Half of all major brand technology decisions made in the back half of any fiscal year are shaped by what happened at the halfway point. Right now, three signals are loud enough to act on: Viant’s programmatic AI evolution, OpenAI’s enterprise marketing integrations, and Coca-Cola’s continued AI-generated campaign rollout. The brand technology trend stack for H2 is taking shape. Here’s what it means for your budget.
Three Signals That Are Defining the H2 Tech Stack
Viant Technology’s push into AI-powered audience targeting has accelerated significantly. Their AIP (Artificial Intelligence Platform) now integrates household-level identity data with predictive bidding logic that bypasses traditional cookie-based segments entirely. For brand marketers running programmatic, this is not incremental. It represents a structural shift in how media dollars reach verified audiences without third-party data reliance.
Meanwhile, OpenAI’s partnerships with enterprise marketing platforms have moved beyond novelty. Brands are now embedding GPT-4o-class models directly into campaign brief generation, creative iteration workflows, and performance analysis. The speed advantage is real: what took a creative team three days of iteration now cycles in hours. The risk is equally real, though — brand voice consistency requires governance frameworks that most marketing organizations haven’t built yet.
Coca-Cola’s AI campaign activity remains the most visible proof point in the industry. After their AI-generated holiday spots generated polarized audience reactions, the company doubled down rather than retreating, incorporating generative AI into real-time personalization across digital OOH, social, and loyalty program touchpoints. The lesson isn’t that AI creative always lands. The lesson is that scaled brands are treating AI as infrastructure, not experiment.
The brands winning with AI right now aren’t the ones running pilot programs. They’re the ones who made a governance decision 18 months ago and have been executing against it ever since.
Where Programmatic AI Is Actually Going
The Viant story matters for a specific reason: it’s the clearest signal that programmatic is no longer a media channel decision. It’s a data infrastructure decision. Brands that treat DSP selection as a procurement exercise are leaving significant performance upside on the table.
Viant’s identity graph approach, built on connected TV and household-level matching, gives media planners a path to reach the same consumer across streaming, display, and audio without relying on probabilistic mobile IDs. creator channel inventory is increasingly plugging into these same programmatic pipes, which means the programmatic/influencer divide is narrowing faster than most planning teams have adjusted for.
For brand marketers, the practical implication is this: your H2 media plan should include a conversation with your DSP partners about identity resolution capabilities, not just CPM rates. The cost of switching DSPs mid-year is high. The cost of running a full H2 on an underperforming identity stack is higher.
OpenAI’s Enterprise Play and What It Costs Brands
Let’s be precise about what “using OpenAI in marketing” actually means in practice, because the gap between what’s happening in pilot programs and what’s in production workflows is still significant.
At the enterprise level, brands are deploying OpenAI integrations in three primary ways. First, creative production acceleration: generating multiple creative variants from a single brief for A/B testing at scale. Second, audience intelligence: using GPT-class models to synthesize first-party data signals into natural-language audience insights that non-technical brand managers can actually use. Third, compliance review: running draft copy through custom-tuned models trained on brand guidelines and regulatory requirements before human review.
That third use case is underreported and undervalued. Brands operating in regulated categories (finance, pharma, alcohol) are finding that AI-assisted compliance pre-screening reduces legal review cycles by 30-40%. That’s not a creative story. That’s an operational efficiency story, and it belongs in your business case for AI tooling investment.
The debate about AI versus human creative judgment is ongoing — and worth following closely. The Cannes Lions AI debate surfaced genuine tension around creative minimums that brand teams shouldn’t dismiss. The consensus position among senior creative directors is that AI handles volume and variation; human judgment handles brand truth. Build your workflows accordingly.
What Coca-Cola Actually Proved
Coca-Cola spent years being the brand that other marketers cited as the gold standard for emotional advertising. Now they’re the brand other marketers cite when discussing AI creative risk. Both things are true simultaneously, and that tension is instructive.
Their AI-generated spots did not perform universally well on sentiment metrics. But the company’s infrastructure investment behind those campaigns, the real-time personalization layer, the generative asset pipeline, the multi-market localization engine, is what competitors should be studying. According to reporting from eMarketer, brands deploying generative AI in creative workflows are reducing per-asset production costs by up to 60% at scale.
The more important signal from Coca-Cola: they have a dedicated AI creative governance team that sits between the generative tools and the market-facing output. That team doesn’t create. They review, calibrate, and approve. That’s the operational model worth replicating, regardless of your category or budget size.
Related: AI automation vs. authenticity is a tension that doesn’t resolve itself with better tooling. It resolves with better process design.
