An outlandish and unrealistic AI generated image of a house, demonstrating the risks of using generative AI incorrectly.

No short cuts to success: Senior professionals cannot rely on junior colleagues for AI expertise and need proper training

Managers should not expect junior professionals to be a reliable source of expertise for teaching senior staff how to use generative AI, new research shows.

Younger employees are often valued by more senior colleagues for their willingness to experiment with new technologies and adapt rapidly.

However, research by Professor Hila Lifshitz from the Artificial Intelligence Innovation Network at Warwick Business School reveals that when it comes to generative AI, young professionals are not a reliable source of technical expertise to teach more senior staff tips, tricks, or tactics for using the technology.

The study – conducted with MIT Sloan School of Management, Harvard Business School, Wharton Business School, and Boston Consulting Group – explores the obstacles to senior professionals rapidly improving their AI skills by learning from more junior colleagues.

Junior staff who experiment with AI should not be considered experts

Kate Kellog, Professor of Work and Organisation Studies at MIT Sloan School of Management, said: “When it comes to emerging technologies like generative AI, younger professionals are the ones who dive into experimenting with them first.

“Ultimately, they are looked to by upper managers as sources of expertise, even though they aren’t experts on the new risks that generative AI poses.”

Researchers conducted interviews with a group of junior consultants, who had one to two years of professional experience with little prior knowledge of generative AI.

They were given access to ChatGPT-4 to help solve a business problem, then were asked how using the technology could create challenges for them working with managers.

Junior staff believed managers would be concerned about the risks that generative AI posed to accuracy and explainability. They suggested mitigating these risks by making changes to human routines on a project level, having managers review the prompts that junior staff used as well as their results, and reaching agreements on when generative AI could be used reliably.

Relying on junior staff for AI expertise creates more risks than rewards

However, they were not aware of – and did not suggest mitigating against – keys risks of introducing generative AI tools more widely across a company.

By studying the responses, researchers found three keys reasons that junior staff may not be a reliable source of expertise for more senior colleagues when it comes to generative AI.

  1. Junior employees lack a deep understanding of generative AI’s capabilities.
  2. They focus on human routines rather than system design when mitigating risks.
  3. Junior staff focus on challenges at a project-level, rather than a company-wide level.

Hila Lifshitz, Professor of Management and Head of the Artificial Intelligence Innovation Network, said: “Historically, the main obstacle to senior staff learning about new technology from more junior colleagues was if they felt their status was being threatened.

“Our research shows that generative AI presents a different set of challenges. Juniors who are working at a project level are more likely to focus on the specific risks they encounter.

There are no short cuts for managers when developing generative AI skills

“However, generative AI platforms pull in an extensive amount of data from a much broader ecosystem. That means business leaders and senior professionals need to mitigate the risks at a company-wide level in order to implement generative AI effectively.

“Expecting junior workers to learn to use AI tools through trial and error, then pass their tips onto senior colleagues, does not provide managers with the whole picture.”

Instead, the researchers found that senior professionals need to focus on:

  • Making changes to system design to fine-tune the parameters used and visualise uncertainty.
  • Intervening at a company-wide level, such as creating a library of effective prompts for particular tasks and establishing mechanisms to provide feedback and report incidents.
  • Working with developers to specify requirements for the systems’ capabilities and limitations, assess the quality of their data sources, and flag misleading outputs for correction.

Professor Lifshitz said: “Senior professionals need to recognise that there are no shortcuts to learning how to use AI effectively and mitigating the risks. They require proper training.

“They cannot simply follow the lead of junior professionals when adopting AI.”

Further reading:

Beyond the hype: What managers need to ask before adopting AI tools

How companies should use generative AI

Don't expect juniors to teach senior professionals to use Generative AI


Hila Lifshitz is Professor of Management and Director of the Artificial Intelligence Innovation Network. She is also a visiting faculty at Harvard University's Lab for Innovation Science. She teachers Digital Transformation on the Executive MBA and Global Online MBA, and Managing Digital Innovation on the MSc Management of Information Systems and Digital Innovation.

Follow Hila Lifshitz-Assaf on Twitter @H_DigInnovation.

Learn more about digital innovation and AI on the four-day Executive Education course Platform Strategy at WBS London at The Shard.

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