IT Myths: AI will solve the skills gap

05 December 2025

The myth

AI will cover missing expertise, guide decisions, and remove the need for experienced people in your IT operation. It will answer every question, design every solution and spot every issue before it happens.

For many SMBs, AI is being positioned as a shortcut: instead of building skills and ownership, you buy tools and let them “take care of it”. That is not what actually happens.

The reality

AI speeds things up. It pulls information together quickly and can highlight patterns you might miss. But it still cannot replace judgement. It has no understanding of business priorities or the real-world constraints behind them. It does not know the political tension between departments, the fragile legacy system everyone is scared to touch, or the customer impact of a mistake.

AI is a multiplier. If your environment is well structured, owned properly and documented clearly, you get useful outcomes. If it is not, the tool simply accelerates whatever problems you already have. You get fast answers to the wrong questions and confident explanations that fall apart on inspection.

Where SMBs get caught out

No service owner. AI is treated as the answer, so responsibility ends up floating around with nobody clearly accountable.
Blind trust in outputs. AI-generated configs, dashboards or fixes get applied without checking assumptions, then systems drift or fail.
Not enough context. Teams underestimate the effort needed to feed AI with the right data. Without that context, answers look impressive but do not help.
Hidden overhead. AI introduces new work: reviewing outputs, managing versions, monitoring accuracy and keeping the tool aligned with how the business actually works.
False confidence. Less experienced staff lean on AI to fill gaps, which can create confidence without capability.

The risk

The organisation leans on AI because it feels like instant expertise. Then an outage hits, costs spike or a security gap appears, and nobody has the knowledge to understand what went wrong. AI can assist recovery, but it cannot lead it. When a business reaches that point, the gap between perception and reality becomes painfully clear.

What actually works

Use AI to remove toil, not responsibility. Let it handle repetitive, predictable work so skilled people can focus on decisions and service quality.

Keep ownership visible. A named person should own service quality, data and processes. AI enhances their role. It never replaces it.

Simplify the foundations. Make processes and handoffs clear and consistent so AI outputs follow the same direction your team expects.

Treat AI like a new engineer. Trust it a bit, validate everything and help it learn your environment before you let it near high-risk changes.

Invest in people. Build confidence and skills in your team rather than trying to outsource their judgement to a tool.

Quick wins checklist

✅ Use AI to draft runbooks, documentation and status updates, then review before publishing.
✅ Let AI summarise tickets, incidents and logs so people spend more time deciding and less time reading.
✅ Keep all AI-assisted changes traceable so you know what it suggested and what you accepted.
✅ Add simple guardrails: who can approve AI-driven changes, what areas are off limits, and how exceptions are handled.
✅ Review AI usage regularly. Keep what genuinely saves time or improves quality. Drop what adds noise.

Bottom line

AI helps close the gap, but it does not remove the need for skills. It shifts the focus instead. The organisations that get the best results are the ones that keep their foundations tidy and know what “good” looks like before they bring AI into the mix. AI is powerful, but it only works well inside a system that already makes sense.