Do Companies Still Need AI Training When AI Can Answer Everything?

Short answer: it depends on what the company needs. AI is very good at answering questions, and it keeps getting better.

Companies tend to benefit from training when staff need guided practice, context for their own work, or shared standards. Not every company needs a paid workshop, and not every topic needs a trainer.

A trainer also brings experience and guided learning together. When people learn with peers, one colleague's question can be the question everyone else did not know they had. A team that learns together moves forward together, instead of each person figuring AI out alone.

If AI can answer everything, why should a company still pay for AI training for employees? It is a fair question. It deserves an honest answer, including the part where AI wins.

It is a little awkward to even be comparing AI and trainers for training and upskilling. But we have reached that era.

The technology is genuinely amazing, and it can help upskill and uplift us. So the question is not whether AI belongs in learning; it does. The question is what each side is actually for.

Where AI genuinely helps

Credit where due. For self-study, AI is often the fastest teacher around.

• A quick answer at 11pm, before a morning meeting.
• A walkthrough of a feature you have never touched.
• A second opinion while you work, on demand.

When answers are this easy to get, training has to offer something more. Otherwise it is not worth paying for.

The frustrations nobody puts in the brochure

Ask people who tried to learn AI mainly by asking AI. Some of these will sound familiar.

• You cannot ask the question you cannot phrase. An AI works with the question you type. When the words come out tangled, the answer can come back tangled too.
• You do not know what you do not know. Beginners are not only learning AI, they are learning how to use it. There is no map of your own blind spots.
• The answer arrives without your context. Unless you spell it out, or the tool is connected to your systems, it does not know your setup, your data rules, or what your team has agreed never to paste anywhere.
• The reply often aims at the typical case. Your situation, with its own history and constraints, is left for you to figure out afterwards.
• You can lose an afternoon. Twenty tabs, forty prompts, and by evening nothing is decided. Each answer only led to three more questions.
• Nobody notices when you are lost. The tool can explain another way when asked. But you have to know to ask, and to know what to ask for.
• Information is not confidence. You can finish a chat session knowing more and daring less. Multiply that across a team and you can end up with private habits, uneven quality and no shared standard.

A small experiment

Picture a common workplace scenario. A manager opens an AI tool and types:

My team keeps missing deadlines. How do I fix this?

The reply might be sensible. Set clearer expectations. Check in weekly.

The tool may also ask a clarifying question or two. Even then, it can only work with what the manager knows to type.

It does not know that two people are covering a vacant role, that the client changed scope three times, or that the last check-in went badly.

So the real question can sit unasked. A follow-up is hard to phrase when you are not sure what the real problem is yet.

A good facilitator can also watch the room and ask the follow-up questions nobody thought to type. That is where an in-person session can make a difference.

When the answer is confident and wrong

Sometimes the problem is not a vague answer. It is a wrong one, delivered with full confidence.

• One Reddit user described asking an AI tool for sources for a research project at work. Every article the tool named was invented.
• Another used a confident answer without checking. Key details were made up, and the redo took longer than the original work.
• A mentor described an intern who asked for basic syntax and got a wrong answer. The first search result had the right one.

These are individual stories, not statistics. But they point to the same skill: knowing when to trust an answer, when to verify it, and how to check.

When the tool alone is not enough

Trainers collect moments like these from real sessions. They are individual accounts, shared anonymously, not statistics.

• One participant saw an AI tool perform impressively in a TikTok video and assumed it could do the same for his work. When he tried it on his own tasks, it could not.
• Another came in believing that tokens are everything, after reading an article about AI. He was not sure what a token or a context window actually was. The session gave him a clearer frame: where AI fits, where his own input matters, and how much to rely on it.
• A third described generating a report with AI. When his superior asked what the report was for and why certain figures were flagged, he could not explain either; he said he had put the information in and AI had generated the rest.

These accounts point to a gap between getting an output and understanding what the tool can do, what its answer means, and what still needs checking.

What a good in-person session does differently

• Someone is actually watching. A good trainer notices who is falling behind, often before that person says a word. A group that nods together is not the same as a group that understands together.
• A trainer hears the question behind the question. When you describe a problem in tangled words, an experienced trainer untangles it and answers what you actually meant.
• The subject is how to think, not just which buttons to press. Good training covers the method and the thought process behind using the tools well, not only the tools themselves.
• A facilitator draws out what you would not type. Some questions never make it into a chat box. In a confidential session, a trainer can surface them and address them safely.
• Sessions survive real life. Wifi drops, accounts refuse to sign in, and a trainer adapts the room and carries on.
• People learn from each other. A colleague's question can be the one you did not know you had, and the answer helps the whole room. Those aha moments rarely happen alone.
• Practice happens in the room. Participants work on realistic tasks while someone experienced watches, corrects and encourages in the moment. Mistakes are cheap there, not back at work.
• Some trainers also draw on approaches from fields such as neuro-linguistic programming (NLP). Building rapport early and creating a safe space to ask questions is the idea. It is one approach among several, not a guaranteed formula.
• A team can leave with a common language. What we use these tools for, what we never use them for, and who checks what before anything leaves the building.
• Done well, a session can leave people believing they can do it themselves. Confidence, adoption and productivity gains can follow. None of these is automatic.

And when the workshop itself is the problem

An in-person format alone does not make a workshop effective. AI workshops have their own failure modes.

• The demo-only spectacle. The trainer impresses. Nobody practises.
• The pace set for the fastest person in the room. Half the room quietly falls behind.
• Tasks from a perfect world. They look nothing like Monday's actual work.
• No checking, no support afterwards. People leave unable to apply what they saw.

A poorly run workshop can waste a day as thoroughly as a lost afternoon of prompting. The difference is never the room itself. It is what happens in it: real practice, someone checking one by one, a pace matched to the people present, and follow-up that helps the learning survive the return to work.

What skipping training can cost

None of this means every team needs a workshop tomorrow. But when teams skip structured learning entirely, a few patterns may follow.

• A good tool in untrained hands. A company can buy the best hammer and spanner available. Without knowing how to use them, the result is not productivity but frustration, and sometimes a negative feeling toward the tools that is harder to name.
• Isolation. When everyone learns alone at a separate screen, nobody hears a colleague's question. One trainer's view: in offices where teamwork is the default, as in many Asian workplaces, that isolation cuts against how work actually gets done.
• Private habits and no shared standard.
• Capable tools used at a fraction of their value.
• Sensitive information ending up in the wrong place, the same risk covered in this guide on Using AI at Work Safely.

None of these outcomes is guaranteed. But they are worth pricing into the decision, next to the fee you saved.

The question worth asking instead

So the choice is not AI or training. It is knowing what each one is for.

AI gives you answers whenever you ask. Training offers experience, facilitation, connection, and the chance to build the confidence to actually use what you learned.

The question is not whether AI can answer. It can. The question is whether answers alone change what a team does on Monday morning.

Where Mike Cheong comes in

One trainer working in this space is Mike Cheong, a Malaysian HRDC-accredited trainer. He teaches these tools because he uses them in his own work, and runs sessions in person and online.

"clear and patient and know extra about ai" – Participant, corporate AI workshop (2026)

If your team could use a session like that, his AI training courses for Malaysian workplaces are a place to start.

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