Oct 2, 2026 · 28 min · 8 segments
In this episode, Ole Olesen-Bagneux sits down with Vanessa Andersson, Senior Data Engineer and founder of Data & AI Stockholm, to explore what the rapid evolution of AI looks like from the perspective…
Ole Olesen-BagneuxHostHow do you see the current state of data and AI? And to elaborate a little, like, what are the trends and what are the things we're not giving enough attention and what is being exaggerated? That's, like, the reason I ask you is because I think you have a particular idea.
Like, I feel, I feel like we're really lucky that we are living in these times.
I mean, AI has moved from something that primary technical teams were talking about to something that almost every company sees now like they need to have a strategy for, right? It's becoming everyone needs and ha- wants AI.
You can see that companies and people have, like, start now moving beyond the, the first wave of experimentation.
It's becoming less about, can we build something with AI? It's more like the question of, of can we make something useful and reliable using AI within the organization? So that changes a lot the conversations between, uh, corporations, because suddenly now you feel like everyone realizes that data quality and governance and security is very, very important in order to actually be able to, to work with AI.
And I think that's, uh, uh, things that are becoming very interesting because you see now suddenly that everyone wants to get there, but also realizing that you need to actually build a good strategy, and you need to have, uh, the fundamentals, uh, the- there in order to get there.
You mentioned about, um, the trends as well, right? So like, what are the trends? Um, well, obviously I would say agents-
Um, uh, we are moving from AI that simply answers questions to, well, systems that can actually reason across information and use tools and take actions.
But I think one thing we are not giving enough attention is even, even what I mentioned before, is like the foundation underneath all of this.
So everyone wants AI, but AI doesn't, like, magically fix, you know, ba- bad data.
If your data is fragmented, then... or poorly governed, then, or something that I see very com- common is, like, very bad document- documentation.
Then, uh, you know, it's, uh, it's the metrics, the outcomes will not really be reliable.
I see, and that's, that was the other part of the question, right? What is being exaggerated? I have to admit, I see a lot of things being exaggerated or not being correctly explained, and that bothers me a little bit.
How do you see the current state of data and AI? And to elaborate a little, like, what are the trends and what are the things we're not giving enough attention and what is being exaggerated? That's, like, the reason I ask you is because I think you have a particular idea.
Like, I feel, I feel like we're really lucky that we are living in these times.
I mean, AI has moved from something that primary technical teams were talking about to something that almost every company sees now like they need to have a strategy for, right? It's becoming everyone needs and ha- wants AI.
You can see that companies and people have, like, start now moving beyond the, the first wave of experimentation.
It's becoming less about, can we build something with AI? It's more like the question of, of can we make something useful and reliable using AI within the organization? So that changes a lot the conversations between, uh, corporations, because suddenly now you feel like everyone realizes that data quality and governance and security is very, very important in order to actually be able to, to work with AI.
And I think that's, uh, uh, things that are becoming very interesting because you see now suddenly that everyone wants to get there, but also realizing that you need to actually build a good strategy, and you need to have, uh, the fundamentals, uh, the- there in order to get there.
You mentioned about, um, the trends as well, right? So like, what are the trends? Um, well, obviously I would say agents-
Um, uh, we are moving from AI that simply answers questions to, well, systems that can actually reason across information and use tools and take actions.
But I think one thing we are not giving enough attention is even, even what I mentioned before, is like the foundation underneath all of this.
So everyone wants AI, but AI doesn't, like, magically fix, you know, ba- bad data.
If your data is fragmented, then... or poorly governed, then, or something that I see very com- common is, like, very bad document- documentation.
Then, uh, you know, it's, uh, it's the metrics, the outcomes will not really be reliable.
I see, and that's, that was the other part of the question, right? What is being exaggerated? I have to admit, I see a lot of things being exaggerated or not being correctly explained, and that bothers me a little bit.
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