Preparing HR for Generative AI Adoption

Preparing HR for Generative AI Adoption

Nataliya Harkins, Ph.D., MBA Nataliya Harkins, Ph.D., MBA
17 minute read

The surge of sophisticated AI tools has set new expectations for employee productivity and business performance. AI-based augmentations are revolutionizing industries, enhancing capacity, and accelerating the development of more informed organizational strategies. According to the Conference Board 2023 research, more than half of US employees already use generative AI tools, at least occasionally, to accomplish work-related tasks. This presents a promising future for organizations; integrating AI in HR and other functions offers enormous opportunities and necessitates careful optimization to fully leverage its potential. 

Large and small organizations strive to understand, define, and leverage generative AI (GenAI) to optimize operations, elevate customer and employee experiences, and drive enterprise efficiency. Driven by employees themselves or organizational leadership, the work and the work processes have been augmented by a combined force of humans striving for efficiency and machines, offering new technological capabilities, including predictive data algorithms and the application of machine learning models. It is time for organizations to appreciate the new era of powerful, intelligent machines.

HR leaders are critical in steering their organizations through this transformation as AI revolutionizes the business environment. By thoughtfully and strategically embracing AI in HR, the function can unlock new levels of productivity and efficiency while safeguarding the principles of fairness and equity that underpin successful and sustainable business practices. This empowerment is key to the successful integration of artificial intelligence.

The transformation landscape 

The rapid advancement of AI technologies is reshaping industries by driving productivity improvements and refining decision-making processes. From delivering personalized shopping experiences to predicting financial models with greater accuracy, AI's influence is pervasive. However, while AI promises increased productivity, operational efficiency, and enhanced employee engagement, HR leaders face significant challenges in integrating these technologies into organizational processes. As change agents and implementation partners, HR professionals must navigate a complex environment marked by regulatory compliance, bias testing, and ethical concerns, particularly regarding privacy, bias, and decision reliability. The effective use of AI in HR requires careful and comprehensive planning and insights. 

HR leaders should proceed with caution, considering valid concerns surrounding new workplace technologies. Augmenting human capabilities with AI enhances productivity and redefines how and where work is performed. As highlighted in Korn Ferry’s Future of Work Trend Report 2022, “new opportunities for how and where work gets done" are emerging as AI technologies become more embedded in business operations. In fact, AI is a common and often essential topic in innovation strategies in many organizations.  

Business leaders are prioritizing enterprise AI-enabled capabilities to gain benefits across core business functions. This focus on productivity gains also drives HR leaders to build compelling business cases for transformations using AI in HR within their teams. Notably, many HR departments are already experiencing AI's advantages, with significant adoption in areas such as recruitment, performance management, and onboarding. These early adopters set a precedent, demonstrating that AI can streamline processes, enhance decision-making, and contribute to a more agile, efficient, and responsive organizational operation.

For HR leaders, the path forward involves championing AI adoption and ensuring its implementation aligns with ethical standards and regulatory requirements. By addressing concerns around bias and privacy, HR can leverage AI to foster a fairer, more inclusive workplace. The challenge lies in balancing AI's innovative potential with the need for responsible stewardship, ensuring that technological advancements enhance, rather than undermine, organizational values and employee well-being.

The proliferation of GenAI in HR

In HR, GenAI tools have assumed multiple roles such as business advisor, insights generator, data manager, and professional coach. AI has already significantly influenced HR functions over the last decade. More than 50% of HR professionals used some form of AI in their work, according to 2019 Oracle’s AI@Work Study. Another study by Engagedly found that 45% of the surveyed organizations in 2023 currently use AI in HR Management functions. 

Generally, the most expected AI impact across HR operations will cascade to employee productivity, experience, and performance management. Examples can illustrate the extent to which HR leaders have already been working with AI technologies' embedded capabilities.

HR Chatbots and Virtual Assistants

Chatbots are becoming important tools to enhance the HR environment by handling HR inquiries, improving information access for all levels of employees, and elevating employee experience by delivering responsive HR. IBM's Global AI Adoption Index 2022 Report indicates that 28% of companies use or consider using NLP solutions (Natural Language Processing) for human resources or employee services. Applications of AI in HR are abundant here.

