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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Whether you're a team of one wearing every hat or part of a fully staffed department with dedicated strategists and creatives, AI is blurring the line between the two jobs. This shop talk invites a candid conversation about what happens when strategists can generate creative, creatives can generate strategy, and teams of every size are left deciding who (or what) should own the idea. Bring your own experiences, because we'll be putting some of the room's toughest questions up for debate on where AI earns its place and where it falls flat.

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As AI agents take on more marketing execution, teams need to rethink how they organize and oversee the work. Who defines the objective? How does a brief become an actionable workflow? Which decisions can an agent make, and when does a person need to review, approve, or intervene?

This case study explores an operating model for marketing with AI agents, following work from initial direction through execution and review. It examines how responsibilities, team routines, and accountability change as agents become part of everyday workflows. Attendees will learn how to keep human judgment connected to execution and establish clear ownership as they move from individual experiments to repeatable operations.

Key takeaways

• Translate marketing objectives into clear briefs and boundaries for AI agents.
• Define responsibilities across business owners, agents, and human reviewers.
• Identify where approval, escalation, and intervention belong in a workflow.
• Adapt team routines to review agent activity and maintain accountability.
• Build a repeatable operating model that connects AI execution to business outcomes.

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Marketing teams today juggle a patchwork of tools just to move a single piece of content from idea to publish: a doc for the brief, a separate AI tool for drafting, Slack threads for approval, and yet another platform to ship it. In this live demo, I'll walk through an end-to-end content pipeline built entirely on AI agents inside the tools your team already uses, showing how a brief becomes a fully drafted, on-brand, reviewed, and published piece without ever leaving your existing stack.

You'll see the pipeline running live. A content brief kicks off an AI draft that follows your brand voice and messaging guidelines, routes automatically into a review and approval cycle with humans still in the loop, and publishes once it's signed off, all tracked and auditable end to end. I'll also cover where these workflows break down in practice and how to design around AI's real limitations rather than the hype.

Key Takeaways

• How to structure a content brief so AI-generated drafts consistently match your brand voice and messaging guardrails
• Building automated approval gates that keep humans in the loop for quality and compliance without slowing down cycle time
• Turning a one-off AI workflow into a governed, repeatable pipeline your whole team can reuse
• Practical patterns for wiring content pipelines into tools you already use, like Slack, Docs, and GitHub, instead of adopting a new platform
• Where these workflows tend to break down today, and how to design around AI's real limitations

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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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AI makes it easy to publish 10x more content. The harder question is whether you should. More of the wrong content dilutes your message, cannibalizes your best performers, and burns budget without moving results.

This case study goes behind the scenes of how L.L.Bean and American Eagle use AI to move from insights to content at scale. It then shows how the same principles apply whether you’re selling to shoppers or to buying committees. You’ll see the real process, numbers, and tradeoffs, including:

• For B2C: How enterprise retailers turn shopper intent, product data, and AI discovery insights into relevant content across thousands of products
• For B2B: How to uncover unmet buyer questions, build content for specific industries and buying stages, and scale thought leadership without flooding the market with generic AI articles
• For both: How to stay differentiated when everyone uses the same tools, what to automate versus where human judgment matters, and how to measure whether any of it is working

Walk away with a practical framework for deciding what to create, what to skip, what to automate, and what to measure.

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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 AI becomes useful when it stops being a chat interface and starts completing real work. Giving a model tools is the easy part. The harder questions are operational: What should an agent actually own? Which steps should remain deterministic? Where does model judgment belong? What boundaries should be enforced in code rather than left to a prompt? When should a human intervene? And how do you know whether delegation is producing better business outcomes rather than simply more output?

Drawing on his experience operating AI inside Amazon and building production AI systems and software at Surmado, Luke F. Walton will break down a practical model for moving go-to-market work from AI assistance toward supervised and delegated execution. He will ground the framework in real operating patterns from global paid media and from Surmado, where an AI agent is entrusted to operate and change customer-facing production websites. The session will show how to decompose work, separate deterministic logic from model judgment, set permissions and escalation paths, enforce critical boundaries structurally, preserve reversibility and human oversight, and evaluate whether the resulting system is actually worth delegating to.

Key Takeaways

• Learn how to break a GTM workflow into deterministic steps, model judgment, and human decisions
• Examine what changes when an AI system is trusted to operate a customer-facing production asset instead of just recommending actions
• Determine when to enforce a boundary in code, architecture, permissions, or CI rather than relying on prompt instructions
• Calibrate the right level of AI autonomy based on stakes, reversibility, observability, and escalation paths

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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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