What if the spread of generative AI makes human resources more strategic? It might appear paradoxical and counter intuitive but when analysing 3 impacts of these technologies in the workforce, the role of human resources appears more critical and strategic. If so, what actions to implement to leverage human assets and build an advantage?
First impact: productivity increase through automation
One recurring question on automation is how will it change work: will we need fewer humans and will the tasks be the same? Generative AI is no exception and we saw a lot of discussions supported by projections and research. Four of them illustrate what could happen.
A paper written by OpenAI researchers estimated that 80% of the U.S. workforce could have at least 10% of their work tasks affected by L.L.M.s and that 19% of workers might see at least 50% of their tasks impacted.
A Goldman Sachs paper suggested that generative AI could improve productivity in the US by 1.5 to 2.9% over a 10-year period. Generative AI will be disruptive to jobs: “We find that roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work“.
Brynjolfsson, Raymond, and Li show in a new study that generative AI has a considerable impact on labor productivity in specific jobs. According to the study, generative AI can increase productivity by 14% and improve the performance of lower-performing staff. The system shortens the learning curve, allowing employees to get six months of expertise in only two months.
It also verifies what Noy and Zhang’s study showed in early March: AI has the greatest positive influence on the lowest-performing personnel. ChatGPT substantially raises average productivity: time taken decreases by 0.8 SDs and output quality rises by 0.4 SDs. Inequality between workers decreases, as ChatGPT compresses the productivity distribution by benefiting low-ability workers more.
Second impact: increased quality with augmentation
Beyond purely substituting, generative AI tools may augment human work in the following four dimensions.
Third impact: higher criticality of purely human tasks
Although some tasks are to be automated and others are to be augmented by generative AI, some will remain purely human in three dimensions.
Motivation and Leadership: While generative AI can support decision-making and problem-solving, the human element of motivation and leadership remains an exclusively human domain. Leaders provide more than just directions – they inspire, motivate, and establish a collective vision. AI, despite its vast capabilities, lacks the ability to empathize, inspire, and connect with people on an emotional level. True leadership often requires a deep understanding of human motivations and emotions, the creation of an inspiring vision, and the ability to drive others towards shared goals. These are subtle arts that cannot be replicated by algorithms.
Engaging and Developing Cooperations with Internal and External Ecosystems: The essence of cooperation lies in the personal relationships that people develop with each other. This is particularly true when it comes to creating alliances and partnerships, both within an organization and externally with partners, customers, and stakeholders. Generative AI can support these endeavors by providing data and insights, but it cannot replace the human ability to negotiate, persuade, build trust, and develop relationships. The nuances of cultural sensitivity, emotional intelligence, and interpersonal skills that are essential to effective cooperation remain in the realm of human capability.
Start and Stop Conflicts: Conflict management is a complex process that often relies on understanding human emotions, interpersonal dynamics, and cultural nuances. Generative AI can provide objective data and analyses, but it cannot understand the subtleties of human interaction that often underpin conflicts. The ability to de-escalate situations, mediate disagreements, and negotiate solutions requires empathy, intuition, and emotional intelligence – attributes that AI does not possess. Similarly, the initiation of conflict, often a strategic move to challenge the status quo or drive change, requires an understanding of human dynamics and potential outcomes that goes beyond what AI can predict. Thus, conflict initiation and resolution remain deeply human tasks.
3 actions to leverage human assets
With the three impacts in mind, three streams of actions can be identified to leverage human assets and use them to build an advantage.
Reinforce technical expertise in generative AI and train staff on generative AI technologies: recruiting specialists and engaging in partnerships to use off-the-shelves solutions and to be able to test and scale proprietary ones. This all along the generative AI value chain (data generation, data quality, data engineering, modelling and analysis). Additionally, in nearly all functions, generative AI opens possibilities for automation and augmentation. To be seized, domain experts need to be trained to master them (how they work, what are their limits and how to use them).
Engage in process transformation: we saw from past experiences of automation that productivity increases only when processes change. It starts by striking the balance between automation, augmentation and pure human tasks. It then requires redesigning processes and structure. This transformation imperative is even higher in a context where generative AI-first companies compete with incumbents and where clients are used to the experience of ChatGPT and similar tools. Even more, a few years from now, we will have generative-AI-native workers entering the job market. Their expectations and work habits will for sure be very different.
Reinforce soft and leadership expertise and skills: the productivity increase may challenge the current view on key performance indicators, how they are built and their scale. When it’s faster to write emails or lines of code and when it’s of better quality, organizations must modify their performance measurement, remuneration structures, and workforce management. As AI takes on more knowledge transfer duties, middle managers may need to reassess their positions. As soft and leadership skills are becoming more critical, investment in those is strategic. Similarly to what we describe for technical expertise, it would mean both recruiting experts as well as training even more intensively than currently staff on those skills.
Deciding what to entrust to AI is deciding what to leave to humans. It’s a strategic choice. Value is captured transforming the organization and combining technological and human assets. The manager’s role does not fundamentally change, but the purely human dimensions become more critical and new technical skills need to be developed.