Recruitment and Hiring

According to a 2024 SHRM research study, among HR professionals who use AI in HR for recruiting, 65% are using it to help generate job descriptions, 42% are using it to customize job postings, while about 33% are using AI to review or screen applicant resumes, to communicate with applicants during the interview process, or to automate candidate searches. Among other HR tasks augmented by AI are pre-selecting applicants for interviews, administration, scoring, scheduling, and conducting video interviews. Other uses include the creation of AI-generated skill or game-based assessments.

Performance Management 

Performance evaluations and reviews have consistently been a critical focus area for HR and business leaders. With the improved connectivity and accessibility of performance data across departments and functions, organizations use AI to support performance management. According to the SHRM research study, common uses of AI in HR performance evaluation processes include assisting managers in providing more comprehensive or actionable feedback to their employees (57% ) and facilitating goal setting (46%).

Learning and Development

Long-term organizational adoption of Learning Management Systems (LMS) and other HR technology platforms like HRIS created a unique opportunity to personalize learning paths based on employee data, career goals, and training needs and requirements. The 2024 SHRM research study indicated that among the organizations that have adopted AI in HR, 43% of HR professionals use it for Learning and Development. The top use cases include recommending or creating personalized opportunities for their employees (49%), helping track employee learning progress (45%), and upskilling or reskilling their workforce (19%). 

The 2023 HR Executive survey provides a sober view on an interplay between AI and HR, with more than 60% of respondents indicating that the teams at their organizations are not yet using AI in HR. These statistics underscore the opportunities still open for broader adoption of generative AI across HR functions to drive greater efficiency, personalization, and strategic decision-making. Organizational leaders must be ready to optimize and elevate employee experiences, business processes, systems, and performance expectations to fully realize AI potential across its three core branches - predictive, generative, and conversational.

HR challenges: Beyond people and skills   

While GenAI has transformed services and experiences across industries and other functions, HR's work is only beginning. Some organizations have been championing the use and adoption of AI in the earlier stages due to market and competition pressures. For example, Goldman Sachs aims to train over 4,000 employees in AI in India in the upcoming year, where they house about one-third of the organizational engineering capabilities.

Preparing people, businesses, and technology to work together requires meticulous planning and execution. Here are some critical challenges that require open and close collaboration between business and HR leaders:

Internal Regulatory Frameworks. Public scrutiny of organizational practices impacted by AI tools has increased significantly over the past decade. Due to mishandled AI-driven processes, customers, partners, and employees have experienced adverse outcomes. To address this, establishing robust frameworks to govern AI usage is essential. These frameworks should ensure transparency, fairness, and compliance with privacy laws across the enterprise. Failure to implement such measures can severely damage a company's reputation.

Strategic Alignment. While the application of AI in business operations may be characterized as a routine optimization project, organizational goals, and workforce policies must reflect the major and minor aspects of such transformation and stay aligned throughout the implementation phase. Poor communication and lack of trust among leadership during this period can delay the benefits of new organizational capabilities.

Human Oversight. Logic and rational thinking are two core cognitive processes that are inherently human. Computing machines have become capable of performing previously laborious tasks with extraordinary speed and efficiency, executing creative and innovative inventions of the human mind designed to improve what machines do. As humans make errors, overlook, or underestimate the magnitude of AI impact, leaders must integrate human oversight into AI workflows to mitigate risks and ensure accountability as we continue to learn about technological shortcomings. AI reflects human logic and is subject to biases.

Transition Management. AI transformation signifies a shift to new ways of performing, evaluating, and improving what employees do every day. For HR leaders, navigating the organizational transition of routine tasks to AI-supported processes encompasses workflows such as candidate ranking, tailored learning plans, or performance reviews. AI promises efficiency but necessitates careful consideration of decision reliability, biased information, and the erosion of human elements. This task cannot be done well unless business and HR professionals collaborate openly and honestly when adopting AI through a legitimate exchange forum.

