Littal Shemer Haim

People Analytics, HR Data Strategy, Organizational Research – Consultant, Mentor, Speaker, Influencer

LLMs Changed People Analytics

Large language models (LLMs) altered how we work and create value. How can the People Analytics profession leverage these deep learning and natural language processing techniques to offer a better business case for its expertise?
Photography by Littal Shemer Haim ©
(Reading Time: 3 minutes)

There is no turning back: LLMs changed People Analytics. Large language models (LLMs) altered how we work and create value. The spectacular growth of ChatGPT users in a few days was a clear sign that these models are fundamentally changing how people process unstructured data. But how can we, the People Analytics professionals, leverage these deep learning and natural language processing techniques to offer a better business case to our expertise? How can we extend our value proposition to help our organization gain better business results and improve its competitive advantage?

LLMs in People Analytics is a vast and developing topic. Therefore, I cut it into five pieces, so we can easily digest it and get ongoing updates. Each of the five parts explores a different aspect of implementing LLMs in People Analytics practices: Introduction, Advantages, Challenges, Announcements, and Career. Let’s start with a review of these five topics and then dive deeper into each one in a dedicated article. 

Since the following topics ongoing updated, I invite you to come back and check them from time to time. To make it even easier, subscribe for updates and receive a notification straight to your e-mail inbox. But if you prefer a conversation, you can ask my chatbot about the latest updates. I also cover LLMs in People Analytics in new lectures and mentoring activities.


The first part of this series is an introduction to LLMs in People Analytics. While there are tons of resources on this emerging technology, the perspective of the People Analytics domain is relatively scarce. However, we are reasonably dissatisfied with a general description of how LLMs work. So instead, we want to explore how they impact People Analytics practices and tools. After reading Introduction: LLMs Impact on People Analytics, you’ll be able to explain LLMs generally and start a conversation in your organization about the change to come.


This series’s second part covers the advantage of implementing LLMs in People Analytics practices and tools. Although LLMs disrupt the People Analytics profession and industry, the fundamental objectives remain the same. We are here to explore, infer, and communicate data patterns, to support business decisions related to people. Implementing LLMs can enhance our ability to offer data democratization and consumerization. After reading Advantages of LLMs in People Analytics, you’ll be able to discuss the business case for extending your tool kit to include LLMs and review the added value for various stakeholders from better access to information and insights.


The third part of this series discusses the challenges and risks of LLMs for the People Analytics domain. The challenges can be categorized into two main types: The first encompasses ethical concerns, and the second includes all kinds of output limitations. This part is crucial because we, first and foremost, don’t want to harm people and any of our stakeholders with our analytics activities and products. However, we must also understand how to overcome berries in LLMs implementation. After reading Challenges of Implementing LLMs in People Analytics, you’ll have a clearer view of the challenges and risks. Such a view is essential for implementing LLMs responsibly.


The fourth part of this series focuses on People Analytics vendors. We already have some fascinating news from some players in the industry regarding LLMs, and we can certainly expect many more to come as the technology develops. In addition, the creative and innovative way vendors leverage LLMs will enhance their offerings and influence how People Analytics departments operate. After reading LLM News from People Analytics Vendors, you’ll find it easier to continue navigating the rough seas of HR tech and introducing opportunities to your organization to expand further and automate your deliverables.


The last part of this series is about you. The emergence of LLMs will require new considerations for your skillset and career path. LLMs are unlikely to replace human analysts who better understand people’s nuances and complexities and better capture the context and subtleties of communication and cultural factors. Nevertheless, a practical approach to the change is to prepare for a hybrid model that combines the strengths of LLMs and human analysts. After reading People Analytics Leaders Leverage LLMs, you’ll have some ideas on adjusting your career aspirations and learning path and discuss an alternative mixture of skills in your team.

I’m excited about this ongoing exploration and curation of LLMs in People Analytics. I hope it enables us a smooth entering to the new era. I invite you to discover more about the Introduction, Advantages, Challenges, Announcements, and Career considerations, return later to each part of this series for updates, and also learn more about LLMs in People Analytics in my lectures and mentoring.

Littal Shemer Haim

Littal Shemer Haim

Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people’s lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.

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