The Problem with AI “How To” Advice on LinkedIn

LinkedIn is now full of AI advice.

Every day there is another post telling us how to use ChatGPT, how to build agents, how to automate our business, how to replace consultants, how to write better prompts, how to “10x” productivity, or how to transform an organisation in a weekend.

Some of it is useful. Much of it is not.

The problem is not enthusiasm. The problem is that a lot of the advice is presented as if AI adoption is simply a matter of finding the right tool, writing the right prompt, or plugging an agent into a workflow.

For institutions, it is rarely that simple.

Banks, insurers and other financial institutions do not operate in a vacuum. They have customers, regulators, data obligations, audit requirements, delegated authorities, legacy systems, conduct risk, operational risk, reputational risk and staff capability constraints. An AI tool that looks impressive in a demo may be completely unsuitable in a live institutional environment.

The real question is not:

“Can AI do this?”

The better question is:

“Can this be done safely, consistently, lawfully and usefully within our operating model?”

That requires more than prompt engineering.

It requires clarity on use case, data exposure, decision rights, human oversight, model limitations, testing, governance, monitoring, escalation, and retirement. It also requires a realistic understanding of the people who will actually use the tool.

A bad manual process does not automatically become a good process because AI is added to it. In many cases, AI simply makes the weakness move faster.

This is where much of the LinkedIn advice falls short. It focuses on tricks, not systems. It talks about productivity, but not accountability. It celebrates automation, but rarely asks who owns the outcome when something goes wrong.

For serious organisations, AI adoption should not start with the tool. It should start with the business problem, the risk boundary, and the operating environment.

The opportunity is real. AI can improve training, sales support, customer discovery, knowledge management, compliance workflow, document production, analysis and decision support. But the organisations that benefit most will not be the ones chasing every new prompt hack.

They will be the ones that ask better questions:

What problem are we solving?What data are we exposing?Who is accountable?What does good output look like?How will we test it?What must the AI never do?What evidence do we need before scaling?How will this fit into the way people actually work?

AI is not just a technology issue. It is a management discipline.

And that is why the best AI advice is often not “how to use the tool”.

It is how to build the judgement, governance and capability to use it well.