Person with Recommendations for HR practitioners to adopt Generative AI

Recommendations for HR practitioners to adopt GenAI effectively

Five key focus areas for building organizational readiness for AI implementation are offered to guide and prepare HR teams. Forward-thinking businesses have driven the rapid adoption of AI in HR and workplaces in general. In this race, business leaders had to quickly implement new operational processes and tools, often making decisions independently. According to a recent survey by Baker McKenzie on AI and corporate oversight, only 54% of C-Suite leaders involve HR in the decision-making process for AI tools. 

Whether it's businesses, organizations, or individual employees promoting new technologies, HR leaders who prepare for and embrace a structured approach to organizational readiness will be better positioned to work alongside business leaders in leading their organizations. Successfully integrating AI into an entire enterprise is a significant challenge for every organizational leader, and time is of the essence. 

In reality, most HR leaders may find themselves in one of two key scenarios as a starting point for establishing organizational readiness. Scenario A: HR acts as an advisor and facilitator once the use case for AI applications is completed and handed over to business leaders for implementation. Scenario B: HR acts as a business partner, advocate, and facilitator when the use case for AI applications is being discussed by organizational stakeholders. In either case, these five areas will provide a solid roadmap for creating organizational readiness for AI implementation.

1. Review and align strategic HR goals to new business objectives

Once organizations approve AI integration plans, HR leaders must conduct a comprehensive change impact analysis immediately. This involves several critical tasks:

  • Revising and Aligning Strategic HR Objectives: HR leaders must ensure that their strategic objectives align with the enterprise-wide AI implementation requirements. This alignment is critical for a cohesive transition and leveraging AI to meet overarching business goals.
  • Establishing Clear Timeframes: Defining specific timeframes for AI adoption and integration is essential. HR leaders must develop a detailed timeline outlining each phase of the integration process, ensuring that all stakeholders know their roles and responsibilities. 
  • Evaluating Alignment of HR Goals with Business Objectives: A key question for HR leaders is determining which HR goals do not align or cannot align with the new business objectives within the established timeline. This requires a thorough review of current HR initiatives and identifying areas where adjustments or new strategies are necessary.

2. Complete a comprehensive talent audit for key and supportive roles  

Integrating AI within an organization elevates the need for a highly trained and skilled workforce. Each organizational function will either contribute to the successful implementation of AI or hinder progress, making conducting a thorough talent audit crucial. Here’s how HR leaders can approach this task:

  • Conducting a Comprehensive Assessment of Workforce Capabilities: Begin by assessing the current capabilities of the workforce. Identify where AI can enhance effectiveness and create competitive advantages. This involves mapping existing skills, competencies, and roles that can benefit from AI integration and those that may become redundant or require significant transformation.  
  • Developing a Workforce Risk Mitigation Strategy: Identify potential risks to the workforce that may arise from AI integration. This includes assessing how AI impacts job roles, employee morale, and security. Develop strategies to mitigate these risks, such as redeploying affected employees to new roles, offering retraining programs, and providing clear communication about the changes.
  • Enhancing Employee Support and Engagement: Ensure employees are supported throughout the AI transformation. This involves maintaining open lines of communication, addressing concerns promptly, and involving employees in the transition process. HR leaders can mitigate resistance and enhance engagement by fostering a culture of transparency and inclusivity.

3. Identify and prioritize technical and people skills

As AI technologies become integral to business operations, HR leaders must identify and prioritize the necessary technical and people skills to ensure effective AI integration. This dual focus will enable organizations to leverage AI tools effectively while enhancing human capabilities that drive innovation and collaboration.

  • Determining Technical Proficiencies: Identify the technical skills employees need to leverage AI tools effectively. This includes competencies in data analysis, machine learning, programming, AI tool utilization, and cybersecurity. Clearly define the proficiency levels needed for various roles and create a roadmap for acquiring these skills across the organization.
  • Enhancing Human Skills: In addition to technical proficiencies, prioritize the development of essential human skills. Skills such as collaboration, critical thinking, problem-solving, creativity, effective communication, data literacy, and synthesizing information are crucial in an AI-driven environment. These skills complement technical capabilities and are vital for maximizing the benefits of AI.
  • Identifying Skills Gaps and Training Needs: Conduct a thorough assessment to pinpoint existing skills gaps hindering AI implementation. Evaluate employees' current skill levels and determine where additional training is needed. Develop targeted training programs to address these gaps, focusing on technical and human skills. This approach ensures that employees are well-prepared to work alongside AI technologies.
  • Developing Targeted Training Programs: Design and implement training programs tailored to the organization's different roles and skill levels. For technical skills, provide courses in data analysis, machine learning, AI tool utilization, and programming. For human skills, offer workshops and development programs that enhance collaboration, critical thinking, communication, and data literacy. Utilize a mix of in-person training, online courses, and hands-on workshops to cater to diverse learning preferences.

