Navigating AI's Impact on Job Markets: The Emergence of New, Yet-to-Be-Defined Professional Titles Revealed

Navigating AI’s Impact on Job Markets: The Emergence of New, Yet-to-Be-Defined Professional Titles Revealed | ZDNET Insights

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Some worry that artificial intelligence will destroy many existing jobs. That’s the subject of another article. What’s happening on the ground, however, is that AI may be at the core of new roles, especially with organizations pouring increasing amounts of money into the technology, with hopes that it will deliver.

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What AI Means for Jobs and Productivity While most of the focus on the impact of AI on jobs, employment, and the future of labor is focused on the fear of many roles being automated away – and for good reason – there’s far less focus on how AI will make the average worker more efficient and productive and potentially increase economic activity and growth. ZDNET explores both. Read now

It’s hard to predict what job titles will emerge over the next couple of years. After all, nobody could have conceived of titles such as “cloud engineer” or “digital sherpa” a few years back.

We know that data scientists and Python developers are needed to build and maintain AI. Andy Thurai , principal analyst with Constellation Research, suggests we’ll see new types of titles in the next few years as well. These titles may sound whimsical now (and Andy meant them to be), but the underlying roles will be needed for vital tasks in budding AI-driven businesses.

Prompt Whisperer: Coaxes the best output out of AI models by crafting clever prompts
Hallucination Wrangler: Tames the AI model when it starts generating nonsensical or off-topic content
Data DJ: Mixes and matches datasets to create the perfect training recipe for the AI model
Bias Buster: Responsible for identifying and mitigating biases in AI algorithms
Synthetic Sommelier: Curates and recommends the best AI-generated content for specific purposes
Digital Puppet Master: Designs and controls AI characters for various applications
Algorithm Alchemist: Experiments with different AI techniques to create innovative solutions
Neural Network Nanny: Nurtures and trains AI models to reach their full potential
Reality Check Officer: Verifies the accuracy and authenticity of AI-generated content
Chief Creative Catalyst: Oversees and inspires a team of AI-powered creatives
Kill Switch Engineer (or AI Tamer): Starts pulling cables in case AI is trying to take control (this one is the most realistic job!)

Also: What is a Chief AI Officer, and how do you become one?

Across the business technology landscape, industry movers and shakers are seeing demand growing for a range of new roles to tame the AI beast. Maybe not with Thurai’s suggested titles, but charged with the same tasks.

At the leadership level, for example, Anurag Gupta , global head of solutions consulting at Revature, sees AI-related leadership roles as “critical for setting the vision, standards, and roadmap for generative AI projects.”

This may include chief AI officers, Gupta told ZDNET. AI product managers are also coming to the fore, “playing a critical role in helping design, develop, and manage AI-powered products and services.” Also emerging: “The need for generative AI engineers continues to rise as organizations apply new techniques to develop, deploy, and maintain AI solutions that solve real-world problems.”

Also: Tech giants hatch a plan for AI job losses: Reskill 95 million in 10 years

The common thread for many of AI’s new roles can be considered part of the realm of “AI transformation,” Sania Khan , Eightfold AI’s chief economist and head of insights, told ZDNET. These roles are becoming part of “teams entrusted with the critical task of selecting the right AI tools for each function and formulating workforce strategies to ensure agility, productivity, and employee engagement.”

Preparing to assume such roles requires a change in direction in learning and preparing. Hopefully, this includes being part of an organization that encourages continuous learning. “Skills-based organizations that consistently assess in-demand skills needed to future-proof the workforce, invest in upskilling/reskilling workers, and recalibrate roles will remain ahead of the curve,” Khan said.

Unfortunately, many organizations won’t support such learning – and even schools and universities can’t keep up. “Finding formal training or relevant qualifications through traditional channels may prove challenging,” Bernhard Gademann , president of the Institut auf dem Rosenberg, told ZDNET.

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“We suggest that individuals embark on a self-directed learning journey, exploring and applying AI in various scenarios and contexts,” Gademann said. “With the abundance of information published daily, there are ample opportunities to learn and be inspired. However, this path demands a highly self-motivated mindset, which – clearly – is another important future skill.”

With technology changing so fast, the time it takes for a technology skill to become obsolete is now less than three years. Gupta urges getting involved with “hands-on or project-based approaches that offer the opportunity to work on real-world projects and scenarios to master and acquire new skills.” Such skills not only involve generative AI, but also security and cloud, along with foundational skills “such as core programming, no code, low code, data science, business analysis, and QA testing.”