This week, I had the opportunity to participate in several AI conferences, where we discussed the influence of AI on labor strategies. The application of AI is advancing rapidly, encompassing both futuristic and tactical use cases. A futuristic example comes from Igor Grossmann at the University of Waterloo, who is developing models with backstory capabilities to simulate human responses, potentially replacing focus groups accurately. On the tactical side, we see the implementation of Generative AI in company HR portals, making it easier for HR leaders to search and manage documents from a knowledge management perspective. Such an implementation saves a lot of use cases.
One of my observations is that the areas where extensive interactions occur between Business and HR can potentially be greatly enhanced through the implementation of AI. HR, as a function, serves various functions within the enterprise. Streamlining processes related to policy clarification, training plans, compliance inquiries, and similar interactions can be effectively optimized with Generative AI. This, in turn, liberates HR’s capacity to focus on more advanced initiatives in skills development and personnel growth. Adopting an interaction-centric perspective can prove to be a valuable approach.
Many leaders have suggested considering Gen AI use cases from two distinct angles: Revenue Generation and Efficiency Enhancement. This approach serves as a straightforward framework for applying Generative AI. “An uncomplicated way to boost revenue involves incorporating Gen AI into the company’s website, enhancing user search capabilities, and facilitating better product and solution matches. Some companies have reported around a 1% revenue increase (though it’s still in the early stages). On the efficiency front, deploying a development copilot could yield up to a 15% efficiency gain.
Additionally, there is a potentially intriguing and unconventional use case worth exploring. Can a traditionally cost-centered function like Workforce Planning evolve into a revenue-generating function? Imagine constructing a location selection model with unique parameters tailored to your industry and offering access to this model (not the data) to peers for a select fee. Such futuristic marketplaces are likely to emerge in the near future. This approach not only improves efficiency in model development but also provides the budget to create newer and more innovative models.
I also had a chance to review the working paper by Brynjolfsson, Erik, Li, Danielle, & Raymond, Lindsey R. (Working Paper 31161). “Generative AI at Work.” From National Bureau of Economic Research. This paper states that AI-assisted call center agents are 14% more efficient. The AI assistant improved the performance of less skilled or less experienced workers across all productivity measures. Agents with two months of tenure who used the tool were able to perform as well as agents with six months of tenure who didn’t have access to the AI.
From a workforce and labor planning perspective, the following are the implications we have to consider urgently:
- We can do more with early career talent using Gen AI
- We should revisit the experience requirements in job descriptions
- Certain jobs like Data Engineering may become critical and bottlenecks if we do not plan well
- AI will not be an added augmentation; it will be fully integrated into the jobs
- We have to train the Analysts’ talent pool with newer generative AI skills
- In certain use cases, we are better off starting earlier, but in certain other use cases
- Understanding and developing cost models for gen AI will become a very critical Workforce Planning (Setup costs, Deployment costs, and Yearly run costs)
- Power skills around interpreting and understanding the limitations of data become very critical
- Smaller-sized talent pool ecosystems will become more and more relevant, and the focus will shift to the quality of talent pool
- The connection between enterprises, universities, research institutions, and professors will significantly grow as Enterprises pilot advanced AI use cases
Our report on the European tech ecosystem is going well, and here is a snapshot of the Startup ecosystem.