4. Restructure enterprise career architecture using a skills-based approach

HR leaders must proactively reshape career development and organizational structures to align with the new blend of technical and human skills essential for sustaining business processes. This shift leads to a skills-based approach to career architecture, ensuring that employees are prepared and empowered to thrive in an AI-augmented environment.

  • Redefining Career Paths: Traditional career paths must be reimagined to accommodate the skills and roles required by AI integration. Develop new career pathways incorporating technical and human skills, emphasizing continuous learning and adaptability. Ensure these pathways provide clear progression routes that reflect the organization's evolving needs.
  • Revising Job Roles and Responsibilities: Update job descriptions to reflect AI's impact on roles and responsibilities. Identify and define new roles that emerge from AI capabilities, such as AI specialists, prompt engineers, data analysts, and machine learning engineers. Ensure existing roles are adapted to integrate AI tools and technologies, highlighting the necessary skill sets and competencies. 
  • Aligning Career Architecture with Strategic Goals: Ensure that the restructured career architecture aligns with the organization’s strategic objectives and AI implementation plans. Review and adjust career development strategies regularly to reflect changing business priorities and technological advancements. 
  • Creating Agile Organizational Structures: Restructure the organization to be more agile and responsive to the dynamic AI landscape. Implement flexible team structures and cross-functional collaboration to facilitate innovation and rapid adaptation. Encourage interdisciplinary teams that combine diverse skill sets to address complex challenges and drive AI initiatives.

5. Revise and enhance HR metrics

Revising and enhancing HR metrics is crucial for the successful measurement of the impact of AI on business outcomes and employee experiences. By identifying key performance indicators (KPIs) and leveraging AI-driven analytics, HR can gain deeper insights and make more informed decisions. Here’s how HR leaders can approach this task:

Identifying AI-augmented KPIs and metrics

  • Productivity Metrics: Measure employee productivity and efficiency changes due to AI integration. Track metrics such as task completion times, output quality, and process automation rates.
  • Engagement Metrics: Assess employee engagement levels using AI tools that analyze feedback, survey responses, and sentiment data from internal communications.
  • Recruitment Metrics: Use AI to enhance metrics like time-to-hire, cost-per-hire, and candidate quality, ensuring the recruitment process is efficient and effective.
  • Performance Metrics: Implement AI to monitor performance metrics such as goal achievement rates, performance review scores, and project completion rates, providing a more comprehensive view of employee performance.

Monitoring and evaluating progress

  • Training Program Effectiveness: Regularly assess the effectiveness of training programs designed to upskill employees for AI readiness. Use AI-driven analytics to track participation rates, completion rates, and post-training performance improvements.
  • Skill Acquisition: Measure progress in acquiring necessary technical and human skills through pre-and post-training assessments, employee self-assessments, and manager evaluations.
  • Adaptability and Learning Agility: Evaluate employees’ ability to adapt to new AI tools and processes. Monitor metrics related to the speed and ease with which employees learn and apply new skills.

Enhancing reporting and data visualization:

  • Improved Dashboards: Develop new dashboards that provide real-time visualizations of key HR metrics. These dashboards should be accessible to HR leaders and other stakeholders, enabling data-driven decision-making.
  • Insightful Reporting: Generate regular reports highlighting trends, insights, and actionable recommendations based on HR metrics. Use these reports to guide strategic planning and continuous improvement efforts.

The integration of AI into business processes offers significant opportunities for organizational optimization. However, it requires careful planning, collaboration, and foresight from various stakeholders. By addressing regulatory concerns, enhancing skill sets, and aligning AI implementation with strategic objectives, HR leaders can position their organizations for success in the era of GenAI. With the genie now out of the bottle, HR must provide the essential guidance to navigate an evolving human-machine workplace.

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