A new study conducted by MIT Sloan and Harvard scholars with Boston Consulting Group, a global management consulting firm, goes against the grain of what common literature suggests and explore the obstacles associated with older workers rapidly upskilling themselves from juniors.  

“When it comes to emerging technologies like generative AI, these younger professionals are the ones who dive into experimenting with them first,” said MIT Sloan School of Management professor Kate Kellogg. “They’re ultimately looked to by upper management as being sources of expertise, even though they aren’t experts on the new risks that generative AI poses because of its uncertain capabilities and exponential rate of change.”  

The authors interviewed 78 junior consultants in July-August 2023 who had recently participated in a field experiment that gave them access to generative AI (GPT-4) for a business problem solving task. 

The researchers conducted interviews with a group of junior consultants — associate- or entry- level employees with 1-2 years of experience with little prior experience with using this technology — who were given access to OpenAI’s GPT-4 to help solve a business problem. Consultants were then asked: Can you envision your use of generative AI creating any challenges in your collaboration with managers? If yes, how do you think these challenges could be mitigated?  

Historically, the main obstacle with juniors teaching senior professionals to use new technologies is a threat to status felt by senior workers. However, the study produced a different set of main obstacles that contradict existing research. The three key obstacles that illustrate that juniors may not be reliable in teaching seniors are:  

1. Juniors’ lack of deep understanding of generative AI’s capabilities  
2. Juniors’ focus on mitigating risk through change to human routines rather than system design 
3. Juniors’ focus on interventions at the project-level rather than system deployer- or ecosystem-level

Thus, the researchers suggest moving beyond a focus on local experiments around human-computer interaction, and into a much wider field of context where all risk factors are considered to best mitigate the gap in transitioning to these technologies. Before implementing generative AI practices in the workplace, organizational leaders should mitigate output risks by: 

• Making changes to system design, such as by fine-tuning a model’s parameters based on additional, specialized data, building an interface that visualizes uncertainty, and designing generative AI systems to begin with a prompt to the user to communicate their goals and preferences to the system
• Intervening at the firm level such as by creating a prompt library of effective prompts for particular tasks, monitoring the alignment of LLM vis-à-vis evaluation metrics, and establishing feedback and incident reporting mechanisms
• Intervening at the ecosystem level with developers to specify requirements such as detailing the systems’ capabilities and limitations, providing assessments of the representativeness, robustness, and quality of their data sources, and flagging and correcting misleading output  

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Source: SSRN | MIT Sloan