Navigating the impact of generative AI in the world of work 

In Reinventing Jobs: A 4-step Approach for Applying Automation to Work (HBR Press, 2018) , Jesuthasan and Boudreau demonstrate that companies that lead with the work instead of the technology are better equipped to ensure the optimal combinations of humans and automation. Those companies see where automation can best substitute highly repetitive, rules-based work; where it can augment human creativity, critical thinking, and empathy; and where it can create new human work.

There are four distinct potential outcomes associated with any body of work:

• Error elimination (think of some of the work of an airline pilot), where the consequences of a mistake are high and there is significant potential for negative value to the organisation from any deviation from an acceptable level of performance
• Minimising variance (think transaction-processing work), where there is no value in improving performance beyond a target level
• Improving productivity (think of the work of a salesperson), where an improvement in performance yields a commensurate improvement in value to the organisation
• Achieving breakthroughs (think of highly creative work, such as data science ), where a small improvement in performance has an exponentially large impact on value

Established automation like Robotic Process Automation (RPA) can be helpful in substituting human endeavour in work where the objective is to reduce variance and there is a greater tolerance for risk. Consider the application of RPA to reduce the variance with which the highly repetitive, rules-based work of analysing and synthesising financial data is performed.

AI has long been used to augment analytical work where productivity improvement or achieving a breakthrough is the goal. Consider how oncologists have used machine learning, trained on significant volumes of specific data and images, to exponentially increase the accuracy of cancer detection, not by replacing skills but by augmenting capabilities and increasing the premium on experience and expertise. However, when it comes to error elimination, we often see automation used to reduce the potential for human error initially through augmentation and then through substitution as the specific data sets, logic and new guardrails are developed.

However, generative AI is at a nascent stage and can be prone to error given its lack of underlying logic. This issue is compounded by the fact that when the stakes are high and our tolerance for risk is low, we are more tolerant of human error than we are of machine fallibility. It is essential that leaders understand when to rely on — and when not to rely on — these technologies at this stage of their evolution, along with the specific roles they should have in human work (substitution, augmentation or creation). Generative AI is most useful in democratising knowledge and creativity through augmentation, lowering the skills premiums traditionally required for a variety of creative tasks where the goal is achieving productivity gains and seeking a breakthrough in domains where there is a high tolerance for risk.

Equally important is understanding the consequences of its use for your talent model. Given that many professions are built on an apprenticeship model, how will you resist the temptation to substitute the work of junior-level talent with AI that may eliminate the next generation of creators, leaders and managers?