The Efficiency Divide Is Becoming a Competitive Gap
Here’s the structural problem for mid-market brands. The technology advantages now available to enterprise marketers through platforms like Viant, OpenAI enterprise tiers, and Adobe Firefly for Enterprises aren’t accessible at the same cost or integration depth for teams running $5-20M annual programs.
That gap is widening. According to Statista, global AI marketing software investment is projected to exceed $107 billion annually within two years. The majority of that capital is flowing to enterprise-tier deployments. Mid-market brands that don’t establish a credible AI tooling strategy in H2 will find themselves operationally two to three years behind by the time they’re ready to scale.
The efficiency divide between AI and manual programs is already visible in creator marketing. It’s about to become equally visible in programmatic, content production, and brand measurement. The brands that move in H2 on even one of these vectors will have meaningful data advantages heading into the next planning cycle.
Mid-market brands don’t need an enterprise AI stack. They need one well-integrated AI capability that improves a workflow they run every single week.
Where Your H2 Technology Investment Should Actually Go
Given the signals from Viant, OpenAI, and Coca-Cola, here’s a prioritization framework for brand technology investment in H2:
- Identity resolution infrastructure: If your programmatic is still running on third-party cookie segments, the window to fix this is closing. Evaluate DSP partners on identity graph quality, not just reach.
- AI-assisted creative governance: Before expanding generative AI creative use, build the review layer. One brand voice incident at scale costs more than the governance infrastructure.
- First-party data activation: OpenAI integrations only compound in value if you have clean, consented first-party data to feed them. Data hygiene is unglamorous. It’s also foundational.
- Measurement modernization: Incrementality testing and media mix modeling are being rebuilt around AI-native methodologies. If your measurement approach is more than two years old, it’s likely misattributing a significant share of your spend.
- Creator-programmatic integration: As noted above, creator inventory and programmatic are converging. Brands that connect these channels in their planning model will capture efficiency gains their competitors won’t see until next year.
For context on how holding company AI efficiency models are reshaping the agency side of this equation, the structural changes underway on the service side have direct implications for how brands should be evaluating their agency partners’ technology capabilities right now.
The AI sentiment platforms entering the creator campaign space are also worth a serious evaluation pass. Real-time sentiment adjustment is no longer a luxury for large campaigns — it’s becoming table stakes for any program where creative risk is elevated.
For regulatory and compliance reference as you build AI governance frameworks, the FTC’s guidelines on AI-generated content and endorsements are the baseline. For brands with EU exposure, the ICO’s AI guidance adds additional compliance considerations that need to be built into your content review process from the start, not retrofitted after launch.
The technology stack for H2 is not about adding more tools. It’s about selecting fewer, better-integrated capabilities and building the human governance layer that makes them defensible. Start with identity resolution or AI creative governance — whichever your current infrastructure is weakest on — and build from there before Q3 planning locks.
FAQs
What is the most important brand technology investment for H2?
Identity resolution infrastructure is the highest-leverage investment for most brands right now. With third-party cookie deprecation reshaping programmatic, brands that have clean, household-level identity data will outperform those still relying on probabilistic targeting. DSP partners like Viant have built AI-driven identity graphs specifically for this environment, making DSP selection a data infrastructure decision, not just a media cost conversation.
How are brands using OpenAI in marketing workflows?
Enterprise brands are deploying OpenAI integrations primarily for creative variant generation, audience intelligence synthesis, and compliance pre-screening. The compliance use case is particularly valuable in regulated categories, where AI-assisted review can reduce legal cycle times by 30-40%. The key is pairing these tools with governance frameworks that preserve brand voice consistency and meet regulatory requirements.
What did Coca-Cola’s AI campaigns actually prove for brand marketers?
Coca-Cola demonstrated that AI creative infrastructure, including real-time personalization, generative asset pipelines, and multi-market localization, can operate at scale when supported by a dedicated human governance layer. The campaigns’ mixed sentiment reception shows that AI creative doesn’t always land, but the operational efficiency and scale advantages of the underlying infrastructure are significant and worth replicating.
How should mid-market brands approach AI marketing tooling without enterprise budgets?
Mid-market brands should identify one high-frequency workflow, such as creative variant production or campaign brief development, and integrate one well-supported AI tool deeply into that process rather than spreading investment across multiple platforms. Building internal governance and review processes before scaling AI output is more important than the tooling selection itself. Even a single well-executed AI capability generates compounding data and process advantages over time.
How are programmatic and creator marketing converging?
Creator channel inventory is increasingly being trafficked through programmatic pipes, meaning brands can now apply the same audience targeting, frequency management, and measurement methodologies to creator placements that they use for display and video. Brands that integrate creator and programmatic planning into a unified channel model will capture efficiency gains and attribution clarity that siloed programs cannot achieve.
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