The DAC Blog

As CMOs take on greater responsibility for AI adoption and the data decisions that come with it, it’s no longer enough for media agencies to simply buy and optimize media efficiently. The best partners are helping brands harness AI to drive measurable business outcomes, turning innovation into real impact felt across the business. Many marketing leaders I speak with feel both excited and overwhelmed by this shift. They’re being asked to make calls on data platforms, automation strategies, and creative optimization tools, areas that once sat firmly in the tech department. But it makes sense: marketing has become the most data-rich, fast-moving function in the enterprise. And it now demands agency partners who understand not just audiences and channels, but how to use AI responsibly and effectively to unlock business value. The most effective way to deliver on that promise is by combining data intelligence, rapid innovation, and operational efficiency. When those elements come together in cohesive systems where AI and humans co-create, the result is what we call Generative Marketing Intelligence, a distinct competitive edge in today’s AI-driven landscape. Here’s what CMOs should expect from their media agency as this new reality continues to evolve. 1. AI-oriented data design It’s tempting to think of AI as a kind of magic black box that just needs the right question, but the truth is that the quality, structure, and description of your data will determine how intelligent your AI actually is. AI performs best when it’s armed with enough context to make informed inferences and decisions, but not so much that its “attention span” is overwhelmed by irrelevant facts. In other words, you need to make sure your data isn’t just stored, but understood. New-generation data platforms such as Snowflake are providing excellent tools to address this, thanks in part to a semantic layer that acts as a translator between human language and machine logic. For example, when a marketer asks, “Which campaign drove the highest ROI last quarter?”, the semantic layer helps AI models understand the question and convert it into an efficient, accurate query. Constructing this layer involves defining key business concepts (metrics, dimensions, hierarchies) and then providing sample queries that teach the AI how those concepts relate to one another, bridging the gap between raw data and real-world questions. Dedicated environments like Proove Intelligence can play a valuable role here, helping teams align data engineering with business logic to make AI outputs more relevant, timely, and actionable. 2. AI-powered rapid prototyping AI-powered rapid prototyping has become one of the most important tools for marketing leaders, allowing them to quickly onboard relevant datasets, experiment with new ideas, and build solutions that address immediate business questions without waiting for enterprise-scale deployments. Now, CMOs can move from hypothesis to insight in days instead of months, and test whether a new data source, model, or concept actually delivers value before committing to full implementation. The key is iteration: small, agile tests that reveal what works (and what doesn’t) long before large-scale rollout. That’s only possible when teams fully embrace AI at every layer of development. Developers now use AI tools that write, review, and optimize code in real time, accelerating build cycles and freeing teams to focus on strategy and integration instead of syntax and debugging. Non-developer power users can leverage tools like Cursor to bring AI-assisted development into the hands of marketers, analysts, and strategists, empowering anyone with business acumen to help shape data-driven prototypes directly. Likewise, visualization tools such as Streamlit allow teams to quickly generate interactive dashboards and insights, transforming complex datasets into intuitive, business-ready stories, helping diverse teams see, test, and refine AI-driven insights collaboratively. Working with an internet service provider, for example, DAC integrated broadband coverage data with real estate listings to visualize internet service availability at a hyper-local level. The resulting prototype showed which ISPs were leading in each neighborhood and why, providing actionable intelligence in days, not months. Agencies play a valuable role in enabling this kind of rapid experimentation, particularly when it comes to designing internal systems that securely connect to large language models and enterprise data environments. Tools like Guardrail are designed to blend governance with flexibility, allowing organizations to manage permissions, ensure compliance, and select the most appropriate AI model for each task. By enforcing brand safety, managing API connections, and maintaining auditable workflows, this type of system makes sure prototypes aren’t just fast, but safe, consistent, and ready for real-world use. 3. AI-optimized media Today’s media experts need to apply AI both within and beyond walled garden buying platforms. On one hand, the platforms themselves have become remarkably sophisticated. Their algorithms can manage bids dynamically, respond to real-time user signals, and even assemble creative assets on the fly to deliver highly personalized composite messaging. This native AI is powerful, and when used correctly, it can drive strong performance at scale. But, as I often remind clients, platform automation has its limits. It isn’t always transparent, and its incentives don’t always align perfectly with those of the advertiser. When agencies trust these systems implicitly, they risk handing too much strategic control to black-box algorithms that may prioritize engagement over efficiency, or short-term clicks over long-term brand value.     