How should LP practitioners think about that difference when evaluating potential AI use cases?

Thank you, Bo.

I think it's important to distinguish between two things, how mature an AI application is and how strong the evidence is that it actually works.

Those are unnecessarily the same thing.

Take self-checkout loss detection, for example.

Among the use cases presented in the briefing, misted scan detection had the strongest LP-specific research example.

The briefing pointed out an academic field study evaluating misted scan problems.

So what were the most important AI use cases or capabilities that stood out in the report for loss prevention practitioners?

offender trying to get someone to do something, but is there anything else that we could consider here?

Yeah, sure.

So the overarching point is that they are persuasive.

That is number one.

For example, this is my experience just from yesterday.

My wife was actually sitting right next to me, and she received a call that appears coming from me, Sam Yeung.

And I told her explicitly that, no, I was not calling you.

And then, so I think you also get into it a little bit in this playbook, but what are some of the solutions or tactics that retailers can use to combat these scams based on what we see here in this playbook?