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There's a quiet restructuring happening inside PR and communications agencies right now — and most of it isn't visible in the work product. It's visible in the systems behind it.

As AI reshapes how narratives are built, distributed, and discovered, the agencies moving fastest aren't the ones with the best tools. They're the ones with the best systems.

I recently connected with Kyle Arteaga, CEO and Founder of The Bulleit Group, a PR and narrative systems consultancy he built in 2012 after leading communications at Siemens and Reuters. In 2024, he restructured Bulleit around lean, AI-enhanced teams — and in 2023, published one of the first open-source generative AI frameworks for communications. His view: the advantage was never the tools. It has always been the workflows.

Let's jump in.

🧭 What does an actual agency-grade AI tech stack look like today — and which tools are genuinely load-bearing?

💬 Kyle's take: Our stack is simple. The advantage is the system.

Claude, ChatGPT, and Gemini are used daily for research, synthesis, and structured thinking — not final output. Zapier pulls in signals across media, competitors, and narratives, shaping what we say and when. Athena shows whether our narrative is showing up in AI systems. It's directional, not definitive. MuckRack and GlobeNewswire remain core, with MuckRack focused on targeting and GlobeNewswire on distribution and structure.

The system is the point. Models shape thinking. Signals refine it. Distribution ensures it shows up. Feedback tells us if it worked. It’s not a collection of tools, but a whole that is greater than the sum of its parts.

🔑 Key idea: A working AI stack isn't a list of tools. It's a loop, where each tool informs the other.

🔍 What does quality control look like now — around accuracy, hallucinations, tone drift, and keeping client work from going generic?

💬 Kyle's take: We assume AI is useful, not reliable. Nothing goes out without verification. Facts are checked against primary sources. AI does not define voice. Humans do. Everything is rewritten or heavily edited.

The review process is stricter, not lighter. Senior review remains required before anything goes out. We don't use AI in high-stakes outputs like CEO messaging, crisis, or positioning. AI can generate answers. It cannot be the source of truth.

🔑 Key idea: At the very least, trust but verify. Better yet: be skeptical, double-check everything, and rewrite heavily.

💰 Does AI fundamentally change how agencies price their work — and are we moving toward charging for judgment rather than hours?

💬 Kyle's take: AI breaks time-based pricing.

If work gets faster, hours don't matter. Clients are paying for judgment and systems. The value is knowing what to say, where to say it, and how to show up consistently.

Scope becomes more fluid. Work adjusts based on what is showing up and what is not. AI compresses production and increases the value of decision-making.

🔑 Key idea: The billable hour may be the first casualty of AI in agency work… but it’s not clear what will replace it. In-house teams and agencies are going to have to figure this out, together.

📣 Is it still the core job for communicators to tell a compelling story — or is it increasingly to build the architecture that makes the story discoverable?

💬 Kyle's take: Storytelling still matters, but if no one sees the story, it doesn't matter. We need to do more than just craft a narrative. The job requires us to build the system that makes the story discoverable — to build something that gets found. Stories create meaning. Systems create visibility. The job is now both.

🔑 Key idea: Communicators can’t just be storytellers anymore. If you’re not thinking about how your story is going to reach your audience, you’re missing a huge part of the equation.

🚀 For agency leaders (or internal teams) who feel overwhelmed, what should they actually build in the next 12 months — and what's AI going to commoditize versus make more valuable?

💬 Kyle's take: Stop learning tools. Start building systems.

First, build an insight layer — a repeatable way to understand what's happening. Second, build distribution across platforms. If it's not distributed, it won't show up. Third, build a feedback loop so you know whether you show up or not. AI commoditizes production and increases the value of judgment. Most teams are still optimizing for output when they should be optimizing for visibility.

🔑 Key idea: The teams that win won't have the best tools. They'll think about how each of those tools connect together. And documentation will be key: what was built, why, and what impact it had. That will inform future builds.

💡 My thoughts: Kyle's framing cuts through a lot of the noise around AI in communications. The question isn't which tools to use — it's whether you've built a system that connects insight, production, distribution, and feedback into something that actually compounds over time. That's a different kind of agency work, and a different kind of value proposition. (And yes, the same principles apply if you’re working in-house.)

It's also where communications leaders have a real opportunity — not just to adopt AI, but to architect how their organizations use it. That’s going to be a theme in comms moving forward.

Want to share how your team is approaching AI? Reply to this email — let's talk!

Last week, I launched AI Made Simple, a free 10-module starter course built specifically for communications professionals. A few dozen of you have already jumped in, and the early response has been really encouraging.

It's about 90 minutes total — practical, comms-specific, and designed to give you a real foundation, not just a taste. If you haven't started yet, it's waiting for you on my Courses site.

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A new BCG survey of more than 200 corporate affairs and communications leaders surfaces a striking number: 68% of CCOs describe their function as an AI laggard. That's happening while their CEOs move in the opposite direction — more than 70% of CEOs now say they are the primary decision makers on AI, and half believe their job depends on getting AI right. The gap isn't skepticism: nearly three-quarters of communications leaders say they believe in AI's potential and payoff. The problem is execution. The top obstacle was not access to tools, but a lack of clarity around how to actually build an operating model for AI. This was flagged by 35% of CCOs.

The leaders who have closed that gap are pulling ahead fast. One in ten corporate affairs and communications teams report deep or systematic AI integration into workflows — and that group is entirely within the leading cohort. Among lagging organizations, that figure is zero (!). They're also investing in people at a different rate: 71% of leading teams have upskilled at least a quarter of their workforce, compared with just 22% among lagging teams. That capability gap will compound as the tools get more powerful.

And this isn’t a one-off survey. PR Daily saw the same trends emerging, stating that “the most dangerous AI gap isn’t technical, it’s operational. It’s already showing up as uneven confidence, misalignment, confusion and reporting that fails to guide decisions.”

Why it matters: The barrier to AI adoption in comms isn't access or awareness — it's the absence of a working model for what good AI use actually looks like in practice.

My take: More comms teams should be hiring AI adoption leads. The role is still rare, but it shouldn’t be. Someone who understands communications and can build workflows, tools, and governance can accelerate adoption far faster than scattered experimentation.

Here are things I’ve built while figuring out how communicators can use AI well — shared here so you can experiment with them too. I’ll keep adding to this list over time.

AI agents already outnumber human visitors to your docs — now you can track them.

See your AI traffic!

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Quick question: how can I help?

What workflows are you struggling with? Where does AI still feel mysterious or overwhelming? What’s worked that you’d like to share with others?

I’m a builder, and I’d love to help you and the rest of The Comms Stack community find great new ways to use AI.

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Even a one-sentence reply helps. For example:
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Until next Wednesday,

Dan

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