Speakers at the Association of National Advertisers AI ’26 event, “Getting Down to Business,” made one thing clear: AI has moved past experimentation. Across sessions, brand leaders described how AI is already reshaping how consumers discover brands, how marketing work gets done and how content is created and scaled. The event’s focus wasn’t on shiny tools, however. It was more about the operational changes companies are making to put AI into production (content systems as opposed to content volume).
Across panels, speakers pointed to a consistent shift: advertising is increasingly judged by whether it delivers answers and outcomes, not just traffic.
AI-powered interfaces have changed how people search, browse and decide. The availability of GenAI often reduces audience reliance on websites and traditional, linear sales journeys. Case studies from Diageo, Clorox and Mondelez highlighted that the biggest gains showed up when AI is treated as a core capability (with process, governance, and measurement around it), rather than a bolt-on tool.
Here are a few highlights brand managers and peer marketing leaders should take away.
I. Treat AI as a capability shift, not a tool upgrade
A recurring theme was that AI’s impact comes from workflow redesign. Adoption leaders aren’t just running isolated pilots. They’re connecting insights, creative, production and media into AI-assisted systems.
Diageo shared a practical starting point to build capability in three priority zones: 1) Content and content systems; 2) research, insights and data; and 3) differentiated experiences. The Diageo team is also approaching adoption as a broader change-management effort.
Their brand has found success around a few core principles: make AI immediately useful, meet people with empathy, make AI experiences interesting so they spread, make adoption stick through habits; and build a flywheel that compounds over time.
II. Optimize for answers, not clicks
AI is changing discovery by delivering guidance and recommendations directly in search summaries and chat-based experiences. This has a direct impact on website visits. As a result, brands are shifting attention from driving clicks to earning presence inside answer environments—where creative, UX, and media work together to deliver content that is useful and decision-supportive.
The downstream effects showed up across the stack:
- Presence matters more than clicks
- SEO becomes “answer optimization”
- Creative behaves more like a product (each message helps customers decide)
- Attribution becomes harder as audience journeys fragment
III. Build content systems, not content volume
Nicole Thomas, Director of Generative AI at Clorox, shared a case study in scaling personalization through modular content and virtual production. Thomas described the company’s move from isolated GenAI experiments toward an always-on “relevance engine” that supports tailored creative by moment, format and channel.
Clorox pairs GenAI with mix-and-match assets and AI-generated environments designed for LED-wall studio shoots. By using modular foreground and midground elements, the brand reduces post-production. Clorox reported producing 50+ reusable video moments in two days—enough for 11 distinct 15-second spots across 13 locations and seasonal refreshes—while saving an estimated $700K–$900K. Best of all, videos saw higher audience traction through more personalized messaging.
IV. Build an AI marketing operating system with governance baked in
Mondelez positioned GenAI as a core business priority for the brand’s global digital transformation. The Mondelez presentation emphasized that scale depends on the surrounding infrastructure: data strategy, governance, responsible AI, change enablement, testing and measurement.
Mondelez also framed agency partners as co-builders, not just executors. This partnership is critical to launching and improving a platform at enterprise scale. For instance, Mondelez uses an enterprise platform co-built with Publicis/Digitas to support content creation and optimization across the brand’s portfolio.
V. The bottom line for content systems
ANA AI ’26 underscored that AI advantage is showing up where brands are operationalizing—through redesigned workflows, modular content systems and platform-level governance. The strongest examples weren’t about experimenting with tools. They were about building repeatable systems that improve speed, relevance and performance without sacrificing brand integrity or trust.
VI. The Pace POV
At Pace, we’ve been applying the same lessons shared at ANA AI ’26 by treating AI as an operational capability, not a novelty. We use it to synthesize large volumes of research and performance data, manage and navigate large libraries of content and information and to pressure-test strategic thinking. We also use AI in quality checks or as a virtual auditor that helps confirm deliverables meet requirements before work ships.
What hasn’t changed is the source of relevance: our creative remains led by people, grounded in lived experience and real relationships with audiences. This how we make sure the work doesn’t just scale—it resonates.
