This week, I had the delightful opportunity to lead a master class at the Amsterdam Intelligence Group Conference.  We discussed the emerging location trends, specifically within Europe, interlinked with Generative AI.  Here’s a summary of the viewpoints I shared: 

• The advent of  Copilot models is fostering greater efficiency in software development.  This dynamic shift casts smaller locations into prominence, making them crucial subjects for evaluation during workforce planning.
• Recognizing and understanding specific skill sets is now central to our endeavors.  This nuanced understanding provides Talent Intelligence and Talent Acquisition (TA) leaders with a golden opportunity to truly comprehend the unique value offered by each talent pool or candidate.
• An in-depth awareness of AI’s role in today’s labor market is indispensable for effective Talent Intelligence and Talent Acquisition strategies.  (Draup can run this master class for your TA and TI professionals.)
• When observing fields like Machine Learning, while the required skills remain consistent, there’s a noticeable shift from classifier models to foundational models.

The audience received our viewpoints well.  Should you be interested in exploring this further, a copy of the presentation is available upon request.  Feel free to let me know if you’d like one.  Here is a snapshot of locations showcased in the report.  (If Europe is not on your planning horizon, we plan to do a similar report for all regions and ensure the same is delivered to you.)

Continuous Research on Gen AI Impact on Labor

In examining the impact of Gen AI, we encountered a noteworthy working paper, “Navigating the Jagged Technological Frontier Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality,” from Harvard Business School, which cites a fascinating phenomenon.  The authors engaged in studies spanning two work categories, delineated as ‘Inside the Frontier’ and ‘Outside the Frontier’ tasks.  Gen AI exhibited commendable performance, particularly when tasks necessitated the analysis of substantial external data assets, such as peer company financial statements, outpacing consultants in this regard.  However, its efficacy slightly diminished when tasks required interpreting datasets with company-specific contexts.  (Example trying to decode a specific business unit among peers)  This discrepancy in performance may stem from the consultants’ ability to integrate a wealth of Context into their approach.  As the paper remains in the developmental stages, conclusions are tentative, pending the release of subsequent editions.  Nonetheless, the findings to date are captivating.  My initial view is in areas of decision-making where Context plays a heavy role; Gen AI may still perform poorly compared to Humans. 

We are developing a cost modeler for Gen AI Talent Initiatives.  The current terminology of tokens is not well understood, and we want to represent the effort in efforts and dollars.  We believe that this will be very helpful for HR leaders.  We will send the complete model when completed, but here is a simple snapshot. 

Talent leaders can select the use case and understand what it would take to develop this.  Would this type of model be helpful?  Let me know as we prioritize the same.