With businesses racing to unlock the benefits of AI, automation and cloud-native architectures, the gap between the art of the possible and the skillsets on the ground is growing.
That’s nothing new – long-term ROI models, budget cycles and risk management have always meant that customer infrastructure is rarely bleeding edge – but the impact AI is already having on core engineering expertise and development is problematic, because whilst engineers focus on learning AI-driven tools, platforms and architectures, something crucial is being lost: the foundational skills of the IT engineer.
This is particularly visible in networking and infrastructure. Today’s AI-driven networking tools often come with the promise that “anyone can deploy anything.” Vendor messages are strong on simplicity and basic user interfaces. But under the hood, the technology is still complex. The result? Fewer opportunities for engineers to get hands-on experience with the basics – and a gradual erosion of deep technical expertise, yet when incidents arise, the engineer remains critical to solving complex networking problems.
There’s no denying that AI has already changed how IT teams operate. Intelligent assistants help with deployment tasks such as config generation, issues such as anomaly detection, and business as usual activities such as predictive maintenance to name a few. But while AI augments the engineer, it doesn’t replace them – at least not for the foreseeable future (that is a whole different blog). For the moment, AI is certainly not reducing the need for architects to solve complex problems: when something goes wrong or needs customisation, the system depends on an engineer who understands the fundamentals.
If we look at other industries, the pattern is clear: AI is gutting entry-level roles first. In IT, that might mean fewer junior network engineers, systems admins or first line support engineers. But without those stepping-stone roles, will the next generation have the opportunity to build up their experience and become the Solution and Enterprise architects of the future?
This is more than a workforce issue – it’s a skills continuity problem for the entire industry.
Another concern is that the convenience of AI is already reducing the need – and the opportunity – for critical thinking. When intelligent tools suggest solutions, automatically write code, or configure systems, there’s a temptation to accept their suggestions without fully interrogating the “why.”
While this may lead to a perception of faster and cheaper implementations, this is a risk. Good engineers challenge assumptions, look for causes, and validate results. If we stop thinking deeply about our systems, we become more susceptible to errors, unconscious biases, and security blind spots. While perhaps not obvious at first test, this could cause major incidents, breaches, and a significant challenge in root cause analysis further down the line.
Technical sharpness doesn’t just come from textbooks or training; it’s forged through troubleshooting, through breaking things and fixing them again. If AI is allowed to mediate every interaction, we may lose that sharpness.
But we cannot afford to ignore the benefits of AI in favour of ensuring entry level skills development and future-proofing operational teams. Leaders can help find solutions which satisfy both the need to develop their technical employees and the need to utilise AI to efficiently deliver solutions, faster. This can be achieved by creating opportunities for engineers to go deeper: lab environments, scenario testing, and shadowing on complex changes. These are not add-ons; they are key to building capability that will still be relevant as technology changes.
We need to acknowledge the ongoing role of humans in managing the physical world. While AI can transform the management of virtual infrastructure, it has less direct impact on the physical realities of IT. Hardware still fails. Power and cooling still matter. End-users still behave unpredictably. The physical layer introduces variables that no model fully accounts for, and someone needs to understand how the digital and physical interact. That someone is the IT engineer.
AI should be a tool that supports and enhances their decision-making – not a black box that engineers become overly reliant on. That means building guardrails: clear policies on when and how AI tools should be used, paired with human oversight and regular review. It also means actively investing in skills development, ensuring entry engineers can move up the stack without skipping the foundations.
AI and automation should be viewed as accelerators, not replacements, for engineering capability. The organisations best positioned for the future will be those that continue to invest in building and maintaining deep technical skills alongside the adoption of new tools.
The AI layer may make operations more efficient, but it’s the engineers who understand what lies beneath it who ensure resilience when it matters most.
Chief Technology Officer
Chelsea Chamberlin leads Roc’s Solution and Technology strategy, ensuring continual innovation and focussed partnerships which drive outcome based value for our customers. Chelsea’s background includes designing and delivering software and networking solutions within mission critical environments. Outside of work she is a green belt in Kick-boxing and mentor to young women paving careers in tech.
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