For leaders such as Chief Technology Officers, Chief Data Officers, and Chief Information Officers, embracing generative AI in the workplace brings a wealth of opportunity to drive productivity and transformation. It also means leaders need to consider how to design meaningful career pathways for team members.
The impact of Gen AI on how teams execute their roles depends on a number of factors: Which tasks are being automated or enhanced using generative AI like large language models (LLMs) as co-pilots? Who in the workplace is most affected by these changes and what are the short and long term implications for their careers? Who makes the decisions about how and where generative AI is being used in the workplace, and what ethical considerations are there around responsible Gen AI usage?
In a recent Workera survey, the majority of respondents reported actively using generative AI in their roles – marking a sea change in the way tech teams, in particular, are working and will be working in the future. In a recent survey of its own, McKinsey reports that one-third of all its respondents say their organization is already regularly using generative AI in at least one function. This means that 60 percent of organizations with reported AI adoption are actively using generative AI in their workplace. In addition, 40 percent of those reporting AI adoption at their organization say their companies expect to invest more in AI overall thanks to the potential transformation generative AI can bring, and 28 percent say generative AI use is already on their board’s agenda.
How is generative AI impacting roles?
According to McKinsey, current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. This is a sharp uptick from McKinsey’s own predictions in 2017 that generative AI technology has the potential to automate half of the time employees spend working.
“Gen AI could deliver value equal to an additional $200 billion to $340 billion annually at full implementation levels.”
McKinsey also predicts that banking, high-tech and life sciences are just three of the industries that could see the biggest impact in their revenues from generative AI use in the business. For example, across the banking industry Gen AI could deliver value equal to an additional $200 billion to $340 billion annually at full implementation levels. In retail and consumer packaged goods, the potential impact is also significant – estimated to be around $400 billion to $660 billion a year.
How many organizations are already using generative AI?
We surveyed data practitioners working in a variety of roles such as Machine Learning Engineer, Data Scientist, AI Scientist, Software Engineer, Chief Information Officer, and CEO.
We found that76% of respondents report already using generative AI to enhance their working practices, while 24% are not currently using it. Of these 24% of users not currently using generative AI, 76% reported that they foresee themselves or their organization using GenAI in the workplace in the near future – while 24% of these respondents reported that they do not currently use Gen AI in the workplace, and that they don’t foresee it being used in the near future either.
“Of our respondents who told us they’re not currently using generative AI in the workplace, over 75% told us they will be using it in the near future.”
This sentiment seems reflected across the sectors. TechRepublic reports that 53% of companies they surveyed are “exploring or experimenting” with generative AI technology and 13% of those organizations are working with or optimizing AI models they already have in place. 34% of those TechRepublic surveyed are “formalizing” a generative AI solution, currently moving “from pilot programs to production”.
Get access to our entire survey data to find out which tasks are the most productive when using generative AI, and which tasks are the least productive. Download our FREE ebook, 'A Leader's Guide to Generative AI Skills in the Workplace'. How much more productive are businesses when using generative AI?
In our survey, we asked participants to estimate how their productivity has or will improve when using generative AI as an input source or as a co-pilot.
Respondents were asked to assume a task currently took them 10 hours to complete without any input from generative AI, and then to estimate how long a task does take or would take to complete from zero hours (where generative AI enables the task to be fully automated) to more than ten hours (where generative AI actually increases the amount of time a task would take to complete).
“Respondents who are already using generative AI to help enhance or co-pilot their work reported experiencing an average 168% increase in productivity.”
We found that text-heavy tasks like language translation, text analysis, documentation generation, and email drafting were the four tasks with the biggest increase in productivity. Assuming these tasks would have originally taken 10 hours to complete without generative AI, the respondents who are already using generative AI such as LLMs to help enhance or co-pilot their work reported experiencing an average 168% increase in productivity.
What this means for leaders
According to McKinsey’s updated adoption scenarios, half of the work activities currently happening in organizations “could be automated between 2030 and 2060, with a midpoint in 2045” – a decade earlier than McKinsey’s previous estimates. This is likely accelerating thanks to a potent combination of “technology development, economic feasibility, and diffusion timelines”.
McKinsey surmises that “generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs.” For leaders, this means supporting their teams to upskill rapidly, sustainably, and comprehensively.
Upskillrapidly to ensure the pace of skills keeps up with the pace of technological innovation and team members have a strong foundation on which to build increasingly cutting-edge skills.
Upskillsustainably to ensure an organization is focused on staying in the highest category of learning velocity and team members feel consistently supported. Upskillcomprehensively with solutions designed to generate end-to-end ontologies that constantly assess knowledge levels and address skill gaps, preventing a slowing down in learning velocity.
Read more in our FREE ebook, 'A Leader’s Guide to Generative AI Skills in the Workplace'.
Access the full data we gathered from our survey of how and why organizations are using generative AI right now. Find out which tasks are the most productive when using generative AI, which tasks are the least productive – and how leaders can stop generative AI skill gaps in their teams from derailing business transformation.
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