That’s why agency-owned AI systems need to operate at a higher level of oversight, sitting above the platforms themselves to orchestrate the total mix. These systems analyze cross-platform performance data, adjust allocations dynamically, and provide an independent lens to ensure the best investment decisions are being made. They’re not just performance tools; they’re governance engines that keep AI decision-making accountable. This layer of oversight is also essential for financial accuracy and operational discipline. AI can manage thousands of campaign variables in real time, but if spend pacing or data reconciliation goes off course, even the smartest algorithms can produce waste. Agency systems must ensure that every impression, click, and dollar is tracked, verified, and aligned with business objectives. Tools like IRIS Pulse, our real-time AI monitoring system, can help make this oversight possible. By continuously tracking key performance indicators (KPIs) and flagging significant deviations from plan, these systems enable teams to respond immediately, not after the fact. By combining the automation power of media platforms with the strategic intelligence and accountability of custom-built systems, agencies can create a hybrid model where AI amplifies human expertise rather than replacing it. The resulting efficiency, intelligence, adaptability, and trustworthiness is exactly what CMOs should strive towards in today’s AI-driven landscape. 4. AI-enhanced experience delivery Delivering a great customer experience requires both creative excellence and contextual intelligence. Properly adapting creative for each audience, journey stage, product, location, and media moment can require thousands of variants, and each version needs to feel distinct, relevant, and on-brand. Media agencies have long worked closely with creative partners to plan, test, and execute campaigns across channels, but growing demand for both speed and scale make it increasingly impractical to rely on traditional production methods. AI-driven solutions can help by extending creative’s reach rather than replacing creative talent. These systems enable teams to test, tailor, and optimize creative faster, while staying true to the brand’s visual and emotional identity.               AI is also proving to be a powerful partner in interpreting creative test results. By combining performance data with a qualitative understanding of imagery, tone, and emotional resonance, AI can surface insights into why certain creative outperforms others, adding a new layer of intelligence to the creative process. This is where AI goes beyond production acceleration to become a creative intelligence engine, helping teams learn, adapt, and continuously refine creative concepts and variants. That’s the hallmark of effective experience delivery in the AI era. 5. AI-driven scenario planning Every CMO I speak with is feeling the same pressure: to do more with less. Budgets are tighter, expectations are higher, and every dollar of media spend must clearly contribute to business growth. So, marketers have to be able to demonstrate that investments are going toward the highest-impact activities, but achieving this clarity is harder than ever. Signal loss from privacy regulations, browser changes, and cookie deprecation continues to erode visibility. To stay ahead, CMOs need to bridge two realities: the fast-moving KPIs optimized in the short term, and the broader business impact that builds over time. That starts with identifying statistically reliable short-term signals that strongly correlate with long-term success. Media mix modeling (MMM) provides the foundational, cross-channel view to understand how different marketing levers interact and contribute to overall performance. Thanks to AI, these models can now be developed and updated faster, making them more accessible and actionable. In parallel, incrementality testing isolates the true contribution of specific channels or tactics. Run in a structured, ongoing way, these tests reveal which activities are driving genuine growth, versus those simply capturing existing demand. The magic happens when these approaches are combined. By layering deterministic and probabilistic methods, MMM, incrementality testing, and AI-enhanced attribution modeling, teams can triangulate toward the truth with greater confidence. AI synthesizes these complex data sources, models future outcomes, and identifies the paths most likely to drive sustainable growth. 6. AI-enabled workflow automation AI systems are most effective when they support human–AI co-creation, not full automation. The goal isn’t to remove people from the process, but to give them smarter tools that extend their capabilities and accelerate their impact. Our human expertise remains the compass. It’s what guides the AI, shapes its prompts, hones its outputs and apply creative judgment. That’s why it’s critical to design workflows with clear intervention points: built-in moments for review, approval, and feedback that keep automation grounded in brand integrity and real-world insight. This philosophy is embedded in platforms like our IRIS platform, which includes Spaces, collaborative environments designed for ideation, content creation, data analysis, and campaign planning. These workspaces support complex, cross-functional projects, enabling media specialists, data scientists, and strategists to collaborate seamlessly. Even the AI systems themselves benefit from specialization. Instead of relying on a single model, we orchestrate networks of AI agents, each designed for specific tasks like data cleansing, creative generation, or reporting. This mirrors how modern teams work: interconnected, expert-led, and collectively intelligent. The future of workflow automation lies in this balance: people and machines working in sync, each making the other more effective, and ensuring that automation drives not only efficiency, but also better outcomes. Expect more from your media agency AI isn’t just about marketing automation, but a complete transformation of how marketing operates, from insights to planning to execution and optimization. CMOs are now stewards of that change, and your media agency should be your most trusted partner in making it real. At DAC, that’s exactly what we focus on: combining human creativity and AI intelligence to deliver measurable business outcomes. Let’s talk about how DAC can help your brand harness AI for real growth.