Marla's LinkedIn Series

AI can arrive quietly inside software you already use, but it still needs ownership and oversight.

A manageable AI pilot can become something much bigger, so it is important to map where it may end up before it starts.

AI can organise evidence and support a decision, but it cannot take responsibility for what happens to the person affected. Important decisions need genuine human oversight from somebody with the information, time and authority to challenge or reject the AI’s recommendation.

Small automated mistakes become significant when they are repeated across a business.

When AI output moves into somebody else’s work, the level of responsibility changes with it.

A quick AI draft may stay with one person, but basic rules and proper checking still matter.

Not every use of AI carries the same weight. The matrix helps you see the difference.

The time to agree when you will stop is before everybody becomes determined to carry on.

Someone approved the money. But who takes responsibility when something goes wrong?

Saving time sounds impressive until you count the checking, correcting and extra work.

Before making a process faster, check whether half of it needs to happen at all.

A job title tells you very little about the different kinds of work happening underneath it.

Sometimes the most sensible thing you can do is leave a perfectly decent process alone.

The question that should come before every AI decision, but is often the first one skipped.

The organisations that succeed with AI will be the ones that move with clarity, ownership and purpose.

There is a difference between being bold with AI and approving risks that have not been properly tested.

Six questions that help leaders protect trust, adoption and operational continuity before introducing AI.

Before approving AI spend, leaders should identify exactly where AI belongs in the work and what responsibility must remain human.

Define the evidence that would make you reduce, redesign or stop an AI project before it gathers momentum.

The questions that appear six months into a struggling AI project should have been answered before approval.