So the reason for AI to exist in any organization, is to enhance human capability. Is not a replacement for human workers, it's an enhancement. So it's looking at the tedious work that a human might have to do. Pouring through data, understanding what humans aren't really necessarily good at, which is that tedious, boring, counting type work, replacing that with machine learning, but then also being able to pull in lots of different sources of data to really turn that analysis into insight.
The best way to set up your organization around data is firstly, to kind of stop calling it data, because I think it creates a psychological barrier for a lot of people. They think that it's just the realm of a techie person or a science oriented person &instead to start reframing it as it's insight, it's knowledge, it's information, & to really personalize the, you know, what you're using as data to make it clear what the sources are so that you can start getting people comfortable with using it on a day to day basis & their roles & understanding the relevance of it to their roles.
You know, it's not about using data. It's about empowering your decisions & these are decisions you're already making. So they might as well be informed.
So it's about creating a shared goal for a team. You're not putting people on a training course to learn how to use data. Instead get people who need to be brought up to speed with a data tool or strategy or technique, put them on a project, working with other people who have that experience, give them a shared goal & have them actually learn while doing, & then they're going to see the value of it. They're going to understand how it's relevant in their day to day work. And they're going to be able to carry that forward. The worst thing you can do is put them on a training course. It's totally in isolation from their job.
Cross collaboration, working with AI & data is, is it's not just important. It's critical because if you don't have that diversity of perspective of experience, of skillset, the whole point of using data as to accomplish a goal for the organization as a whole, as it relates to their external users, you will naturally have people across the organization who play a role in delivering that service, that product to those end-users.
So you need those people to be at the table, making the decisions around how to use something like. AI, otherwise the decision-making of the AI is only going to be partially informed. So you really do need that diversity, not just in terms of people's roles, but also, their backgrounds, their insights into the people that, that end product that end service is designed to serve.
So it's really obvious the businesses that have not successfully digitally transformed because change happens around them & they panic by it, they don't know what to do in response to change. Whereas a successfully digitally transformed organization is adaptive to change. They see change as an opportunity rather than a threat, & they're able to mobilize around it.
You know, I say in my book, digital transformation really just boils down to being adaptive, to change the way you do it is by leveraging data, technology & new ways of working.
So the new ways of working for the digital age is around collaboration. Iterative working style so that you're, you're collaborating across your organization & you're including the voice of the users you're iterating on your solution, so that you're running small experiments to get data, to find out whether the things that you're producing is actually having the, the end goal that you wanted to, & then you're scaling it up as you go.
AI and data are essential for decoding the future because there are so many points of data right now. There are so many sources of data right now that if you don't use those tools and that's really just all they are they're tools. If you don't use those tools, you're going to be missing a whole picture.
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