Question 1

Why are custom-trained compliance models becoming less effective?

Accepted Answer

While training models on firm-specific data once seemed like the gold standard, the reality is that today’s large language models (LLMs) are already highly capable. Custom models introduce high operational overhead, require compliance staff to act as AI trainers, and often suffer from poor generalization or vendor lock-in. In practice, these models can actually reduce effectiveness when compared with leveraging foundational LLMs supported by smarter system design.

Question 2

What makes agentic AI different from traditional AI approaches?

Accepted Answer

Agentic AI doesn’t try to “retrain” the model itself. Instead, it builds intelligent, structured frameworks around the model—using agents configured with firm policies, workflows, and decision trees. These agents act as specialists, guiding the LLM with precision prompts and rules. The result is greater fluency, reliability, and transparency—delivering faster reviews, consistent compliance, and audit-ready documentation.

Question 3

What should compliance teams focus on to get real value from AI?

Accepted Answer

The real differentiator isn’t the model—it’s how you communicate with it. Success depends on AI fluency: building systems that give models clear instructions, structured prompts, and contextual rules. Firms that focus on architecture and communication see shorter review cycles, quicker time to market, reduced risk, and stronger regulatory alignment—all without the burden of endless retraining or black-box outputs.

Question 4

Show me the transcript!

Accepted Answer

Speaker 1

Welcome to the deep dive. Today we're really diving into quite a seismic shift happening in AI.

Speaker 2

Definitely.

Speaker 1

Especially in areas like compliance, ad review—places where it's critical.

Speaker 2

Absolutely.

Speaker 1

For years all the talk was about who had the smarter AI model. Right.

Speaker 2

The custom built one, that was the race.

Speaker 1

So our mission today is to unpack why that's kind of flipped. Why we're now talking about something called agentic AI. You might actually be surprised to hear that in a way, the model wars, they might be over.

Speaker 2

It's a really fascinating turn, isn't it? Because for so long companies were pouring, I mean, millions into training their own.

Speaker 1

Bespoke AI, thinking that was the edge.

Speaker 2

Exactly. The ultimate competitive advantage. But what we actually saw happening—well, those custom models started showing brittleness and overfitting.

Speaker 1

Okay, what does that mean in practice? Like brittle.

Speaker 2

Think of it like a super specialized athlete: amazing at one thing, but struggles with anything else.

Speaker 1

Gotcha.

Speaker 2

They kind of break down when they face something new, something unexpected. And in compliance, that happens constantly.

Speaker 1

Right. Regulations change, market conditions.

Speaker 2

Precisely. So this brittleness, it often meant performance actually degraded when these models hit the real world.

Speaker 1

Okay, let's dig into that a bit more. The old way, you'd build your own bespoke model, feed it thousands, maybe millions of your documents, fine tune it.

Speaker 2

Sounds logical on the surface.

Speaker 1

Yeah. More data, more specific training should mean better learning, you'd think. But what were the, like, the real world snags?

Speaker 2

Well, that whole approach hit some pretty significant walls. First off, just the high operational overhead, just the sheer time and resources needed for training, and then retraining constantly, especially as rules kept changing.

Speaker 1

Yeah, that sounds exhausting.

Speaker 2

And that burden—yeah, it often landed smack on compliance teams who were already, you know, stretched.

Speaker 1

Thin, asking them to suddenly become AI experts overnight.

Speaker 2

Pretty much. Then there was poor generalization. A model trained perfectly on yesterday's data.

Speaker 1

Might completely fail tomorrow.

Speaker 2

Exactly. On those unforeseen edge cases.

Speaker 1

Yeah.

Speaker 2

And critically, you often got stuck with vendor lock in and opacity.

Speaker 1

Ah, the black box problem.

Speaker 2

Right. These custom solutions.

Speaker 1

Yeah.

Speaker 2

You couldn't easily figure out why the model flagged something specific. Try explaining that to an auditor.

