Research Topics

A long long time ago, in a company far away, we wrote a performance management framework. It talked about all the different components in performance management, how to think through how to use them cohesively together, and how to think about the role of technology as an enabler (not simply an automator) of PM practices. Should we write a new version? One that takes into account hybrid work, AI, generational preferences, and more? What else should we include to update such a framework? Let us know what you think!

One of the challenges in incorporating skills into talent practices is the proliferation of skills data and frameworks (taxonomies / ontologies). This study would dive into how organizations are managing these data to make them talk to each other and tell all stakeholders something useful. We will look into: How are organizations structuring their skills data? How are they managing the multitude of taxonomies / ontologies from various vendors and making them work together? How is the "skills tech stack" structured to enable the effective use of these skills data? Is there a primary source of truth? What is the glue between all of these skills data sets and tie all the data together?

The use of Generative AI in the workplace may level the playing field when it comes to skills. What will be the new category of KSA's needed to succeed?

Everyone's all a buzz about generative AI and its impact on HR Tech. But what is real and what is hyperbole? And what can we realistically expect to see coming soon? We will explore this and more in this report.

How are organizations truly enabling talent mobility, in each of the 5 models we identified in 2021?

As the economy works to find its footing, how are organizations making decisions about what talent is critical, and how to develop that talent?

There are tons of great analytics tech out there, but how do they all fit together? And how might a people analytics practitioner think about making decisions on which type of ecosystem to stitch together?

What are the systems of L&D

Update to Coaching Tech Research - 2021

What skills does L&D need

AI is changing how we do performance management -- but we aren't necessarily convinced it is all good. This report would analyze the ways AI might be used in performance management, and analyze the pros and cons of the approach, using pre-existing research to understand how people might react to this new technology. Readers would learn: Which applications of AI in PM are ok, and which should you potentially stay away from? Which technologies are using AI in PM in which ways and why? How might your organization begin using AI in PM?

Every year, we survey HR and the broader employee population to understand what is happening with performance management. This would be our 2025 edition of this study. In it, we would look to understand: In what ways have perceptions of performance management changed? What is the role of AI in changing how we do performance management? What do employees most need from performance management today? What drives high performance?

As organizations look to both grow and be efficient, leaders are re-examining previous talent management strategies. In this study, we will analyze: What comprises a talent strategy? How do organizations create and measure a talent strategy? Who needs to be involved? What are critical decision points?

AI is popping up everywhere, but we aren't hearing folks talk as much about it within talent management. This paper would aim to fix that, digging into the following questions about AI in TM: What are we seeing? What experiments are being run? What are people thinking about for 2025?