You probably won’t read these lines if you’ve already used GenAI. Instead, you’ll ask an app to summarize it and craft the summary in a written work. As AI tools become familiar, reading and writing may diminish, and human attention to content may shrink. Strategic People Analytics with AI will impact you even more.
Therefore, in a recent keynote to CHROs and HR leaders, I decided to skip the traditional speaking engagement and immediately offered key takeaways at the opening. During the remaining time, I told four stories about the impact of GenAI on HR and people analytics in my entirely human fashion. Hopefully, these stories that I cover here briefly in ascending complexity will give you a clue about what to do next to tame the data beast, i.e., the unstructured data representing your organization’s knowledge.
Distinguish between AI impacts on HR
You can’t avoid stumbling over the word combination AI for HR these days. However, some contents mix automation and augmentation while neglecting the purpose. Next time you encounter associations of AI and HR, ask about the context: Is AI a means, a method, or an objective?
Let me explain this distinction. AI is a means in HR operations that automates repetitive tasks and enables data democratization and consumerization. AI is a method in strategic people analytics that enables the improvement of decision-making by providing actionable insights from significant patterns of employee data. Yet we all head to the future, and AI is a purpose in organizational readiness regarding new skills and roles.
Learn how the means and methods support the objective in your role, and leverage AI in innovative HR-Tech and People Analytics to support the value your organization will create in the age of AI.
Measure and develop data and AI Literacy
Your role in HR touches on these three distinguished impacts of AI. Therefore, data and AI literacy are core skills. Eventually, all HR professionals will “speak data” but with a “different accent” in the AI era because soft skills will become technological skills. Organizational interventions and processes will become new textual data sources that HR practitioners will manage to gain AI-powered insights.
However, the HR sector still lags in data and AI literacy and must develop competencies, such as knowing what types of data exist, what tools and methods are suitable, interpreting outputs and data visualizations, critically evaluating insights from data analysis and AI derivatives, identifying misleading data and AI usage, including biases, and communicating data-driven and AI-powered information.
Your motivation to develop and measure data and AI literacy derives from the abovementioned objective: organizational readiness to create value with AI. You need to know how to connect data on employee behaviors and attitudes to business performance, highlight opportunities and risks, and turn organizational conversations into applied research, which data experts or machines will execute. Learn how to assess data and AI literacy using valid tools backed by academic research.
Become a manager of AI in your future role
Will AI replace you at work? The World Economic Forum researchers analyzed Jobs of Tomorrow, covering over 19,000 tasks across nearly 870 professions to assess risks. Their findings reveal that routine tasks have a high potential for automation, and tasks requiring abstract thinking and problem-solving have a high potential for augmentation. Tasks with lower risk are those requiring personal interaction and collaboration.
I examined the findings from the People Analytics perspective by screening jobs that were similar in tasks. I discovered that jobs with high automation potential are analysts and statistical assistants. Jobs with high augmentation potential relate to research and content editing. Jobs with low automation or augmentation potential relate to education and consulting. New roles relate to AI ethics and governance. I advise you to use this research to understand AI’s impact on your job and start upskilling to integrate AI into your workflows.
Don’t panic if your job includes a large portion of automation and augmentation. An essential skill for working with AI is managerial, even if you don’t manage others. Delegating tasks, communicating, setting expectations, analyzing results, and providing feedback – In the age of AI, everyone will be a manager. Therefore, get familiar with frameworks and AI tools that will enable you to bring this inner manager to life.
Leverage AI to access knowledge as unstructured data
To be able to manage AI tools in my workflow, I try to embrace the idea of designing a dialogue with AI through task analysis (who is suitable for what?), interaction protocols (who alerts whom?), and feedback loops (who measures and evaluates what?).
I am overwhelmed with content in my workflow, which is not exceptional. Like many professionals in trendy and evolving fields, I cover books, podcasts, videos, social media, blogs, journals, webinars, and other resources. To systematically and healthily manage this overload, I created a systematic approach to curate, learn, create, and present content. Various AI tools I integrated into my workflow now leverage my knowledge management. However, the secret sauce of my success includes only two ingredients: every piece of knowledge is unstructured data, and all pieces of knowledge are a network.
In knowledge management, humans and AI tools are interdependent, and they both need data, particularly unstructured data. To understand this interdependency, I advise you to be familiar with some terms: LLMs and RAGs on the machine side, ontology and knowledge graphs on the human side. We can develop a competitive advantage by connecting humans to machines and stream data correctly.
Simply put, while LLMs can predict the suitable words in sentences based on massive content, RAGs support them by bringing relevant information from a specific knowledge base in response to a user’s query. Ontologies and knowledge graphs represent the human in the loop. Respectively, they are like the architectural blueprint of a domain and the blocks that are organized based on that plan.
To guarantee valid and controlled conversations with my knowledge using AI, the main goal now is building the first ontology for the domain of people analytics. I complete this mission these days based on the resources I collected in two decades of professional experience. You will do that too someday in your organization, creating an excellent opportunity to gain a competitive advantage.
The name of the game now is to organize your knowledge as pieces of unstructured data and combine them as a network, so AI solutions will enable you to continue organizing, storing, retrieving, and conversing with your knowledge. By doing so, you’ll enter the future of People Analytics.
Most of the knowledge your organization holds about its people is unstructured. After decades of technology-enabled automation of data analysis, i.e., structured data analysis, we are finally at the rise of a revolution. For the first time, anything is data. Therefore, the knowledge economy as we know it will change, and people analytics will demonstrate such transformation, offering the organization a strategic foundation for informed decisions based on unstructured data.
