AI Implementation Strategy: Why Data Fundamentals Still Matter in the Age of LLMs

Hello, Tom again, back for another AI briefing. Uh, today I just wanted to talk briefly about, uh, you know, how the, the people feel like they're gonna get behind the curve. AI everything in every organization, in every product. If it doesn't have some element of AI in it, then potentially you're missing out, and it's obviously the hype train that's just driving all the things at the moment. Now, of course, the knock-on impact of that is everyone's freaking out about how to leverage AI in the best possible manner.

Now, I have worked across all sorts of different industries in my career, and we've always dealt with data, and underpinning every bit of interesting AI is some interesting data. An organization shouldn't lose sight of the fact that this is critical to any type of AI leverage you wanna do, whether it's just sticking a chatbot on top of it or just going back to more traditional li-like, you know, um, machine learning models that do a specific thing in a more deterministic way than LLM. So when you're considering about how to, like, best leverage AI in the environment you currently work in, also ask yourself the question, "Is there anything that I can be doing with the data that would make it more efficient, more accurate, more reliable when being used in an AI context? " Like I said, that's not just LLMs. That's, like, any type of, like, automated data processing, data insight, data analytics capture.

Often overlooked, but, like, the basics haven't changed. Things-- Like I've been doing this job for the last twenty years, and whilst the technologies have changed, the way that you do, uh, data manipulation, data processing, and, you know, data management as a whole, the concepts have not changed, and everyone still needs to put the effort in to make sure the data is accurate, the data is well-structured, the data is consistent enough. Because even if the LLM comes to the right conclusion, it's gonna take a whole bunch more tokens, cost, and ownership rights that you're gonna have to deal with if you don't put that groundwork in in the first place. So before you do anything, take a step back, think about what data you have, what you want to do with it, what the outcomes and goals are, and how you're gonna achieve it, and with that, you'll be in a much better place. My name is Tom.

Thanks for watching. I'll be back soon with another AI briefing. [upbeat music] Why hire when you can partner? Concept Cloud's leading engineers build your startup's prototype without the overhead. Launch faster.

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