Setting the Stage for AI in HR: Can You Say YES to This Critical Question About Your Data?

The buzz around Artificial Intelligence (AI) in the workplace is growing louder by the day. As organizations worldwide attempt to harness this revolutionary technology, particularly in the realm of Human Resources (HR), a fundamental question arises: Is our workforce data truly ready for AI and Machine Learning (AI/ML)?

The Reality of Data Readiness for AI and ML

In our modern business environment, HR teams are making use of workforce data for a variety of purposes. Traditionally, these teams had focused on extracting data for reporting in the form of monthly extracts or daily snapshots. This approach, while useful for traditional needs, falls short of the data needs for AI and ML. 

That’s because data preparation for AI isn’t just about collecting and storing data to review later; it's about curating data in the right way to effectively train sophisticated models.

AI tools today are highly complex and capable of predicting patterns with remarkable accuracy. However, vast amounts of high-quality, curated data are required to effectively train those models. The quality and relevance of the data are critical for the fine-tuning needed for specific tasks or domains like our use cases in HR.

The Need for a Paradigm Shift

From this perspective, most HR datasets and HR data stores that we had previously prepared are not ready for AI and ML (whether it's generative AI or "traditional" predictive AI).

Without appropriately prepared training data, the algorithms we hope to launch will fall short in their learning. Potential benefits of AI in HR—from recruitment optimization to workforce alignment with business goals—could remain untapped or, worse, lead to unintended consequences if models are trained on poor or incorrect data.

Preparing your HR team for this new phase of work isn’t just about adopting new technologies; it's a paradigm shift in how we think about and handle data. This is even more pivotal in the areas of MLOps and LLM operations when we try to deploy these models at scale in a repeatable fashion.

We’re going to start to hear more about these terms and the operational needs of machine learning in the near term future and it’s HR’s responsibility to stay on top of the nuances in this space.

The First Step: Preparing and Unlocking Your Data

Data extraction is one of the most essential parts of preparing for AI and ML. We address the foundational importance of this step, robust data preparation and management, in our blogpost 5 Tips for Getting Data Extraction Right. It explores in greater detail these 5 action steps:

1. Prioritize and align extracted data with the needs of the business
2. Be thoughtful about what you extract
3. Build the business case to pull more
4. Automate your extractions
5. Extract for data science, not just reporting

The paradigm shift and these tips can help HR teams more effectively and efficiently adopt AI practices that will drive business value and insights.

Why One Model Stands Out in People Analytics AI

The final key in preparing for AI and ML is having the right technology in place to build a fine-tuned model that meets your company’s unique needs.

One of the main reasons I joined the One Model team stems from their foresight and commitment in this area. Due to that investment, we're now the only people analytics vendor with a machine learning platform that runs on a data model tailored to your firm, not just last-minute AI features. This distinction is vital.

And "One Model" isn’t merely about preparing data for AI models; it’s an end-to-end platform encompassing data management, storytelling, model creation, evaluation, deployment, and crucially, audit-ready and transparent tools. Our platform empowers HR teams to manage and deploy customized ML models and MLOps effectively, beyond the traditional scope of data engineering teams.

The dialogue around AI, ML, and MLOps in HR is already in full swing. Staying informed and engaged in this conversation is crucial. If you wish to delve deeper or discuss strategies and insights in this space, I, along with the One Model team, am more than willing to engage. We're keen to hear how your team is navigating the intricate landscape of MLOps in HR.

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