AI adoption barriers across organizations:
How to solve them & implement a data-driven strategy

Warren Saunders, President of Global Sales at GfK, has been interviewed at our Decode the Future event. He answers questions around the barriers of adopting AI, how to solve them & implement a data-driven strategy.


  • How should businesses make the leap to being data-centric?

    I think the most important thing is to use data, to challenge your existing processes & beliefs rather than to reinforce them. So think about how you use data to do things differently rather than to do things the same, but maybe quicker or in a slightly different way. How does it change your business & how can you adjust the way that you run on a day-to-day basis?

  • What are some of the barriers to adopting AI?

    I mean, clearly there are two main barriers to AI adoption. One is a technical one and the second one is a cultural one, and I think the technical one can be overcome relatively easily. I think. Organizations like GfK have the ability to roll these kinds of services out to you in a browser-based format so that you can democratize them & get them out across your whole business.

    I think the cultural one is more of a challenge.

  • How should businesses start having the conversation with employees about being more data-centric and using AI?

    It's a cultural shift & I think it's a bit about empowerment. I think, the great thing about AI & the great thing about something like these kinds of solutions. So it's a great opportunity to democratize that data across the business & rather than having data being controlled by a small kind of gatekeeper within the organization & you can take that much more broadly & that data can provide a much broader range of answers for you than perhaps it has historically been doing.

    So, I would encourage that kind of democratization go broad, as quickly as you can.

  • What are some client pain points in using data within their businesses?

    Data's a dangerous thing. There's a lot of it. We're all drowning in data on a constant basis & data can tell us kind of pretty much whatever we want to hear.

    You need to look broad, but equally not too broad because otherwise it just comes into big & it just comes & becomes almost overwhelming. So I think you really need to be super focused on what's the question I'm answering. What is the issue that I need to address & then where do I need to get that data from in order to answer that question?

  • What skillsets do you need to recognize actionable data points?

    I think training, more broadly is really important and I think making sure that if you're getting a third-party solution, you're getting one that can be easily rolled out. And that does deliver its insights in a relatively plain & simple way, so that the barrier to adoption is limited. While there will be of course training & onboarding & all of those things required, hopefully you can get up to speed relatively quickly.

  • What is step one in implementing a data-driven strategy?

    Think of one question you want to answer. Kind of going back to something I said before, I think don't try & go too big at once. Take things a step at a time, think about what thing would I change in my organization? There'll be one element maybe within your customer journey that you're unclear on or that you want to know more about pick that one & then go in & try & answer that & then go broad from there, otherwise we end up either with too shallow an insight across to broader range of questions or just overwhelmed.

  • How do you make data open & accessible across an organization?

    Put something in the cloud so that everyone can access it.

    Make sure that any insights served up from that data are done so in a really clear way, so that the language used is not confusing.

    Again, being focused rather than too broad & too vague, & making sure that that's targeted at the right users within your organization.

  • How can intelligent automation (AI) and data help decode the future?

    Historically, I would have argued that if the past can teach us anything it's that it can teach us, what's going to happen in the future. Clearly that's I would say that by belief has been challenged in the last couple of years. So I think really where AI gets becomes more & more important is being able to slice data much more finely. If you were an organization making a washing machines five years ago, you would probably have looked at what happened over the last year and use that to understand where you go next and use that, quite a granular, basic level. I think now, as consumer behaviors changing, channel strategies changing, pricing changing, all sorts of things are changing.

    You've got sustainability coming through really, really strongly, really, really quickly. COVID has brought a massive kind of step changing in culture and in retailing and in consumer behavior. So I think using AI to thinly slice and really understand what, understanding what happened last week is as important as knowing what happened last year, because things are changing so fast now.

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