Use of AI moving from niche tech projects into the core operations of organizations across all industries, creating a surge in demand for talent skilled at harnessing AI’s potential.
A report from Bain & Co. found 44% of corporate leaders say limited in-house expertise has slowed AI adoption, with demand for AI skills rising 21% annually since 2019 and the shortage expected to last at least two more years.
A ManpowerGroup survey revealed seven in 10 US organizations are struggling to fill roles amid ongoing digital transformation, a challenge intensified by generative AI.
Roles such as AI cloud engineers and AI product managers are now essential for designing, deploying, and translating AI capabilities into real business outcomes. Breaking in requires a blend of technical skills—Python, statistics, MLOps, and cloud platforms—paired with business insight, strategic thinking, and clear communication.
For IT professionals, the road to an AI career starts with building on existing strengths, gaining AI fluency, and proving value through hands-on projects that show measurable impact.
Emerging careers in artificial intelligence
Paul Farnsworth, President of Dice, says companies no longer want people who can just build models; they need professionals who can apply AI to solve real-world business challenges.
“Roles that bridge the gap between tech and strategy are taking off, especially in industries like healthcare, finance, and manufacturing that are leaning into AI hard,” he says.
Farnsworth points to growing demand for roles such as AI product managers, automation specialists, and AI implementation specialists. For IT workers, he stresses that many transferable skills are already in place, and curiosity about how AI is being used in their industry is the first step to moving onto a more AI-focused career path.
“Anywhere data is collected, there’s potential for AI and a need for skilled talent,” says Iris Safaka, AI engineering lead at Ontinue.
She highlights the importance of AI cloud engineers, who design, deploy, and optimize AI workloads on platforms like AWS, Azure, and Google Cloud, as well as AI product managers, who translate AI capabilities into business value.
Unlike traditional PMs, AI product managers oversee probabilistic systems that change as models are retrained, requiring comfort with experimentation, uncertainty, and ethical considerations.
Technical skills and business acumen
Igor Beninca, Data Science Manager at Indicium, emphasizes that success in AI requires both technical and strategic expertise.
“On the technical side, a solid foundation in statistics, data structures, and a primary language like Python is the price of entry,” he says. Beyond that, proficiency with major cloud AI platforms and MLOps practices is essential to transform models into scalable, repeatable business assets.
On the non-technical side, Beninca says business acumen, strategic thinking, and communication are just as important. AI professionals must be able to link model performance to business outcomes and explain risks and opportunities to both engineering teams and executives.
“Storytelling and communication are indispensable,” he notes.
That means tech pros must articulate the value and risks of a complex AI initiative to the C-suite as clearly as they can to the engineering team.
Mid-career transitions into AI
For professionals looking to pivot mid-career, targeted upskilling and hands-on projects are key. Certifications in AI cloud development from AWS, Azure, or Google Cloud provide exposure to infrastructure environments that power modern AI.
Open-source frameworks like Hugging Face, LlamaIndex, and FAISS allow practitioners to build personal projects and gain real-world experience. Adopting a “fail-fast” prototyping mindset helps validate ideas quickly and avoid costly investment in unscalable solutions.
Safaka notes that AI engineer and AI QA engineer roles are natural transitions for IT professionals, leveraging backgrounds in infrastructure, security, and systems integration.
Farnsworth adds that most IT workers already have a foundation in data and systems, making AI literacy the next logical layer.
“Start by learning how AI connects to what you already do, whether that’s automating workflows or managing cloud environments,” he says. “Use cases come first—tools second.”
Building experience and portfolios
While certifications and bootcamps create a baseline of knowledge, employers ultimately want proof of practical ability. Beninca argues that portfolios showcasing successful projects demonstrate problem-solving, adaptability, and resilience under real-world conditions.
“A certification might get you an interview, but a portfolio demonstrating real-world impact is what gets you the job,” he says.
Farnsworth says he agrees, noting employers want to see what you’ve built, not just what you’ve studied.
“No matter what you’re working on, make sure to show your work and build a portfolio of your achievements,” he says.
The AI career roadmap ahead
The growing market for careers in AI and machine learning job roles is expected to expand as industries integrate AI into more workflows. Success will depend on a combination of technical skills, business acumen, adaptability, and continuous learning.
For IT professionals and aspiring AI specialists, the AI career roadmap is straightforward: start with a solid technical foundation, add business and communication skills, gain certifications, and build hands-on experience.
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