Key Takeaways:
AI’s role in media isn’t new. However, the rise of generative AI presents powerful new opportunities for advertisers to harness the technology to drive impact for their brand or clients.
While almost all marketing and advertising professionals report using generative or agentic AI at work at least once a month, only a third use it every day. This reflects the barriers limiting wider adoption, including insufficient data readiness as well as inadequate skills and training, unclear strategy, and concerns about reliability.
Overcoming these hurdles creates real separation from competitors, with media strategy emerging as the biggest unlock due to the speed and performance the technology enables. Successful adoption hinges on understanding where AI can provide the most value to media strategists. To that end, this article explores key use cases for AI in media strategy as well as recommendations for successful implementation.
Three use cases stand out when it comes to AI’s applications on the media strategy side:
AI has long been used in media via machine learning algorithms that analyze large datasets to optimize programmatic ad buying, predict audience behavior, and automate bidding strategies. Today’s AI tools create new opportunities to activate advanced data analysis—helping media strategists quickly structure and clean inputs like historical performance, first-party data, brand health studies, and MMM outputs, which often come in the form of massive unwieldy Excel sheets.
Similarly, AI tools can help media strategists quickly gather and synthesize information around audience, competition, and trends in the marketplace to ground themselves in the context of their business or their client’s business. These tasks, which have historically taken media strategists days and weeks to complete, can now be consolidated down with the help of AI.
AI can also serve as a powerful brainstorming partner, helping media strategists generate thought starters and new ideas that they wouldn’t have thought of on their own. This application resonates widely: Nearly 80% of marketers report using AI for brainstorming, making it the most common AI use case, according to one survey.
Which tools and platforms are advertisers using for data analysis, information synthesis, and brainstorming? ChatGPT appears to be far and away the most commonly used tool, with 88.6% of marketers reporting that either they or their organization use it. Gemini (45%) and Copilot (41%) round out the top three most-used platforms.
While AI’s applications in media strategy will continue to evolve, these three use cases—data analysis, information synthesis, and brainstorming—have already proven their value and should be part of every media strategist's toolkit.
Of course, to make the most of AI’s expanding media strategy capabilities, it takes more than just having the right tools: The way marketers and marketing teams harness AI in service of these use cases has an enormous impact on the technology’s effectiveness. To realize this potential, individual marketers need to refine their approach to AI, and leaders must establish the right infrastructure and practices across their teams.
One of the most critical considerations for advertisers to keep in mind when using AI to assist in any marketing function is the technology’s tendency to hallucinate. 35% of brand marketers cite reliability concerns, especially those around hallucinations, as the most significant hurdle for marketing AI implementation. One study found that close to half of marketers experience AI inaccuracies multiple times a week, and over 70% say they dedicate multiple hours per week to fact-checking as a result. Recent tests of six major LLMs found that ChatGPT tended to hallucinate the least and produced the highest percentage (59.7%) of fully correct answers, while Grok had the highest error rate (21.8%) and the lowest proportion of fully correct answers (39.6%). Because of this tendency to hallucinate, media strategists must validate all AI outputs to ensure accuracy, demonstrating how human expertise and critical thinking will continue to be indispensable to the media buying process.
Media strategists will also be essential to ensuring that any AI-assisted work produces differentiated, compelling recommendations rather than generic outputs. It’s easy to look at AI tools as a shortcut to which we can outsource work. But they’re most impactful as collaborative partners, blending AI’s computational power with human creativity. Since AI tools like Claude and Gemini train on similar data, their outputs tend toward the generic—and the last thing any marketer wants is a media plan that mirrors their competitor’s. Turning standard AI outputs into expert-level recommendations requires that strategists embed their brand knowledge, category perspective, competitive insight, and planning approach into the process. It’s also what ensures marketers can defend their recommendations and confidently explain the strategy behind them, rather than presenting AI outputs they don't fully understand.
Finally, the way media strategists go about prompting the AI tools they work with is a critical differentiator. This includes everything from incorporating guardrails around hallucinations to instructing tools on how they’re expected to “think” and communicate with the prompter. For example, LLMs are trained to be polite, so they’re rarely going to give negative feedback. This is something marketers must actively work around in their prompting as well as in their evaluation of AI outputs. Equally important is anchoring prompts in brand context—clear audiences, KPIs, and competitive dynamics—so outputs optimize for your goals, not generic playbooks. The closer the input reflects your real business nuances, the more differentiated the recommendations.
While human oversight, subject-matter expertise, and thoughtful prompting are critical, they’re not the sole drivers of distinctive AI-powered strategy. The use of proprietary, brand-specific data to augment the broader LLM datasets is foundational to unlocking AI's full strategic potential, enabling it to generate recommendations that reflect a brand's unique audiences, competitive positioning, and historical performance rather than broad, generalized patterns. Without proprietary data, even the most sophisticated AI tools will produce strategies that could belong to any brand in any category.
Leaders play a critical role in ensuring their media strategy teams are implementing AI effectively. This includes regularly discussing AI applications with their employees and actively encouraging its use. It also means establishing expectations around how teams leverage the technology and setting guardrails around utilization, so that employees don’t default to thinking of it as an “easy button” and risk losing the human skills that are so important to successful AI use.
Leaders can also empower their teams by working to operationalize AI. This might mean investing in differentiated or custom tools for media strategy purposes to complement their team’s use of tools like ChatGPT and Claude. Specialized tools can offer capabilities tailored to media planning workflows, proprietary data integrations, and industry-specific insights that general-purpose AI tools lack—all of which will help to further differentiate and strengthen AI outputs.
One key aspect of operationalizing AI effectively—and making use of custom AI tools—is data readiness. Because AI outputs are only as good as their inputs, advertisers need large volumes of high-quality data to fuel high-quality media channel recommendations, audience insights, budget allocation strategies, and any other tasks AI tools are used for. And, to make those outputs as differentiated and brand-specific as possible, organizations need ready access to clean, comprehensive data across channels and campaigns.
Currently, most organizations lack the data readiness to fuel their AI use in this way: Only a fifth of marketers call first-party data “foundational” to their organizations’ AI initiatives, and one-third report that first-party-data plays little or no role in their organizations’ AI initiatives. To truly make the most of AI, leaders must treat data infrastructure as a strategic priority, investing in systems that collect, organize, and make first-party data accessible for AI applications.
All in all, leaders who establish clear usage guidelines, invest in the right tools, and build robust data infrastructure will position their teams to extract maximum value from AI in media strategy.
The trajectory of AI in media strategy points toward increasing automation. However, humans will continue to play a key role in managing AI tools and ensuring their organizations are maximizing their AI outputs.
As AI models improve and prove their capabilities through consistent results, marketers may become more comfortable reducing human intervention. For now, however, the winning approach balances AI’s computational power with human strategic judgment. Media strategists who master this collaboration—validating outputs, injecting expertise, and continuously refining their AI tools—will lead the field as the technology matures.
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Looking for more insights on AI’s impact across marketing? We surveyed professionals at leading brands and agencies to uncover adoption patterns, performance gains, and roadblocks to implementation. Check out AI and the Future of Marketing for comprehensive findings on the forces driving—and hindering—AI integration in marketing.
