Generative AI has been leading the charge in AI development over the last year. Large language models (LLMs) like OpenAI’s ChatGPT and text-to-image models like Stability AI’s Stable Diffusion are changing how workers develop strategies, ideate creative assets, and write code. As more and more companies clamor to adopt AI strategies and start implementing AI tools and technologies in their products, services, and operations, the role of the knowledge worker is also changing.
In our 2023 State of Data Science survey, 40% of respondents indicated their companies are working on internal generative AI tools and technologies; however, the majority of workers are also worried about losing their jobs. In our survey, 65% of IT workers and 45% of data science practitioners stated they felt the rise of generative AI threatened their jobs. Interestingly, the majority of respondents also had upskilling pathways at work. This imbalance is worth investigating.
While upskilling seems to be common among our survey respondents, it’s not a guarantee. With the pace of innovation outstripping traditional education models, online learning resources, webinars, and conferences can help bridge the gap between classroom learning and on-the-job skill building. Recently, we enhanced Anaconda Learning by making it accessible in Anaconda Notebooks, allowing users to learn in the same platform where they work. Easy access to learning resources that focus on specific skills may be the answer to helping workers upskill to meet the requirements of emerging job titles and changes in organizational needs.
In fact, our survey respondents stated that they learn about new tools and technologies mostly through reading and consuming video content and paid online courses. Traditional educational institutions were listed as the fourth highest learning approach, out of six responses. The shift to more on-the-job learning is likely due to the rising pressure of businesses wanting to use AI. With about 68% of companies building with AI and 40% of projects focused on generative AI specifically, it’s no surprise that the pressure to create business value for and with data has led to a need for further learning and adaptable skills.
When it comes to how workers are using generative AI, content creation was listed as the first, with data cleaning, visualizations, and analysis coming in second and automation tasks in third. When we look at emerging job titles in response to increased AI usage and the need to mitigate security vulnerabilities, we see AI Data Analyst, AI Engineer, Big Data Engineer, and AI Auditor listed as top postings. Other job titles mentioned by respondents were AI/ML Scientist and Prompt Engineer. These new job titles call for specialized skills and adaptability.
Increasingly, data practitioners need to understand data science, know how to build and deploy machine learning (ML) models, use secure open-source software (OSS) tools for AI projects, and report on those projects to executives.
What’s certain is that there is no clear path forward. As more AI-related job titles emerge and generative AI continues to fundamentally change the way work gets done, adaptability will be the name of the game. Continuous education, using a multitude of resources, along with company-led upskilling may be the only way to combat the threat many workers feel when it comes to generative AI and the changing nature of work.
No matter what, AI can’t replace the creativity, ingenuity, or logical processing power of humans. True, AI can automate tasks that were traditionally done manually, but we’ll still need people to ensure those algorithms are performant, explainable, secure, and improved over time.
To learn more, read our 2023 State of Data Science report, bookmark our blog on OSS security, and explore the raw data for yourself.
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