Quincy Valencia's Analyst Perspectives

The world of human capital management (HCM) technology, and tech in general, is buzzing with excitement over the potential of generative artificial intelligence (GenAI). Startups, especially, are releasing software at seemingly breakneck speed, and larger vendors, specifically the platform providers, have been releasing their own net-new or enhanced features and functionality. We’ve all read that GenAI, and the practical application of large language models (LLMs), are the technological advances of a generation, with the potential to change the landscape in a way that rivals the release of the iPhone. But while enthusiasm is natural, a healthy dose of pragmatism is warranted as vendors and buyers consider their roadmap approach and buying criteria. GenAI models require vast amounts of data, and large organizations are sitting on a goldmine of it, like employee profiles, performance metrics and workforce trends. That data is like rocket fuel for training GenAI models. By tapping into this vast treasure trove, HCM tech vendors can supercharge the accuracy and reliability of AI-generated insights. The diverse and comprehensive nature of the data allows organizations to create AI models that encompass a wide range of scenarios and complexities, enabling more robust and nuanced results. Moreover, the availability of extensive data resources allows for the development of AI models that capture industry-specific knowledge, workflows and compliance requirements. For example, a large multinational organization can leverage its diverse employee data across various countries to create AI systems that understand and adapt to specific regional regulations and cultural nuances. This customization ensures that the AI models are not only accurate but also highly relevant within the organizational context. That said, these vast data sets may inadvertently contain baked-in biases. If not carefully managed, these biases can be perpetuated and amplified in the generated outputs, leading to discriminatory or unfair outcomes. It is essential to proactively address bias during the training and validation phases to ensure ethical and unbiased AI systems. Further, data privacy and security should always be at the forefront in the development of these models, but the sensitivity of much of the data used in HCM applications requires particular care. Organizations must ensure that appropriate data anonymization techniques, encryption, access controls and secure storage protocols are implemented to safeguard sensitive information and protect against potential data breaches or unauthorized access. The other important consideration in building GenAI models is cost. Investments in infrastructure and human expertise aren’t cheap, yet. Most technologies decline in cost over time, often significantly, and some may choose to wait until that happens rather than entering the fray in the near-term. Regardless, a robust ROI analysis is warranted. Along with the immediate market appeal that comes from integrating the latest technology advances into a product, the long-term savings and efficiency gains can often outweigh the initial investment. Where in HCM will GenAI have the most impact? Workforce management, compensation Management, talent management, candidate engagement, payroll, etc. In the fast-paced world of HR tech, being pragmatic may be the best approach. It's about finding that balance between caution and innovation when bringing GenAI into the mix. By taking a careful approach, organizations can test and refine the models before going full throttle. This helps to avoid many of the hiccups that come with shiny new tech. Organizations can conduct pilot projects to assess the effectiveness of GenAI in specific HR functions or use cases, allowing them to identify potential challenges and make necessary adjustments. This phased approach allows organizations to gather valuable insights and build confidence in the technology before scaling it across the HR landscape. Whether vendors choose to upend their HCM roadmaps by going all-in on GenAI or they choose to be cautious in their approach, the most important thing for all HCM tech vendors is to have a stance, be open and forward with it, and demand that vendors are well-versed in the approach and reasons behind it. It is critical to strike the right balance between caution and innovation. GenAI models are here to stay and will continue to evolve rapidly in their use cases. We assert that by 2028, virtually all HCM technology vendors will have integrated generative AI into their offerings to remain competitive and meet evolving buyer requirements. HCM tech vendors have the ability to leverage GenAI to transform their customers’ HR practices, and while there are certainly benefits to being first to market, there is also room for a more reserved approach. I strongly encourage vendors to critically evaluate their stance, conduct a robust cost-benefit analysis before adjusting their product roadmaps, and to ensure they have a well-documented organizational position on the inclusion of GenAI in their software so they are fully equipped with all the information needed to educate their internal and external stakeholders alike. Regards, Quincy Valencia

Quincy Valencia's Analyst Perspectives

Pragmatism for HR Leaders: GenAI and HCM Technology

Re-Humanizing the Candidate Experience

Mitigate Risk During Employment Separation with Onwards HR

Our Analyst Perspective Policy

Our Analysts

David Menninger

Jeff Orr

Keith Dawson

Mark Smith

Matt Aslett

Quincy Valencia

Robert Kugel

Stephen Hurrell

Ventana Research

Subscribe to Email Updates

Posts by Month

Posts by Topic

Analyst Perspectives Archive

About Us

Links