Published on October 19, 2023, 12:44 pm

TLDR: Generative AI, a technology that can understand and create natural language, is gaining significant attention and investment. Many CEOs consider it a top priority and organizations are experimenting with its integration into human capital strategies. Generative AI is particularly useful for nonroutine analytical work and creative tasks. However, there is debate about the extent to which generative AI can achieve human creativity. The technology is already being used to automate tasks and enhance productivity in various human capital domains. Ongoing discussion and experimentation are crucial for progress in the field of generative AI.

The emergence of generative AI is marking a significant moment in history. This technology has captured the attention and excitement of millions worldwide. Just five days after its release in November 2022, over 1 million people, myself included, logged on to try out the ChatGPT platform.

The level of investment in generative AI further demonstrates the commitment towards its development. In the first five months of 2023, a staggering $12 billion has been invested in this technology. These figures highlight the immense potential and interest surrounding generative AI.

To gain insights into the current momentum and direction of generative AI, I recently hosted a research webinar exploring its impact on the workplace. With participation from 260 executives representing organizations from Australia, Europe, Japan, and the United States, we discussed their use of generative AI in human capital domains.

During this webinar, it became evident that many CEOs consider generative AI as a top priority. Organizations are actively experimenting with this technology alongside navigating through ambiguity. Artificial intelligence is already being integrated into human capital strategies to augment workforce capabilities.

Similar to the early days of responding to the COVID-19 pandemic and implementing hybrid work models, understanding how to best leverage generative AI poses challenges. It is a learning process marked by ambiguity, experiments, and changes in approach. Both individuals and organizations are spearheading this journey towards effectively utilizing generative AI for nonroutine analytical work.

Generative AI’s ability to comprehend natural language is driving its impact on knowledge work tasks such as hypothesis formation, content creation, medical diagnostics recommendations, and sales pitches. Approximately 25% of knowledge workers’ time involves these creative tasks that generative AI is progressively excelling at.

Nonetheless, like any new technological shift or transformational event like COVID-19’s impact on work dynamics, initial stages tend to be filled with conjecture and speculation. Pushback will emerge alongside adjustments in direction. There will be advocates and critics. It is a process that necessitates numerous experiments before leaders can formulate their own narratives on how best to support workers in the face of complexity and ambiguity.

The potential for generative AI to rapidly influence workplaces is driving attention towards this technology. McKinsey’s report on generative AI, published in June 2023, offers valuable insights into the shrinking timelines for the substitution of human tasks by generative AI. Comparing estimates from these experts with projections made in 2017, it is remarkable how the estimated timelines have contracted significantly.

These accelerated timelines raise questions about how we interpret this information. Drawing from my observations of the pandemic’s impact on work, closely studying current realities and engaging with field experts becomes crucial. During my webinar, I inquired about generative AI’s position on leadership agendas. Over half of attendees (52%) affirmed that it is a CEO priority with ongoing discussions surrounding its impact. Only 13% stated that it has not yet gained significant traction.

Although generative AI sits atop leaders’ agendas, debate surrounding its scope remains active. The subject of machines and creativity emerged as a central theme during the webinar. While McKinsey’s panel of technologists estimated that human-level creativity would be achieved by generative AI between 2025 and 2030, opinions among leaders varied significantly. This discrepancy reflects contrasting perspectives, with technological experts potentially overestimating progress while psychologists like myself may lean towards overestimating human progress.

The lack of consensus regarding whether gen AI will achieve human creativity at the top quartile between 2023 and 2030 was striking—10% strongly agreed, 34% agreed, 22% were neutral, 30% disagreed, and 4% strongly disagreed. Conducting a quick survey revealed diverse thoughts among participants within seconds. Comments highlighted various arguments supporting agreement or disagreement: breaking down creativity into logical steps; leveraging data; emphasizing prompts over AI; drawing parallels with existing creative works like songs; concerns about augmentation rather than substitution of creative individuals; the role of empathy, vulnerability, and originality; and the need for government regulations due to unknowns.

The diversity of opinions serves as a reminder of the richness of conversation surrounding generative AI. Engaging in open debates around this topic is essential for progress.

In terms of experimentation and usage, I was curious about current applications of generative AI. Specifically, my focus was on its impact on the experiences of workers within the human capital realm. McKinsey’s report highlights productivity enhancements resulting from generative AI’s automation across various activities. For knowledge workers, these enhancements involve technology substituting or augmenting tasks such as establishing connections, retrieving stored data, personalizing content, automating activities, enhancing response speed, and facilitating collaboration.

During the webinar, I explored participants’ insights on the current velocity of generative AI usage within three human capital domains: talent development (e.g., recruitment, induction, career management), productivity (e.g., skills training, collaboration management), and change management (e.g., internal knowledge management). The self-selected attendees demonstrated


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