Speaker 1

Okay, so this is where it gets really interesting then. If everyone's starting to use the same powerful foundational models, LLMs, right, the large language models like OpenAI's GPT-4, Google's Gemini, Anthropic's Claude—these big engines—then the difference isn't the engine itself anymore, it's everything else. Everything you build around it.

Speaker 2

Precisely. And that is the core idea of agentic AI.

Speaker 1

So you're not trying to reshape the core brain, the LLM.

Speaker 2

No, you use that LLM as this incredibly powerful general-purpose reasoning engine.

Speaker 1

Yeah.

Speaker 2

And around it you build these structured intelligent layers. We call them agents. Think of them like specialist detectives. Each one is designed for a very specific task.

Speaker 1

Okay, like what?

Speaker 2

Well, maybe one agent is an expert at parsing complex financial disclosures, another one is checking the tone in marketing copy, and maybe a third is just flagging high-risk terms. But based on your firm's specific rules and context.

Speaker 1

What I think is super fascinating here is how these agents, how they actually interact with the LLM.

Speaker 2

Right.

Speaker 1

They communicate with the LLM on your behalf using things like structured prompts, metadata, decision trees.

Speaker 2

Exactly. It's all about speaking the language of the LLM fluently, not just throwing data at it.

Speaker 1

It's less brute force, more finesse. Like giving a master craftsman really precise instructions instead of teaching him how to build the tools himself.

Speaker 2

That's a great analogy. It really does replicate how a seasoned human compliance reviewer operates.

Speaker 1

How so?

Speaker 2

They don't just scan for keywords. They bring context, policy knowledge. They have that kind of structured mental checklist.

Speaker 1

Okay.

Speaker 2

Agentic AI tries to mimic that structured thinking through better communication frameworks. And the beauty is you improve the system over time by refining the agents.

Speaker 1

Tweaking the prompts without needing to retrain the model from scratch.

Speaker 2

Exactly. Which makes it incredibly agile, much faster.

Speaker 1

To adapt, and the real world payoff seems pretty significant. Firms using this agentic approach, they're seeing real benefits. Yeah?

Speaker 2

Oh, absolutely. Tangible stuff. We're hearing about major reduction in review cycles. Like how much—some firms report, like a 70% drop in manual oversight needed, which obviously leads to faster time to market for campaigns and products.

Speaker 1

That's huge.

Speaker 2

Translates to lower risk exposure too.

Speaker 1

Yeah.

Speaker 2

And maybe the biggest piece for many—audit-ready transparency.

Speaker 1

Ah. Because you can see the why precisely.

Speaker 2

Every agent's decision, the specific policy rule it looked at, the prompt it used, it's all logged. Automatically creates this clear audit trail. Completely different from those old opaque models.

Speaker 1

And this matters so much right now because let's face it, the regulatory environment isn't getting any simpler.

Speaker 2

Not at all. Compliance teams need solutions that are flexible, transparent and frankly, more human-aligned.

Speaker 1

Yeah, it feels like it's about avoiding the traps of the past. Maybe where AI was sometimes overpromised.

Speaker 2

Right. Through approaches that were maybe fundamentally flawed. This is about delivering real measurable value now.

Speaker 1

So when you boil it down, the key insight seems to be the future of AI in compliance. It's not really about which model you pick anymore. It's about how you are using it. It's about your team's, your firm's fluency—how well you talk to the machine.

Speaker 2

That's exactly right. The AI arms race, you know, the one focused purely on the model—that's over now. It's all about strategy.

Speaker 1

Strategy and how you use it.

Speaker 2

Yeah. The firms that win are going to be the ones who master building these agentic systems. Systems that understand business context, apply specific—

Speaker 1

Company policies, and deliver that contextual intelligence.

Speaker 2

Through clear, purposeful, nuanced communication with the AI. So the real question for you listening is: what will your firm do to become fluent in this new language of agents?

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