When a company decides to get serious about AI, the first instinct is often to send people to get certified. Certifications feel concrete: a syllabus, an exam, a credential on a profile. But six months later the certificates are framed and the way work gets done hasn't changed. The problem isn't that certifications are bad — it's that they answer a different question than the one most organizations are actually asking.
A certification validates that an individual, on a given day, could pass a standardized exam on a defined body of knowledge. That's genuinely useful for a few things: establishing baseline vocabulary, giving individuals a structured path, proving depth on a specific vendor's tools, and providing a hiring signal. If those are the outcomes you need, a certification is a reasonable buy.
What it does not measure is whether your team can apply that knowledge to your workflows, your data, and your constraints — or whether capability spreads beyond the person who sat the exam, or whether any of it survives contact with next quarter's tools.
Use certifications deliberately, for what they're good at: giving a specialist deep vendor-specific knowledge, supporting an individual's career development, or setting a baseline for a hiring bar. Treat them as one input, not the strategy.
Ask what outcome you're actually buying. If it's an individual's credential or baseline knowledge, a certification is efficient. If it's “our teams use AI well, safely, and in daily work,” that's an organizational capability outcome — and certifications were never designed to deliver it.
Certifications and corporate training aren't rivals; they solve different problems. Certifications credential individuals. Corporate training builds capability into how the organization works. Most companies asking “certs or training?” actually want the second outcome — which is the one we focus on at CloudCamp: private, role-based programs built around your teams' real work.
