Jun 19, 2026 · 39 min · 14 segments
In this episode of the B2B Marketing Podcast, Kavita Singh sat down with Adam Bertelsen, AI Workforce Transformation Lead, Baringa to unpack how agentic AI is reshaping the way work gets done and what…
Adam BertelsenGuest
Kavita SinghHost
But I guess, you know, in your experience across sectors, which type of tasks are AI systems absorbing first? And what does that pattern sort of tell us about how marketing roles are sort of likely to change in the future?
And I think just very, I'll try and be very brief in terms of when we're talking about AI, of course, it's a very kind of a word packed with lots of different definitions.
So for example, if I, you know, use chat GPT like Google search, I just put in a request, I get an output, you know, that sort of one end of the spectrum.
At the other end, Genting AI solutions are more those sort of agents that will autonomously pursue goals that you set using tools and memory across multiple steps or loops, you know, without requiring your input.
And so tools like OpenClaw that have come out sort of led the way at that end of the spectrum.
And of course, there's a lot of a big, wide middle ground where humans and AI agents kind of collaborate together.
in real time where, you know, AI tools have access to workspaces and humans steer them.
So, for instance, you know, when I've been building a history game on Claude Code, you know, very much it's a collaborative effort.
You know, I really wanted to personalize it and give it the steers I want to.
My point being that each of these different types of AI have their uses and very much obviously not everything should be agentic.
In terms of your question about what tasks, you know, that these systems are kind of absorbing first, I mean, there is a pattern that is quite consistent that I'm seeing across every sector, which again, probably won't surprise most people listening to this, which is that AI is absorbing anything that's repeatable, high volume, and particularly things that are explicit by the sense of AI can actually understand the context and have a clear kind of view on whether their response, whether the output is accurate or not, right, it can actually be judged in a machine led way.
And so for law, for example, you know, things like document review and research, you know, synthesis of obviously fall into this, this category, um, in financial services, it's looking at reporting and, and, you know, routine and so on, right? So I think for marketing, B2B marketing, I imagine that a lot of the immediate opportunities across this is content creation, copy creation, and obviously some personalization.
And again, customer service is obviously another area that we're seeing a lot of early adoption of these sort of agenda capabilities.
And I think the thing maybe we can get into a minute, I think is around, you know, this is obviously an entry point for a lot of organizations looking at how we can really automate and, you know, massively, you know, build the amount of kind of content that we create the production value.
But there is a constraint to this still, like it's not just a case that we can implement agentic systems and, you know, deliver lots of value very quickly, right?
But I guess, you know, in your experience across sectors, which type of tasks are AI systems absorbing first? And what does that pattern sort of tell us about how marketing roles are sort of likely to change in the future?
And I think just very, I'll try and be very brief in terms of when we're talking about AI, of course, it's a very kind of a word packed with lots of different definitions.
So for example, if I, you know, use chat GPT like Google search, I just put in a request, I get an output, you know, that sort of one end of the spectrum.
At the other end, Genting AI solutions are more those sort of agents that will autonomously pursue goals that you set using tools and memory across multiple steps or loops, you know, without requiring your input.
And so tools like OpenClaw that have come out sort of led the way at that end of the spectrum.
And of course, there's a lot of a big, wide middle ground where humans and AI agents kind of collaborate together.
in real time where, you know, AI tools have access to workspaces and humans steer them.
So, for instance, you know, when I've been building a history game on Claude Code, you know, very much it's a collaborative effort.
You know, I really wanted to personalize it and give it the steers I want to.
My point being that each of these different types of AI have their uses and very much obviously not everything should be agentic.
In terms of your question about what tasks, you know, that these systems are kind of absorbing first, I mean, there is a pattern that is quite consistent that I'm seeing across every sector, which again, probably won't surprise most people listening to this, which is that AI is absorbing anything that's repeatable, high volume, and particularly things that are explicit by the sense of AI can actually understand the context and have a clear kind of view on whether their response, whether the output is accurate or not, right, it can actually be judged in a machine led way.
And so for law, for example, you know, things like document review and research, you know, synthesis of obviously fall into this, this category, um, in financial services, it's looking at reporting and, and, you know, routine and so on, right? So I think for marketing, B2B marketing, I imagine that a lot of the immediate opportunities across this is content creation, copy creation, and obviously some personalization.
And again, customer service is obviously another area that we're seeing a lot of early adoption of these sort of agenda capabilities.
And I think the thing maybe we can get into a minute, I think is around, you know, this is obviously an entry point for a lot of organizations looking at how we can really automate and, you know, massively, you know, build the amount of kind of content that we create the production value.
But there is a constraint to this still, like it's not just a case that we can implement agentic systems and, you know, deliver lots of value very quickly, right?
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