Research Topics

Federated, Centralized, or Decentralized: which structure makes sense, and what roles belong in each? Optimization of budget Optimizing portfolio, etc. Financial mgt of the learning org, how to get really good at doing that Look up Dave Vance - a practical way to justify needing a budget (forensic spend analysis) (financial acumen) what other practices are there to undertstand where is the org spending money and can we optimize that spend to pool dollars

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.

What are the systems of L&D

What skills does L&D need

I'd like to see some research around comparing in-house L&D versus outsourced L&D - Looking at cost, effectiveness, distribution of in house vs outsource across different companies/industries, etc. L&D is getting pressured on cost and spending and those in the executive ranks who may not have as much familiarity with the capabilities of L&D may be looking to reduce cost. This research would provide L&D teams with data to support the most foundational question that executives might have "Could we achieve the same or better outcomes for less cost?"

And how do we mitigate bias when integrating technology where humans used to be -- what NEEDS to be human vs. where can async/tech-based can work.

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?

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?

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?

What does the performance tech landscape look like? What are the trends in the space, and what should organizations be looking for when purchasing tech? This will be our Q3 2025 Tech Consortium webinar.