Agenda

Every marketer now has access to the same AI tools — so what actually makes one brand's results different from another's? This shop talk invites a candid conversation about the data your business already owns, the data it doesn't yet know it needs, and how to connect the two before your competitors do. It's also a conversation about the shift underway in how marketers even find these answers: as static dashboards give way to AI agents that can surface anything on demand, the hard part stops being what data to gather and becomes what questions actually need answering.

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When you're shipping ten times the creative, you inherit ten times the ways to get it wrong. This shop talk takes on the quality-control problem head-on: how high-output teams keep AI-generated work on-brand, accurate, and on-standard without a human eyeballing every asset. Expect a frank exchange on review systems, brand guardrails, and the failure modes worth watching for as creative volume climbs.

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When agents handle the execution, the marketing org itself has to be redesigned around them. This case study walks through a real operating model: how work gets briefed, routed, and reviewed when humans set direction and AI does the doing. You'll see how roles, rituals, and accountability shift, where humans stay in the loop, and what a team running this way actually looks like on an ordinary Tuesday.

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Watch a single piece of content travel from brief to published without ever leaving the AI stack. This live demo focuses on the parts most teams get wrong — building a brief the model can actually act on, and running the edit-and-approve loop — then carries it all the way through to publish. A practical, screen-share look at a working content pipeline, with the handoffs, prompts, and checkpoints made visible.

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Everyone claims AI made them faster. This session puts numbers to it. Drawing on real benchmarks, a marketing leader breaks down how to measure AI's impact credibly — the productivity metrics that hold up, the ROI proof points that convince a CFO, and what “good” actually looks like at a mature organization. You'll leave with a measurement framework you can use to separate genuine gains from activity theater.

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Ten-times content output makes a great headline. This case study tells the real story behind one. A team that genuinely scaled production walks through the numbers, the workflow that got them there, and the tradeoffs they accepted along the way — where quality held, where it strained, and what they’d do differently. An unvarnished look at what 10x actually costs and delivers.

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If AI can execute almost anything, human value moves to what it can't do: taste, judgment, and direction. This panel digs into the parts of the job becoming more important, not less — setting the standard, making the calls models can't, and steering creative vision when production is nearly free. A thoughtful conversation for leaders rethinking what to hire for, and what to develop, in an AI-executed org.

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Text was the easy part. In this session, a hands-on practitioner walks through the real workflows behind AI-generated image, video, and audio at production scale — the tools, the pipelines, and the quality bar to hold across formats. You'll see how multimodal generation folds into everyday output without the process buckling under its own complexity, plus a candid read on where the technology is ready to trust and where it still needs a human hand.

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The rapid adoption of AI has created a new challenge for organizations: not simply choosing the best tools, but understanding what they already have, why they have it, and whether it is delivering meaningful value. As experimentation expands across teams, organizations can quickly find themselves with overlapping capabilities, unclear ownership, inconsistent governance, and limited visibility into what employees are actually using.

This session will explore a practical framework for moving from AI experimentation to intentional AI portfolio management. We’ll look at how leaders can assess their AI landscape, distinguish valuable experimentation from unnecessary duplication, establish clearer ownership and governance, and evaluate tools based on business need, adoption, and measurable value. The goal isn’t necessarily a smaller AI stack. It’s a more purposeful one.

Attendees will learn how to:

• Assess the AI ecosystem: Map tools, agents, content, workflows, and overlapping capabilities to understand what exists and where rationalization is needed.
• Evaluate value, not hype: Start with the business problem and define the outcomes that determine whether an AI investment is actually working.
• Make intentional investment decisions: Determine what should scale, coexist, consolidate, or retire based on business need, adoption, overlap, governance, and value.
• Establish ownership and governance: Define who owns AI solutions, their underlying knowledge, decision rights, and ongoing lifecycle management.
• Govern without stopping experimentation: Create enough structure to scale successful AI while preserving room to test, learn, and innovate.

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On-brand AI output doesn't come from better prompts, it comes from better grounding. This case study reveals the source of truth behind consistently on-brand work: a structured canon of brand voice, messaging, and competitive context the models draw from every time. You'll see how one team built and maintains its grounding engine, and why this infrastructure is fast becoming the difference between AI that sounds like you and AI that sounds like everyone.

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Agentic go-to-market has moved from concept to production. This case study shows real organizations running real agent-driven workflows — how the work is structured, where humans step in, and the results it's producing.

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AI-native search is rapidly evolving from a place where consumers retrieve information into an agentic customer journey where they turn to AI for research, comparisons, recommendations, and eventually transactions. In this new environment, winning is no longer just about achieving a top search ranking or generating a single click. Brands must earn a place in the AI’s answer — and build the proof points, product data, reputation, and pathways that enable confident, accurate recommendations. This keynote explains why Generative Engine Optimization is not simply “SEO for chatbots,” but a new GTM discipline at the intersection of brand strategy, product marketing, data infrastructure, content, commerce, and media.

Drawing on a product-marketing and ad-tech perspective, Patricia Clark will introduce the GEO Control Tower: a practical operating model for CMOs and marketing leaders to manage earned answer visibility, third-party proof, machine-readable product and brand data, AI-native measurement, and emerging paid AI opportunities. 

Key takeaways:

• Move beyond rankings and clicks to win citations, recommendations, and actions in AI-native search.
• Learn how agentic customer journeys are reshaping research, comparison, and purchase decisions.
• Build “AI confidence” through clear positioning, accurate product data, credible proof, and frictionless conversion paths.
• Use the GEO Control Tower to align content, product marketing, PR, data, web, sales, and media.
• Leave with a six-step playbook to measure share of answer and turn AI visibility into qualified demand.

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AI as the connective tissue, not just a tool. This case study shows one operator running content, paid media, SEO, and CRM through a single AI-driven system, with specifics on what stayed human, what didn't, and the pipeline results it produced.

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The ad platforms now run themselves, so the craft is operating inside the black box. This case study covers what levers you still control, how to feed the algorithms well, and how to hold AI-native campaigns accountable.

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AI breaks the old tradeoff between personalization and effort. This session explores dynamic segmentation and self-orchestrating journeys that deliver individualized experiences at scale, and where the line sits between relevant and unsettling.

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When AI answers without sending a click, attribution breaks. This case study tackles how to value visibility you can't track to a session, what proxies actually work, and how to defend budget for presence in AI results.

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Market intelligence no longer waits for a quarterly deck. This session looks at the systems teams use to sense competitive signals in real time and respond at the same speed, without drowning in noise.

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Reporting eats hours that could go to strategy. This live demo shows a working AI reporting stack that pulls data, surfaces what changed, and drafts the narrative — run end to end, with the tools exposed.

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