Nearly three years after ChatGPT launched, AI is still dominating technology discussions and raising questions about the future of IT jobs and the skills needed by the overall workforce. Constant improvements to the tools and underlying models have reduced the likelihood of incorrect results and introduced concepts like agentic AI and reasoning systems. As companies find new use cases for AI in the workplace and integrate AI into their workflows, the stage is set for significant economic disruption and new career possibilities.
For individual workers, AI offers a new palette of options. Routine tasks can be automated, new content can be created, and complex data can be analyzed. With these benefits, though, there is also a challenge: New AI skills are required for the jobs of the future.
The June release of the CompTIA Tech Jobs Report found that employer job postings related to AI were up 117% between 2024 and 2025 in the January-May time period. Clearly, businesses want to boost their AI expertise. While the earliest waves of AI demand focused on software development for building large language models or customizing AI algorithms, the postings are rapidly expanding to include roles in data, cybersecurity, IT infrastructure, tech support, and even non-technical roles. Across the board, workers need to focus on skill-building that will keep them relevant.
Building a foundation with digital fluency
The digital transformation of the workplace has not only increased the importance of digital fluency but also changed the definition. Digital fluency is the ability to apply technology to business problems. Beyond everyday tech skills for beginners such as how to use a smartphone or a spreadsheet, individuals must know how to apply these tools to solve business problems.
For many existing workers, digital fluency was built over time as more and more technology made its way into the workplace. For first-time workers, there is typically a learning curve around translating their digital literacy as consumers into digital fluency as employees. For both groups, the current levels of digital fluency need to be refreshed to emphasize the basics of data management, cybersecurity, and especially AI.
For all varieties of AI usage—from direct interaction with chat to embedded AI within workflow to agents running complex tasks—expertise begins with fundamental knowledge. Understanding the data requirements and the probability-based nature of AI tools is the first step in leveraging this powerful new technology in an evolving workplace.
Each job role needs targeted skills
Digital fluency is table stakes for practically every job role, and the next step is adding those skills that allow an individual to complete day-to-day tasks. Where digital fluency skills may not be specifically highlighted in a job posting because they are an assumed part of the candidate’s skill set, targeted skills are the critical pieces employers are demanding. For a tech support specialist, these may include basic IT skills around hardware and operating system functionality. For a network administrator, the OSI network model and cloud operations are common requirements. For a software developer, proficiency with a specific coding language and familiarity with lifecycle management feature prominently.
Focusing on targeted skills is important because so many established job roles remain in place as AI takes over. Although some specific AI jobs are emerging, the vast majority of employer demand is for AI extensions of core skill sets. It is difficult (if not impossible) to automate or accelerate with AI without knowing how the underlying tasks are meant to function.
Just as AI is filtering into digital fluency, it is becoming a part of the core skill set for any job role. The starting point, though, is not the newness of AI but the established skills needed to successfully perform business operations. Even leaving AI out of the equation, these role-specific skills are going through changes as companies modernize and refine their IT architecture, so learning targeted skills and staying current is the next piece of the puzzle for a long career.
Advanced skills lead to strong career pathways
The final stage of skill growth involves adding advanced skills that extend an employee’s knowledge and effectiveness. In many cases, these advanced skills may be pieces that an employee adds as they consider a new level of responsibility within their current job role or a move into a different job role in another functional area. This stage is where discrete AI skills are most prevalent.
Think about the case of a data analyst who already knows how to gather data from different sources, run queries on the comprehensive data set, and turn the resulting analysis into a visual interpretation to assist with decision-making. This data analyst can learn how AI deals with multiple data sets, how AI can find patterns that may have gone undetected, and how AI can create visualizations. The primary workflow may remain the same, but applying AI can speed up the process or unearth new insights.
Several domains are emerging where AI skills are needed, and these domains cross the existing job families in the field of technology. For example, skills in AI cybersecurity certainly apply to cybersecurity professionals, but some of these skills will also be useful for IT infrastructure professionals or software developers. Likewise, skills in AI data analysis will be extremely relevant for data analysts and data scientists, but they will also be helpful for tech support specialists and cybersecurity professionals.
With the stage of advanced skills containing the most AI-specific topics, it is also the stage with the most churn. AI has the potential to drastically change the way business is done, but if that materializes, then the exact definition of workflow and related skills will also change. A good example of this is the role of prompt engineering, which was considered a high-growth role when the primary form of interaction was a chatbot but has since faded as more diverse forms of interaction are developing. With a strong foundation in digital fluency and targeted skills, workers will be able to closely monitor the AI skills in this advanced stage and pursue training or certification in the areas that seem most relevant and sustainable.
AI skills are part of a bigger picture
CompTIA’s Building AI Strategy whitepaper describes the place of AI within the larger stack of IT applications and IT architecture. Although many sources (including this article) often use “AI” as a shorthand for new algorithms and techniques, many individual pieces may find their way into a company’s systems, including predictive AI, generative AI, machine learning, and neural networks. When a company is integrating AI, it is typically not buying a standalone product and plugging it into its server room.
In the same way, “AI skills” could cover a wide range of possibilities. At the level of digital fluency, it is sufficient to take a high-level approach, learning about the broad definition, the typical components, and the common interactions. When it comes to targeted skills, there will be more detail, as certain parts of the AI ecosystem are tied to existing skill sets. With advanced skills, things expand dramatically, and there can be highly tailored skills for each facet of AI.
Staying relevant requires a comprehensive understanding of these different layers of skill development. It can feel intimidating to think about relearning everything from the ground up, but it’s important to remember that everyone is in the same boat.
By building a solid foundation in AI essentials, then learning AI skills that enhance targeted role responsibilities, and finally pursuing advanced knowledge through AI skills training or AI certifications, workers can ensure that they remain relevant as the jobs landscape goes through the next phase of change.
Ready to harness the power of AI? Explore CompTIA AI Essentials to gain the knowledge and skills needed to lead in the AI-driven future.