Embracing GenAI to Transform HR and Lead Organizational Change

The Human Resources profession is about to become, simply, the “Resources” profession. Think about it: Our (not too distant!) future Strategic Workforce Plans will include full-time employees, part-time and gig workers and, yes, robots and bots. This is not a dystopian perspective. In fact, I am a huge believer that HR professionals have a great opportunity to significantly increase our impact through Generative AI (GenAI) tools. This technology has the potential to transform our work at three levels: enhancing individual performance, improving HR processes, and as leaders of organizational change.

Enhancing Individual Performance

You are likely already using GenAI tools as a powerful tool to boost personal productivity and effectiveness. How many of the below uses (or perhaps you have other examples to contribute in the comments section) are you currently applying to improve your professional (and even personal) productivity:

• Writing Assistance: Use GenAI to draft and refine emails, reports, and PowerPoint presentations.

• Data Analysis: Employ GenAI for deeper insights in people analytics, identifying trends in employee data and generating graphs and charts.

• Learning Support: Create personalized learning paths and generate quizzes for skill development. 

• Report Interpretation: Quickly access and understand complex reports and produce summaries of meetings.

• Interview Preparation: Generate an analysis of a specific CV and generate relevant interview questions based on job requirements.

• Generate ideas for a specific project 

• Self-coaching (See last week’s blog)

By using GenAI as a work aide, HR professionals can focus more on strategic initiatives and high-value tasks.

Improving HR Processes

GenAI can streamline and enhance every HR function, although not all of them equally (at least not yet!). Here are some examples (again, please list in the comments section which other areas of HR have benefitted from GenAI tools in your organization):

Recruitment:

• Write compelling job posts

• Implement AI-powered resume screening to efficiently identify qualified candidates.

• Use chatbots for initial candidate interactions and scheduling.

• Analyze video interviews to assess soft skills and cultural fit.

• Prepare interview questions and evaluation criteria for each of the interviewees (or for each area to be assessed)

• Preparing onboarding plans and having a chatbot to help new employees get their questions answered as they onboard

There are already several specialized AI tools in the market for resume screening and video analysis. Or, if you have the expertise, you can consider develop in-house chatbots using large language models for more flexibility. In either case, there can be considerable gains in quality and productivity by implementing GenAI modes to improve the whole recruiting process.

Compensation:

• Analyze market trends and internal data for fair compensation recommendations.

• Generate personalized total rewards statements.

• Prepare job offers

• Aid in gender pay equity reporting

• Create rewards dashboards (e.g., number of high performers paid below a certain compa-ratio)

In the case of rewards, currently it may be best to develop in-house solutions using secure, on-premises AI models to ensure data privacy and customization.

Learning and Development:

• Personalized Learning Paths: Use AI to analyze each employee's skills, performance data, and career aspirations to create tailored learning journeys. The system can continuously adapt recommendations based on progress and changing organizational needs.

• Content Creation: Employ GenAI to rapidly generate diverse learning materials such as course outlines, quizzes, case studies, and even interactive scenarios. This can significantly reduce the time and resources needed for content development.

• Virtual Coaching: Implement AI-powered virtual coaches that can provide on-demand guidance, answer questions, and offer performance improvement suggestions based on an employee's specific role and challenges.

• Skill Gap Analysis: Utilize GenAI to continuously assess the organization's skill inventory against future needs, identifying emerging skill gaps and suggesting proactive learning interventions.

A hybrid approach may be effective in the case of L&D. Your next Learning Management System (LMS) should come with AI capabilities for core functionalities. You can then supplement it with in-house developed GenAI tools for content creation and personalization to allow for greater customization to your organization.

People Analytics:

• Predictive Attrition Models: Develop models to predict employee turnover by analyzing several potential hypotheses including performance data, engagement survey results, communication patterns, and external labor market trends.

• Organizational Network Analysis: Analyze communication patterns, collaboration tools usage, and project data to map informal networks within the organization, identifying key influencers and potential silos.

• Performance Driver Identification: Mine data from multiple sources (e.g., employee surveys, performance ratings, productivity metrics) to uncover non-obvious factors that drive high performance in different roles and departments.

• Real-time Sentiment Analysis: Implement AI-powered tools that can analyze internal communications, survey responses, and even facial expressions in video meetings to provide a continuous pulse on employee sentiment and engagement.

• Dashboards: The most straight-forward use of GenAI for HR analytics is the development and creation of dashboards for communication with the line about HR data. GenAI can also help interpret these dashboards, thus generating possible insights and recommendations, which is the real aim of people analytics

As people analytics requires a greater deal of data management to feed the models, and as the possible uses are varied, best to rely on commercially available packages. However, keep in mind that there is considerable data management required, given the sensitive nature of the data required, to ensure data privacy and availability, even beyond HR data (e.g., sales data or business data).

As HR starts to work on the implementation of GenAI tools, it is important to create policies to address several challenges:

• Data Privacy: Implement strict, centralized data governance policies.

• Hallucinations: Maintain human oversight for all AI-generated content and implement a mandatory "human-in-the-loop" approach.

• Data Privacy: Implement stringent anonymization techniques and ensure all analytics comply with data protection regulations.

• Bias Mitigation: Regularly audit AI models for potential biases and implement fairness constraints in the algorithms.

• Interpretability: Ensure that AI-generated insights can be explained in clear, non-technical terms to stakeholders.

• Ensure that all models and the AI-generated content is regularly reviewed for accuracy and relevance.

• Implement mechanisms for employees to provide feedback on AI-suggested learning paths to maintain a human touch in development planning.

GenAI generated content cannot replace human judgment. The most effective implementations will combine the power of AI with the contextual understanding and empathy of HR professionals.

By doing so, HR can deliver more personalized, data-driven, and impactful services across the employee lifecycle.

Leading Organizational Change

We have a brilliant opportunity to lead the GenAI transformation across the entire organization. As CEOs continue to seek ways to increase productivity, customer service and speed to market via GenAI, we can be on the forefront of the change that will be required across the organization. Here are some of the areas we should already be doing, some of which in themselves can be aided by AI: 

• Skill Mapping and Reskilling: Identify skill gaps and create targeted reskilling programs.

• Change Management: Develop personalized communication and sentiment tracking.

• Workforce Planning: Model various scenarios including full-time employees, gig workers, robots, and AI solutions.

• Plan the redeployment of talent as needed

• Ethical AI Governance: Lead the development of responsible AI use policies.

The Future of HR: Towards Resource Management

With GenAI, our function is inevitably evolving from “Human” Resources into a more comprehensive "Resources” Management role that integrates all facets of how work gets done, from people to technology. This shift requires new competencies:

• AI Literacy: Understanding AI capabilities and limitations.

• Ethical AI Management: Ensuring fair and responsible AI use.

• Human-AI Collaboration: Facilitating effective teamwork between humans, robots and AI systems.

• Continuous Learning Facilitation: Fostering a culture of ongoing adaptation.

• Change management at a new scale  

By embracing this transformation, and doing it early, HR can position itself as a strategic driver of innovation. We can shape the future of work, balancing technological advancement with human values and potential.

The integration of GenAI presents an exciting path forward for HR. By leveraging these tools at individual, functional, and organizational levels, we can enhance our strategic impact and lead our organizations into a new era of work. Let's embrace this opportunity to evolve our profession and drive meaningful change.