders must adopt a strategic approach that combines academic insights with practical considerations. This article outlines five critical steps for successfully integrating generative AI into HR practices: understanding the technology’s capabilities and limitations, aligning its use with organizational strategy, engaging stakeholder support, planning for change management, and establishing robust governance and oversight. By following these guidelines, organizations can leverage AI to augment HR functions and drive competitive advantage while managing associated risks and ensuring ethical application.
Artificial intelligence (AI) is rapidly transforming nearly every industry, disrupting traditional business models and driving new opportunities for growth. In human resources (HR), generative AI technologies like natural language processing are poised to augment human judgment and boost productivity. However, for organizations to successfully apply these technologies requires a strategic approach informed by both academic research and practical realities of implementation.
Today we will explore five key steps HR leaders can take to strategically leverage generative AI for competitive advantage: understanding capabilities and limitations, aligning with strategy, engaging stakeholder support, planning change management, and establishing governance/oversight.
Understanding Capabilities and Limitations
Before embarking on any AI initiative, it is critical for HR leaders to develop a nuanced view of what generative technologies can and cannot do based on their current state of development.
Capabilities of Generative AI: Generative AI encompasses a broad range of technologies capable of synthesizing new content based on examples or probabilistic models rather than direct human instruction. Two main capabilities relevant to HR are natural language generation and computer vision/image generation (Markoff, 2022). Natural language AI has made strides in automating more routine communication tasks like responding to basic employee queries, generating personalization documents at scale, and even drafting sections of documents like performance reviews (Brynjolfsson & Mitchell, 2017). Computer vision AI can analyze photos to extract metadata that helps with tasks like onboarding paperwork and benefits enrollment.
Limitations of Generative AI: While capabilities are expanding rapidly, generative AI still has substantial limitations (Sculley et al., 2015). Models are narrow - good at one task but not versatile. They lack common sense reasoning, have narrow contexts, and can hallucinate or generate toxic, unreliable, or biased outputs without careful oversight (Bommasani et al., 2021). For high-stakes HR judgments, full replacement of humans is still far off. AI augments, but does not replace, human expertise, nuance, empathy and common sense. Remaining aware of both promise and peril will guide responsible integration.
Aligning with Organizational Strategy
For any new technology to deliver value, it must be aligned with and support the organization’s strategic objectives (Porter & Heppelmann, 2014). HR leaders should thoughtfully consider how generative AI could help advance key strategic goals like talent management, employee experience, diversity and inclusion before implementation.
For example, Anthropic, an AI safety startup, leveraged generative AI to enhance their company culture (Hill et al., 2020). They used conversational agents to onboard remote employees, answer basic queries, and distribute company updates - freeing up managers for more strategic work aligned with their goal of cultivating top technical talent globally. This helped Anthropic recruit top AI researchers from diverse geographies efficiently while strengthening cultural cohesion across a distributed workforce.
Conversely, if integration of generative AI is not strategically scoped and aligned, it risks becoming a distraction that offers few returns. Careful reflection on strategic fit will guide resource prioritization and maximize value realization from AI investments.
Engaging Stakeholder Support
No technology adoption succeeds without buy-in from impacted stakeholders (Kotter, 2007). To gain support, HR should communicate the potential benefits of generative AI transparently and address likely concerns upfront through an engagement process (Oreg & Berson, 2019).
For example, when Salesforce implemented AI-assisted recruiting, they conducted focus groups with recruiters to understand perspectives and anxieties (Marks, 2019). Recruiters worried about job losses but were open once educated that AI augments, not replaces, human judgment. Salesforce also created expert user groups to give feedback throughout development, building advocates that eased wider adoption.
Gaining stakeholder insights and cultivating two-way communication early on helps design solutions addressing real needs, manage change more smoothly, and spot/mitigate unintended issues proactively. Done right, engagement builds trust that speeds responsible integration of new technologies.
Planning Change Management
Even the best-designed technology will fail without a change management plan addressing people, processes and cultural dimensions of adoption (Armenakis & Harris, 2009). HR should develop a plan outlining how work will evolve, skills needed, and support provided to help stakeholders transition.
When Anthropic adopted AI-enabled tools, they paired technical training with mentorship programs, rotating work assignments, and career coaching to help employees upskill comfortably and avoid skills gaps disrupting work (Hill et al., 2020). Change management also addressed processes - redesigning tasks AI assists with while maintaining necessary human oversight and judgment.
Thoughtful change planning is vital to technology success and mitigating transition risks like resistance, skills gaps or workflow disruptions. With a change roadmap in place, HR is better positioned to shepherd generative AI integration smoothly and maximize benefits for all stakeholders.
Establishing Governance and Oversight
For any new technology especially one with accountability and ethics implications like AI, clear governance is needed to establish accountability, guardrails and oversight mechanisms (Jobin et al., 2019).
HR should work cross-functionally to establish principles, roles and processes for issues like: data governance ensuring privacy, transparency in model development/training; protocols for monitoring outputs/detecting biases; version control and documentation; incident response; user support; and continued assessment/refinement.
For example, Anthropic established an internal review board including ethicists and subject matter experts from different functions who approve all new AI applications, review incident reports and influence model development roadmaps to prioritize fairness, transparency and safety (Hill et al., 2020).
HR plays a key role not just in day-to-day governance but shaping a culture where issues are surfaced transparently, balanced with innovation. Clear boundaries and oversight enable both accountability and responsible progress. Overall, governance provides the "guardrails" for strategic, stakeholder-centric and risk-aware adoption of generative AI.
Conclusion
Generative AI holds promise for augmenting HR operations at scale, but realizing benefits requires a strategic, risk-aware integration approach. The five key steps outlined - understanding technology realities, aligning with strategy, engaging stakeholders, planning change management comprehensively, and establishing governance - provide HR leaders a framework for thoughtful adoption maximizing benefits while mitigating common risks and issues. Responsible integration of AI rooted in both research and practice is required for organizations to gain sustainable competitive advantage through emerging technologies. With care and oversight, generative AI promises to enhance HR work in transformative yet trustworthy ways.
References
Armenakis, A. A., & Harris, S. G. (2009). Reflections: Our journey in organizational change research and practice. Journal of Change Management, 9(2), 127–142. https://doi.org/10.1080/14697010902879079
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Kotter, J. (2007). Leading change: Why transformation efforts fail. Harvard Business Review, 85(1), 96-103.
Markoff, J. (2022, January 11). The incredible, terrifying possibilities of generative AI. Wired. https://www.wired.com/story/generative-ai/
Marks, E. S. (2019). Implementing AI in recruiting transforms how Salesforce hires. Fortune. https://fortune.com/2019/10/04/salesforce-artificial-intelligence-recruiting/
Oreg, S., & Berson, Y. (2019). Leadership and employees' reactions to change: The role of leaders' personal attributes and transformational leadership style. Personnel Psychology, 72(1), 159-191. https://doi.org/10.1111/peps.12268
Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., & Young, M. (2015). Hidden technical debt in machine learning systems. In Advances in neural information processing systems (pp. 2503-2511).
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. (2024). Strategic Steps for Using Generative AI in HR. Human Capital Leadership Review, 11(1). doi.org/10.70175/hclreview.2020.11.1.12
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