The skills every IT pro needs in the AI era
There’s no denying that the surge in popularity of artificial intelligence is a game-changer for the workforce. That is especially true for IT workers, who are now expected to have some familiarity with AI, whether their job really demands it or not.
As evidence, consider the findings of the 2025 CIO Sentiment Survey from research firm International Data Corp. Survey participants were asked how important AI knowledge and skills are when evaluating IT job candidates. Nearly two-thirds (65%) said they expect all new IT hires to have a basic level of skill and competency in AI. Another 19% percent said they want new IT hires to have a foundational understanding of AI. Only 14% said they have no AI expectations for new hires unless specifically required by the position.
Clearly, AI skills have become table stakes for nearly all IT jobs. With that in mind, Spiceworks asked several IT leaders what the skills are that every IT worker should have in the new age of AI.
A skills game-changer unlike any IT has seen before
“As a CIO, I recognize we are witnessing the most game-changing cycle in history, with the fastest adoption of technologies across both consumer and business in history. It is indeed creating a clear skills gap,” says Christopher Kraus, CIO at FreshPet. “For us, the pace at which new business cases are emerging is tremendous, yet our workforce is simultaneously trying to build the technical expertise and the more practical skills needed to fully leverage AI inside our business.”
Also, with the democratization of IT adoption itself over the years, where business leadership is stepping more and more into the driver’s seat as it pertains to tech-enablement, Kraus says his organization is focusing on upskilling its workforce. The goal is to not only effectively measure the ROI of each AI use case, but to also collaborate closely with their IT colleagues to implement these solutions to ensure they maintain the proper guardrails for adoption.
Bottom line: “Increasing AI skills is no longer optional for us – it is becoming key for employees at every level to learn new skills and grow into an AI-enabled company,” Kraus says.
The growing skills gap
The rapid adoption of AI is indeed creating a skills gap, but not the traditional kind we might expect.
“Research shows that 78% of organizations now use AI in at least one business function, yet this adoption often lacks strategic depth,” explains Alexander ‘Sasha’ Sidorkin, chief AI officer at Sacramento State University in California. “The gap exists not in technical AI skills alone, but in what I call ‘cognitive orchestration’—the ability to effectively coordinate human intelligence with artificial intelligence.”
Organizations report significant variation in productivity gains from identical AI tools, depending entirely on how employees approach human-AI collaboration. Some teams achieve breakthrough results, while others see minimal impact from the same technology. The differentiating factor is rarely technical sophistication but rather strategic thinking about thinking itself, Sidorkin says.
This creates what economists term ‘orchestration value’—the premium placed on effectively coordinating human and artificial intelligence toward complex goals. The skills gap is fundamentally about learning to think differently about work, not just learning new software.
The good and bad of rapid AI deployment
For IT professionals not directly involved in AI projects, the rapid rollout of AI tools can be a double-edged sword, says Brent Moffatt, an IT manager in public safety and higher education. On one hand, AI has the potential to enhance productivity, automate repetitive tasks, and support decision-making. But for those who aren’t trained or encouraged to use these tools, the benefits often go unrealized.
In many organizations, AI tools are introduced with the assumption that staff will ‘figure it out,’ Moffatt explains. As a result, workers unfamiliar with AI often either ignore these tools entirely or use them in limited, inefficient ways. This not only limits the value the organization can extract from its AI investments, but it also puts these workers at a disadvantage compared to peers who are more comfortable exploring and leveraging emerging technologies.
“This dynamic risk creates an internal skills divide within IT departments—between those who are AI-literate and those who are not,” says Moffatt. “Over time, this could impact job security, advancement opportunities, and overall effectiveness in hybrid IT environments increasingly shaped by AI.”
The impact on IT workers not directly involved with AI
The impact of AI on the tech workforce is profound and unavoidable, even for IT workers not directly assigned to AI platforms. The transformation resembles the shift from manual to digital processes—it changes the entire cognitive landscape of IT work.
AI is becoming embedded in virtually all technology systems that IT professionals manage, from automated security monitoring to infrastructure optimization. Even traditional system administration now involves supervising AI-assisted diagnostics and accepting or rejecting automated recommendations. Network administrators find themselves managing systems that increasingly self-configure through machine learning algorithms.
