Start with low-risk AI pilot projects in non-critical areas to build confidence before wider rollout. Teams experiment with generative AI, compare outputs to traditional methods, identify gaps, and refine prompts through iteration until trust grows enough to scale across the organization.
I think part of the buy-in piece is really to create, maybe create a pilot in some part of the business so that you can experiment and something which is probably not as critical as another dataset. And then you can actually then explore and see and compare the information, the data that you're getting that's been generated through AI. You can explore it to your more traditional models and to see and make comparisons. And through that, you can see where the gaps are going to be and where that we need to make some different adjustments maybe in the kind of prompts where we're looking for the way that we're wanting the technology to function. And I think at that kind of experiment, creating the space for people to experiment, the technology then generates the confidence and trust that then could then be added. So as the example that was already given around marketing, you know, I mean, even my own business, I don't know, actually I've only been using AI for the last 12 months, but then I can't even imagine what I would do, what I was doing before using generative AI. So how did we do this before? So I think as that confidence comes and giving certain divisions, units of the business the confidence to experiment and to see and to then compare, and that then you can see where the gaps are and then close those gaps and then build the confidence. I think that is the way to get buy-in.
