What AI skills do IT pros really need in 2026?
Information technology professionals keeping an eye on ‘help wanted’ ads might be confused on current expectations about AI experience. Some recent headlines have suggested that AI skills are now must-have, while others say it is more of a foundational understanding of AI that counts.
Either way, staffing experts agree that IT pros should get as much exposure to AI strategies as possible, especially as the technology relates to business needs.
“AI skills on the CV are no longer a nice bonus but a must for most roles,” explains Nathan Putsey, talent acquisition manager at Job Leads. “Of course, we don’t expect a marketing candidate to be a machine learning engineer. However, we do expect them to be AI literate when it comes to their job. At the same time, we expect more than ‘a proficient ChatGPT user.’ They need to be able to automate their processes with AI, research and use relevant AI tools, and distinguish high-quality AI output from low-quality one.”
Many firms want IT pros to have AI ‘literacy’
Right now, expectations tend to skew towards AI literacy rather than engineering with AI, agrees Pankaj Khurana, VP of technology and consulting at Rocket and founder of AI sourcing tool Firki. Most organizations seek IT hires who know how to use AI tools effectively. They don’t expect applicants for technical positions to engineer an AI model from the ground up. The underlying assumption is, generally, if you can use an AI tool like previous generations used cloud, you’ll be fine. The most important factor is your level of practical fluency, not the depth of your academic knowledge in AI.
For most IT roles, foundational awareness is the new baseline, explains Travis Lindemoen, CEO of IT group Underdog.io. You don’t need a PhD, but you must understand how AI integrates into your domain.
For a DevOps engineer, that means knowing how to deploy and monitor ML models, Lindemoen says. For a software developer, it’s about leveraging APIs – such as OpenAI or Azure AI – and understanding data structures for AI. Deep expertise requirements are typically reserved for specialized roles such as ML engineers and data scientists. For everyone else, it’s about applied literacy, he explains.
What do employers really want when it comes to AI knowledge?
So when organizations say they want foundational knowledge of AI, what exactly does that mean?
“It really depends on the role and seniority level, but it should be deep enough to know how AI can help assist them in their role and streamline their processes. It should also be deep enough to understand when it’s better to avoid using AI at all,” Putsey says. “Hands-on familiarity instead of theoretical knowledge is, of course, very welcome.”
Khurana has a similar view: “The expectation is not to build a model from scratch — it’s more like applied AI competency. Engineers and other IT hires are expected to know how to use AI to automate repetitive tasks, improve documentation, streamline troubleshooting, summarize logs, generate configurations, assist in coding, and optimize workflows. The depth varies, but most organizations are not asking for AI researchers. They want engineers who can work smarter because of AI, not despite it.”
How ‘deep’ is the expectation for required AI knowledge or skills?
One staffing expert who believes we’ve gone too far in the requirement of AI skills is Ben Lamarche, general manager at Cambridge, MA-based Lock Search Group.
“A point I’ve been making a lot lately is that we’ve gone overboard on AI requirements,” Lamarche says. “Nearly every role that crosses my desk demands ‘AI literacy.’ It has become a branding exercise for employers who want to appear cutting-edge – but at what cost? As a recruiter, I see talent pools shrinking, often for no good reason at all. Many of these roles don’t benefit from AI integration, or use simple tools that can easily be learned on the job.”
Even in IT, where you’d expect a higher baseline of technical fluency, the demands of AI skills have ballooned into something counterproductive, Lamarche says. “Hiring managers ask for ‘AI experience’ as if it is a single, coherent skill set instead of a vast, specialized universe. That lets me know they are checking a box — nothing more.”
Before adding that requirement, Lamarche says organizations should first ask themselves a basic question: what exactly is the use case for AI in this specific role?
“If they can’t answer that, they shouldn’t be putting it in the job description,” Lamarche explains. “Broad, undefined expectations don’t produce better hires — but they do create confusion and mask what real competencies matter for the job.”
AI experience expected at different IT career levels
While organizations rarely see cases where lower level or mid-level IT job candidates have led AI initiatives, participation in projects is highly valued, Lindemoen explains. Questions they might ask in an interview include ‘Have you been part of a team that piloted a Gen AI feature? Have you built a RAG? Have you contributed data, tested outputs, or helped integrate an AI service?’
“That’s the gold we are always screening for,” Lindemoen says. “It shows you understand the workflow, the iterative nature, and the challenges – such as hallucination testing or data quality. Fresh grads should have personal or academic projects demonstrating this curiosity.”
Expectations are pragmatic, Lindemoen says. Candidates should be able to:
- Prototype and integrate: Use existing tools and APIs to build simple automations or features – such as adding a smart chat assistant to an app.
- Collaborate effectively: Speak the language. Work alongside data scientists to define feasible requirements and operationalize models.
- Manage and curate data: Understand that AI is data-first. You should know how to access, clean, and structure data for AI use cases.
- Evaluate and test: Critically assess AI outputs for your domain, understanding limitations, bias, and cost.
What an ideal AI-experienced IT job candidate looks like
Sources interviewed for this article were very clear on what they believe an ideal IT job candidate looks like now when it comes to AI knowledge.
Matt Collingwood, founder and managing director at VIQU IT Recruitment, says an ideal IT job candidate in 2026 would have some experience with knowing how to complement their skills with automation. Employers are increasingly placing more importance on soft skills such as communication, stakeholder management and teamwork, as AI takes over repetitive and some technical tasks, he explains. Excelling in soft skills will help them to stand out and be seen as invaluable in the age of AI.
An engineer who is well positioned for 2026 will have a solid base of engineering skills as well as the ability to comfortably work with AI, Khurana explains.
Engineers will look at AI as a partner rather than as a threat, Khurana says. They will be adaptable, curious, and able to rapidly learn new technologies rather than just knowing all of the components in a technology stack off the top of their head.
In the case of a hybrid specialist, Lindemoen says they should have deep experience in one core area – such as security, cloud architecture, or backend development – but be fluent in how AI transforms it.
Putsey says a perfect IT candidate in 2026 should be “T-shaped: strong in their core area – such as backend – and proficient with how AI, data, and infrastructure connect to business outcomes. They should be very comfortable with daily use of AI but have exceptional critical thinking skills to understand what AI output is of high quality. On the soft skills side and something more ‘human,’ I’d mention adaptability, curiosity, and willingness to constantly learn – the shifting AI landscape makes this a must.”
Above all, Putsey advises IT pros not to take AI skills for granted.
“Everything moves so fast that, unless you are constantly learning, your knowledge will be outdated very soon,” Putsey says. “Also, don’t simply list AI-related skills on your CV like every other candidate — be ready to show it. Describe the impact your AI work has brought, give project examples, paste your example automations or tools you created with AI, and think how you can apply this to real business problems. And of course, stay curious and follow all recent developments in AI.”