“More fundamentally, the economics of cognitive labor have shifted,” Sidorkin explains. “Routine troubleshooting, documentation, and even code review are increasingly automated, which means IT professionals must develop higher-level orchestration skills. They need to understand when to trust AI outputs, when to verify them, and how to integrate AI capabilities into existing workflows.”
Going beyond the basics
Basic computer skills aren’t enough anymore for IT workers. To support AI initiatives effectively, IT workers need to understand how data works, be comfortable with automation and scripting, and know how to integrate AI tools into existing systems—often via APIs or cloud platforms.
On the business side, critical thinking and communication are essential, Moffatt says. IT staff must be able to properly process data through AI platforms, interpret AI outputs, explain their relevance to stakeholders, and guide teams through workflow changes. As AI adoption grows, so does the need for ethical awareness, adaptability, and strategic thinking.
“For us, change management is the key skill every IT professional must have, as our team is seen as the core of AI enablement and adoption throughout our company,” Kraus explains. “Beyond technical expertise, we are seen as being able to guide and support the business through responsible use of AI, fostering a data-aware culture that relies on AI, and are continuing to collaborate proactively across functions across many different areas of AI adoption.”
On the technical side, skills in automation, cloud platforms, and cybersecurity measures are increasingly vital to ensure all systems are secure, scalable, and effective. “These all continue to position our IT team not just as technology support, but as a strategic partner who is driving our AI transformation.”
The right competencies for success
The key insight is that success requires not just using AI tools but developing sophisticated judgment about when to trust, when to verify, and how to preserve human agency while leveraging technological capabilities, Sidorkin says. This involves several specific competencies:
Technical skills:
- Prompt engineering excellence: The ability to construct effective prompts for AI systems, iterate based on outputs, and understand the nuances of different AI models
- AI output validation: Systematic approaches to verify AI-generated solutions, including understanding common AI failure modes and bias patterns
- API integration and workflow design: Connecting AI services with existing systems and designing workflows that leverage both human and machine capabilities
- Data pipeline management: Understanding how to prepare, clean, and structure data for AI consumption while maintaining security and privacy standards
Business skills:
- Strategic delegation: Decomposing complex problems into components suitable for human versus machine processing
- Risk assessment: Evaluating when AI-assisted solutions are appropriate for different risk levels—from low-stakes drafting to high-stakes system configurations
- Change management: Helping colleagues adapt to AI-augmented workflows and addressing resistance to technological change
- Ethical decision-making: Navigating the ethical implications of AI implementation, particularly around bias, privacy, and transparency
How to upskill in an AI-first environment
Ironically, one of the best ways to identify skill gaps in an AI-first world is to use the AI itself, Moffatt says. Many AI platforms can guide users through their features, suggest use cases, and even recommend learning paths based on user input. Exploring these tools directly helps IT workers discover what they don’t yet know.
That said, the responsibility shouldn’t fall entirely on the individual. Organizations investing in AI should also invest in structured training programs. Internal courses, hands-on workshops, and clear documentation are essential if staff are expected to use these platforms competently and responsibly. Without that support, even the best AI tools will fall short of their potential.
IT professionals should continuously benchmark their skills against industry standards to find opportunities for continuous growth. Certification tracks from providers such as Amazon Web Services or Azure provide targets on both the technical and business competencies.
“Just as important is staying very active in professional communities, online forums, and associations providing feedback on how AI is evolving — helping people in our industry measure themselves not only against the formal frameworks, but also against real-world practice,” Sidorkin says.
Finally, the IT workers who will thrive are those who embrace change, stay curious, and actively seek to learn. Adaptability and a willingness to explore new tools—and apply them meaningfully within their roles—will be essential as AI becomes more deeply integrated into daily operations.
Just as importantly, success also depends on leadership. Senior managers who understand and support AI adoption can accelerate its impact. Without that buy-in, even the most skilled and forward-thinking employees may find their efforts stalled. In short, thriving in an AI-driven IT landscape requires both individual agility and organizational alignment.
