EL:

Yeah, exactly. The technology is not the issue. So here we know that generative AI works. Yes. So it allows the merit team word uh to answer one of its biggest pain point is gathering the data. So now thanks to generative AI you can structured unstructured data from many stakeholders many templates. Now you can have the good data at hands in order to leverage the decision making. But the challenge here is organizational. So there are three pillars. You need to ensure that your workforce know how to use properly AI. So not only training them but helping them understanding the capabilities and also the limitation of AI especially because with generative AI, uh the output is critically dependent on the way you’re going to use it. Yes. So it’s a nondeterministic solution and you need to empower people by uh how to use it.

NN:

Absolutely. It’s a tool and if you use it correctly you can really do a lot with it. But if you do it wrong, you could actually end up with with data that is in unhelpful or it might misguide you.

EL:

Yeah. And you need to mitigate that. So any output need to be supported by the source so that all the time that you’re going to save can be part of it can be used to verify the source and check the validity of the output. And lastly the accountability, especially at Bureau Veritas, the expert are at the center of the decision making. they can get help from AI but in the end they remain accountable for the decision and um this is our vision of AI at Bureau Veritas, so it’s not about pushing the technology it’s empowering our expert.

NN:

That sounds really wonderful because basically if any organization gets the three pillars right if they make sure that people know how to use the technology that they’re making sure that the data that’s feeding the technology is accurate and you’re managing that and you’re making sure that it’s an actually an expert who is looking at the output puts and applying them correctly, you’re basically using it really as a powerful tool to empower the workforce rather than something that is meant to either replace very easily or you know with the potential to actually go completely awry and cause uh cause harm.

EL:

Yeah, correct.

NN:

Wonderful. Well, thank you so much, Edouard.

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