What happens when AI doesn’t replace jobs, but fundamentally transforms how they’re performed?
This is the reality now facing the global technology workforce. While generative AI (GenAI) continues making headlines for its disruptive potential, our research reveals a more nuanced story: one of transformation rather than wholesale replacement.
At Cisco, we recognized the urgent need to understand these changes at a granular level. Building upon the foundational work done within the AI-Enabled ICT Workforce Consortium—a coalition led by Cisco and nine other ICT industry leaders—Cisco Networking Academy has partnered with Lightcast to release a new white paper specifically designed for educators: “Educating Tomorrow’s ICT Workforce: The Role of Generative AI Skills in Entry-Level ICT Roles.”
How generative AI is reshaping entry-level IT roles
Our research focuses on nine high-demand, entry-level ICT jobs, revisiting and expanding insights from the Consortium’s broader study to address the specific needs of instructors and educators. Beyond analyzing AI’s impact, it provides a comprehensive methodology for forecasting how AI technologies will transform specific job roles—a crucial tool for educational planning in this rapidly evolving landscape.
The paper examines the following job roles to identify how GenAI is reshaping skill requirements and task allocation:
- Cybersecurity Analyst
- Ethical Hacker
- SOC Analyst – Level 1
- Network and IT Automation Engineer
- Network Support Technician
- Network Administrator
- IT Support Specialist
- Data Analyst
- Python Developer
This white paper builds on broader research from the AI Workforce Enablement Consortium, which previously analyzed 47 jobs across seven job families ranging from business and cybersecurity to infrastructure and software.
From roles to tasks—a more precise understanding of AI’s impact
Rather than analyzing these job titles in isolation, our research breaks each role into discrete tasks and evaluates which are likely to be automated, which will be augmented by AI, and which remain largely unchanged.
This task-level approach provides greater insights into how jobs may evolve. Low-risk, repetitive tasks—like documentation or data cleaning—are increasingly being delegated to machines. Meanwhile, high-risk or human-centered tasks—those requiring sound judgment or interpersonal skills—are more likely to be augmented rather than replaced.
As a result, workers must shift focus from pure execution to defining problems, delegating appropriate tasks to AI, verifying outputs, and maintaining accountability for outcomes. This transition demands a workforce that is fluent not just in the specific technology and task, but also in how to collaborate effectively with intelligent systems on the task.
Building upon this task-level mapping, once we have established which skills support specific tasks, we can extend the impact analysis to the skills themselves. This deeper analysis allows us to identify which skills will become more or less relevant and highlights new skills that will become indispensable in an AI-driven work environment, informing the evolution of educational programs.
What’s actually changing? Role-specific transformations
Our analysis reveals varying degrees of AI exposure across the nine roles studied. The percentage of principal skills exposed to AI (through either augmentation or automation) ranges from as low as 5 percent to as high as 73 percent, depending on the specific role. This exposure analysis provides a much more nuanced view than simply categorizing jobs as “safe” or “at risk.”
The nature of these changes varies significantly by role:
- Software-oriented roles like Python developers and data analysts will see time-consuming tasks—writing test cases, cleaning data, and documenting processes—increasingly automated. These changes free workers to focus on more strategic, creative work.
- Network automation specialists can leverage generative AI tools to automatically produce scripts, detect anomalies, predict outages, and streamline routine tasks. Specialists remain crucial, however, by guiding implementations and validating outputs through a human-in-the-loop approach, ensuring accuracy and reliability.
- Technician roles in hardware and support remain relatively stable for now. Their hands-on, user-facing nature makes them less susceptible to full automation—at least until embodied AI (artificial intelligence systems that are integrated into humanoid robots) becomes more prevalent. These transformations don’t signal job elimination—they reflect role evolution. Workers aren’t becoming obsolete; they’re being released from routine tasks and called to take on more analytical, integrative, and human-centered responsibilities.
Insights for educators
The research aims to equip educators with knowledge, including a framework for analyzing how GenAI will impact job roles and skills. Based on these findings, high-level recommendations for instructors preparing students for these roles include:
- Equip students with core professional skills.
- Integrate AI literacy across all
rolestraining programs. - Teach both the why and how of work so students understand the reasoning behind their work, know how to define the task to be done to an AI, and what to verify in the output of the work product done by an AI.
- Prioritize developing skills in responsible AI and ethics.
In addition to the 50+ page report, we also provide Cisco Networking Academy instructors with a companion web page outlining specific training recommendations for each role, along with resources to train and upskill themselves and their students.
The time to act is now
The pace of change continues to accelerate. Within three to five years, GenAI is expected to be deeply embedded in standard work processes. But it won’t replace people—it will amplify their capabilities.
For educators, this means preparing students to use AI tools, understand them, question them, and work alongside them. Technical skills alone are not sufficient. It is more important than ever to cultivate the judgment, communication, and leadership abilities that will matter most in hybrid human-machine environments.
We’ve entered a new era—one that rewards learning agility, a growth mindset, and a proactive approach to lifelong learning. Educators who adapt their curricula now will ensure their students remain competitive and excel in an AI-integrated workplace.
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