Covering Scientific & Technical AI

Last month, we explored how job seekers are responding to labor market uncertainty by investing in new skills, certifications, and hands-on experience with emerging AI tools. Now, a new survey suggests employers are increasingly setting expectations for this trend, rather than simply responding to it. The result: AI skills are quickly becoming a standard part of the job.

A recent survey from automation software provider Zapier finds that 98% of executives now expect employees to have some level of AI proficiency. The finding suggests that AI expertise is no longer confined to dedicated technical roles but is now becoming an assumed component of daily work, even in positions that have not traditionally been associated with software development or data science.

(Graphic courtesy of Zapier)

Rather than hiring only for dedicated AI roles, companies are beginning to expect AI skills across a wider range of positions. While 44% of executives say they plan to hire new AI talent, a larger 65% report plans to train existing employees. At the same time, 33% expect to need external AI consultants, which suggests employers might be setting expectations faster than they can implement them.

So what skills are employers actually looking for? According to the survey, demand spans both technical and non-technical capabilities:

• Generative AI usage and prompt engineering: 67%
• Data management, processing, and analysis: 60%
• Soft skills, including communication, creativity, and problem solving: 47%
• AI deployment and DevOps: 46%
• Project management: 42%

The mix is notable. While generative AI and data skills lead the list, nearly half of respondents also emphasize soft skills and project management, hinting at a growing need for workers who can apply AI in context rather than develop it from scratch. Also, continued demand for areas like data science and DevOps indicates that AI is being layered onto existing technical roles, not wholly displacing them.

Theoretical capability and observed exposure by occupational category (Credit: Anthropic)

Recent research from Anthropic offers a more granular view of how employer expectations around AI are changing the workplace. In “Labor Market Impacts of AI,” the company introduces a measure it calls “observed exposure,” which tracks not just what AI could do in theory, but which tasks are actually being performed with AI in real-world work settings. Using this approach, the report finds that AI adoption remains well below its potential. In many occupations, systems are being applied to only a subset of tasks rather than the full scope of the role. This gap suggests employers are setting expectations based on where AI is headed, not just where it is today. Anthropic also finds no clear increase in unemployment among workers in highly AI-exposed occupations since late 2022, suggesting that AI’s impact has so far been more incremental than disruptive.

At the same time, the Anthropic data points to early changes in hiring patterns that align with the expectations seen in the Zapier survey. Anthropic’s report finds some evidence, though not conclusive, that younger workers are entering AI-exposed roles at lower rates, which may indicate that some entry-level tasks are being absorbed by AI systems. As a result, employers may be setting a higher baseline for new hires, expecting familiarity with AI tools rather than building those skills on the job.

The workforce picture that emerges from these studies is less about job loss and more about a redefinition of what it means to be qualified. AI is not only creating new roles, but is also reshaping expectations for existing ones. Familiarity with AI tools is becoming a prerequisite at hiring, not something developed over time on the job. Routine tasks are increasingly handled by AI systems, but for more complex work, there is more emphasis on judgment, context, and the ability to apply these tools effectively. For employers, the challenge is how to integrate AI into workflows, teams, and hiring practices while navigating the gap between expectation and implementation. For now, the research suggests many organizations are still working through that transition.

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