AI Brand Storytelling Workshop for PR & Marketing Professionals

Generic prompts lead to generic content—and in an AI-first discovery world, generic gets ignored. Answer engines tend to elevate what they can understand, verify and corroborate across sources. That’s why earned media and third-party validation often carry disproportionate weight, but you can’t sustain that credibility without owned media that provides depth, evidence and clarity. That’s why we need to evolve how we do PR and use frameworks like the PESO Model®, which has also evolved into an operating system for AI-era visibility. Owned builds the source of truth, Earned validates it, Shared distributes it and Paid amplifies what’s already working. Join us to turn your owned channels into an “evidence library” that makes it easier for journalists, creators, analysts, reviewers and answer engines to quote, cite and trust you. We’ll work through:

• Why authority works differently now and what answer engines reward (clarity, consistency, proof, corroboration)—plus what PR teams must change as a result.
• How to build owned assets that make earned credibility easier to win, and how earned validation strengthens your owned authority over time.
• How to create practical guardrails (voice, tone, claims rules, proof standards) with a brand DNA guide, so AI outputs still sound like your organization, not the internet’s average.
• How to identify the first-party inputs an d structure them using a source-of-truth framework—while also making them usable by humans and machines.
• How to use AI to surface the most cite-worthy stories already hiding in your leaders, operations, customer outcomes, and internal data, and turn them into publishable proof points.
• How to create a lightweight editorial system that protects credibility while still letting you move fast.

Generic content doesn’t earn trust. And in an AI-first discovery world, trust is the ranking factor. Answer engines elevate what they can understand, verify and corroborate across sources. That means your job isn’t to publish more—it’s to publish proof that travels, such as clear answer blocks on owned channels, reinforced by earned and third-party validation, then distributed through shared platforms where the questions (and language) actually show up. Join us to dig into Generative Engine Optimization (GEO) as a practical way to make your expertise visible, extractable and attributable, without chasing SEO hacks. We’ll work through:

• Why AI citations work differently now and what answer engines reward (clarity, specificity, proof, corroboration).
• How to structure content so it’s easy to summarize and hard to distort: claim, context, proof and source.
• How to format thought leadership so it reads as a human wrote it, but still points back to owned proof in a way that’s quotable and consistent.
• The dos and don’ts for newsletters, videos, podcasts and posts so systems can extract your ideas and connect them to you correctly.
• How Reddit and community spaces fit into the system (question mining, objections, language patterns, credibility signals)—and how to show up without losing message control.
• How to evaluate existing content through the “eyes” of an answer engine to spot where authority breaks (vague claims, missing proof, weak sourcing, inconsistent language).
• How to define and reinforce topic clusters over time so your expertise becomes the consistent, corroborated answer.

High-quality stories only matter if they scale without losing credibility or control. And in an AI-first world, scaling is deceptively easy because AI can produce endless “content.” But it can also flatten your voice, blur your claims and turn proof into mush. Once you have all of your inputs ready, we’ll show you how to use the PESO Model® as an operating system for distribution and amplification. We’ll work through:

• How one proof-backed owned asset can become an earned storyline, executive social content, community distribution and paid amplification—without diluting the message or voice.
• A repeatable sequence for repurposing content that protects the core claim, preserves proof and keeps every derivative pointing back to the source of truth.
• How to treat AI like a junior writer—useful, fast and never in charge—while you keep strategic and editorial oversight.
• How to maintain consistency as stories move from internal drafts to external platforms like LinkedIn, newsletters and community spaces (including Reddit).
• How to track LLM visibility, citation frequency and narrative share of voice—plus the distribution metrics that actually connect reach to outcomes (not just output).