Skills development has long been a staple of IT culture. But the rapid expansion of AI across the sector requires not just upskilling, but a fundamental change in how technology professionals work. Whether you’re expanding your team or charting a new career path, here are seven essential technical skills to succeed in the AI era.
Key Takeaways
- AI is reshaping technical skills. As automation handles more routine tasks, IT professionals must adapt by developing skills in AI engineering, data management, and generative AI fluency.
- Hybrid skill sets drive success. Employers increasingly value professionals who can pair technical expertise (programming, cloud computing, and cybersecurity) with creativity, strategy, and collaboration.
- Continuous upskilling is essential. Staying competitive in 2025 and 2026 requires proactive learning, from mastering AI tools and frameworks to strengthening soft skills that enable effective teamwork with AI systems.
How Does AI Impact Technical Skills?
AI has dramatically reshaped which technical skills are necessary for IT success. One of the biggest shifts has been a change from tactical execution to more strategic, creative, and collaborative capabilities. Some of the biggest trends include:
- Changes in skill demand, as AI-driven automation has replaced many routine and repetitive technical tasks like data entry and simple coding
- Faster problem-solving and greater system optimization, as AI automates large-scale data analysis, routine troubleshooting, and infrastructure monitoring
- Shift toward strategy, creativity, and collaboration, as hybrid roles emerge that require mastery of both conventional IT practices and AI applications
- Growing emphasis on soft skills, as communication, critical thinking, adaptability, and leadership are less susceptible to automation and vital for collaborating with AI-enabled systems
- New skill sets and opportunities, which enables technology professionals to find new niches within which they can thrive (e.g. machine learning engineering, AI-specific cybersecurity, and conversational AI design)
What Technical Skills Are in Demand in 2025?
Although the technology market is constantly in flux, AI is here to stay in some form or another. The productivity and efficiency gains it offers are too significant to ignore. As such, here are some in-demand skills that IT professionals should prioritize in 2025 and into 2026.
1. AI Engineering & Development Skills
There’s a growing need for technologists who can plan, implement, and operate AI systems. We’re seeing growing demand for AI or ML engineers, AI solutions architects, and prompt engineers.
2. AI Literacy & Generative AI
Even for roles that don’t build AI tools or models directly, overall fluency with generative AI and natural language processing (NLP) is a critical technical skill. The more systems AI touches, the more that engineers, developers, and DevOps teams will need to understand how it works.
3. Programming Languages & AI Frameworks
AI is shifting demand for specific programming languages. Specifically, Python remains the dominant language for AI coding, and AI-focused libraries like TensorFlow and PyTorch are necessary for ongoing development, learning, and collaboration.
4. Data Science & Analytics
The potential of any AI tool is downstream of its data (both training and operating). As such, data science and analytics are coveted skills. Specifically, data interpretation and visualization are important for companies looking to derive actionable insights from AI-generated information.
5. Cloud Computing & AI Infrastructure
Although the industry was already trending toward the cloud before AI came onto the scene, the use of cloud environments in scalable AI deployment is accelerating this trend. Technical skills in major cloud platforms like AWS, Azure, and Google Cloud are quickly becoming table stakes, as are cloud native applications like Snowflake, Databricks, and more.
6. Cybersecurity
As AI becomes increasingly integrated into systems, the amount of data that companies need to house and maintain is growing. And the more data a company houses, the more likely they are to be a target of a cybersecurity attack. Thus, cybersecurity expertise will become increasingly critical to protect AI-enabled infrastructure.
7. Human-Centered & Interpersonal Skills (Soft Skills)
AI can handle a good number of tasks. But there are still soft skills that, while not strictly “technical”, are dependencies for realizing business value from AI. Communication, leadership, collaboration, and ethical governance of AI are skills that can help align AI investments with broader business goals.
How to Evaluate Technical Skill Competencies
There are several ways to evaluate technical skill competencies, whether you’re screening candidates, looking upskill employees, or testing your own proficiencies:
- Use AI-driven technical assessment platforms like HackerRank, Codility, Peoplebox.ai, and DevSkiller for structured coding challenges, simulations, and role-specific quizzes
- Conduct real-world simulations, like building an app feature, fixing system failures, or analyzing datasets, to judge practical competence and adaptability
- Combine technical and soft skills evaluations to capture a holistic view of candidate capabilities relevant to technical roles
- Leverage automated or semi-automated technical interviews to simulate onsite coding interviews asynchronously
How to Close Technical Skills Gaps
Once you’ve performed the analysis mentioned above, here are some steps to close those gaps and put yourself (and your team) in the most competitive position in the AI era:
- Perform a skills gap analysis by identifying the difference between current employee skills and those needed for future success.
- Set specific, measurable, and time-bound objectives for closing those gaps, prioritizing them based on strategic business outcomes. For example, if cloud computing is a business priority, focus on closing skill gaps in cloud technologies.
- Build tailored learning and upskilling plans that align with individual employee needs and learning styles. These could include self-directed online courses, instructor-led training, mentorship, and job rotations.
- Provide those seeking to be upskilled with seasoned mentors that can provide personalized guidance, feedback, and confidence-building.
- Identify which skill gaps will be impossible or difficult to close internally, so you can start engaging external IT staffing partners to fill critical roles.
Technical Skills FAQs
What Are Technical Skills?
Technical skills are the specialized knowledge and expertise that enable technology professionals to perform specific tasks. Unlike soft skills, technical competencies can be formally assessed through exams, certifications, or hands-on projects.
What Are Some Examples of Technical Skills?
Technical skills traditionally include knowledge of coding languages, network configuration and admin, database management, OS proficiency, hardware deployment, and more. With the rise of AI, the list of technical skills required for success in the IT sector now include understanding AI models, data engineering, AI proficiency, and more.
Why Do Technical Skills Matter?
For employers, understanding technical skills is important for evaluating potential hires. For job seekers, mastering these skills can provide you with a competitive edge, enhancing employability and access to high-paying roles.
Final Thoughts on Technical Skills
As AI disrupts the technical skills needed for success in the IT fields, upskilling and professional development have never been more critical. This is even more important as more companies adopt skills-based hiring vs. traditional role-based approaches.
But staying on top of rapidly evolving technology trends is a tall order. That’s why it helps to have a staffing partner in your corner who knows the market, can track skill demand, and make sure your job descriptions and compensation plans are up to date.
Contact Capstone to develop a custom staffing plan for AI-based IT roles today.
