The launch of generative AI represents an era-defining moment. Never before have so many people become so excited by a technology program. Within five days of ChatGPT’s release in November 2022, more than 1 million people (including me) had logged on to try it out. If financial investments are a predictor of growth, then the $12 billion invested in generative AI in the first five months of 2023 shows the depth of commitment.

To get a sense of current momentum and direction, I ran a research webinar last month on the impact of generative AI on the workplace. Some 260 executives from organizations in Australia, Europe, Japan, and the United States joined me to describe their current use of generative AI in human capital domains. What I heard is that generative AI is a top priority for many CEOs, experiments abound alongside ambiguity, and organizations are already beginning to use artificial intelligence to augment their human capital strategies. I’ll share more details about those findings in a moment.

I wanted to hear directly from practitioners because it seems to me that, as with the previous era-defining event — the COVID-19 pandemic — and its impact on the workplace, imagining what is ahead with this new technology is a tough call. As with the emergence of hybrid work and work-from-home initiatives in response to the pandemic, figuring out the right approaches to generative AI is a process replete with ambiguity, experiments, and changes of mind. In other words, it is a learning process driven both by the initiative of individuals and the strategies of organizations.

Like the response to the pandemic, debate and action around gen AI are moving fast.

Two facts are emerging. First, this is a rapidly developing technology. The size of investment gives a sense of this. So, too, does the volume of experimentation: One executive told me, “We have created a head of generative AI with a role simply to moderate and make sense of the hundreds of experiments we have running on any day.” Second, unlike previous workplace technologies, generative AI is not being positioned as a substitute for routine tasks — either analytical tasks, like keeping records or providing repetitive customer-oriented services, or manual tasks, like picking and sorting products or performing routine assembly. Instead, it’s a technology with the potential to hit at the heart of nonroutine analytical work. This is knowledge work, such as forming a hypothesis, creating content, recommending medical diagnostics, or making a sales pitch. The source of this impact is generative AI’s growing capacity to understand natural language. Around 25% of the total work time of knowledge workers is spent on the kinds of creative tasks that generative AI is beginning to get good at.

Yet, like during the early days of the pandemic and hybrid work, much is currently conjecture. There will certainly be pushback, changes of direction, and believers and resisters, just as there has been throughout the ongoing debate about hybrid work. There will be many experiments before leaders begin to develop their own narratives about how best to support their workers in these complex and ambiguous times.

The human capital domain with the greatest reported use of generative AI right now is “internal knowledge management.” Forty-six percent of attendees said they are experimenting in this sphere, with use cases such as creating communications, conducting market research analysis, finding resources and competencies, and engaging in employee listening. Almost as many attendees (44%) reported using gen AI for recruitment, with use cases such as creating job advertisements, developing simple approval flows, and onboarding new hires. About a third of attendees (34%) said they’re using gen AI for skills training, with use cases that include self-directed learning, the creation of learning content, and virtual reality-based training. And 23% said they’re using the technology for assessment and feedback, with use cases such as developing bespoke assessments, improving performance feedback, designing career paths, and creating chatbots for employees.

I feel that what I captured in this research webinar is a sense of the sheer depth of conversation and experimentation taking place right now across the world. How to think about and use generative AI is being debated in the C-suite as use cases are being created and tough questions (like the relationship between machines and creativity) are being debated. For organizations to be aligned with this emerging narrative, it is imperative that they experiment freely and observe closely.