Generative AI’s (GenAI) ability to streamline marketing and boost productivity has fueled rapid adoption, with eight in ten brands and agencies using GenAI to some extent. However, this rapid uptake has already made the technologies less of a differentiator and more of a standard tool. 91% of agencies and 75% of brands in the UK and US are already incorporating it into their creative workflows, indicating that its role has evolved beyond idea generation and it is now powering the entire content supply chain—from ideation and design to production, pre-testing, in-flight optimization, and campaign analysis.
While this advancement has improved efficiency by reducing production times and costs, it has also amplified the challenge of standing out with differentiated content for brands and demonstrating unique value in RFPs for agencies.
Addressing Key Challenges in AI Adoption
While the potential of AI is vast, the tech is still in its early stages of discovery and development so to unlock its full value in the content ecosystem, the starting point is clear: identify a specific challenge and use case, and have a plan to deploy it more effectively than your competitors.
Defining the goal is critical for understanding how AI can meet marketing objectives. For example, if the goal is enabling marketing teams to create scaled ad variants from existing assets, a basic automation tool may suffice. Alternatively, agencies aiming to pre-test for creative quality or optimize performance in-flight may require more sophisticated AI capable of analyzing creative attributes and identifying what drives the best outcomes. While new AI solutions will continue to emerge, the key to success isn’t adopting every tool, it’s identifying the specific purpose behind your AI use and aligning it with broader marketing goals. In other words, start with the why and the challenge you want to address.
- Creative Quality: Seventy-five percent of agencies and brands say that, at today’s level of sophistication, genAI still requires human input to produce on-brand, fit-for-purpose creative. So, while the appetite for AI-driven efficiencies continues to grow, oversight—whether through trained AI models or human supervision—will remain necessary for the foreseeable future, so it’s not hands-off just yet.
- Homogeny Risk: GenAI’s reliance on shared data and best practices can result in content that blends in rather than stands out. With many agencies using the same tools and datasets, the challenge is to avoid generic creative outputs that fail to capture consumer attention and meet performance goals. As Forrester aptly noted, quoting The Incredibles, “When everyone is super, then no one will be.”
- Operational Integration: Seamlessly incorporating AI into existing workflows is a significant hurdle. With multiple new AI capabilities being introduced into the ecosystem, sometimes side-by-side, marketers must ensure that these tools add value at every stage of the content lifecycle while maintaining streamlined operations
At this critical juncture, teams face a pivotal decision: continue chasing the latest innovation for fleeting advantages or strategically harness AI to consistently elevate creative outputs and build long-term value.
To overcome these challenges and harness AI for growth, it’s crucial to build a cohesive, interconnected framework that prioritizes both efficiency and creativity.
Building an AI-Driven Content Ecosystem
The beauty of AI lies in its versatility—it can be deployed by brands, agencies, or even individual marketers to amplify their creative and operational efforts. However, this same flexibility can inadvertently create silos within tech stacks, preventing the full potential of AI from being realized. To fully optimize your marketing efforts, AI must work seamlessly across functions, with outputs and learnings shared across teams and departments. This approach is essential for improving overall effectiveness and unlocking the full value of an AI-enabled content ecosystem.
Companies like Mondelez are leading the way by demonstrating how AI can tackle creative challenges, whether through building in-house engines, leveraging external technologies, or combining both approaches. Their example underscores the importance of taking the time to vet technologies and identify opportunities to apply learnings across the board. By streamlining workflows, enhancing creative and media decision-making, and strengthening collaboration across departments, organizations can create an ecosystem that supports the entire content lifecycle with minimal friction and maximum impact.
Understanding the Critical Role of First-Party Data
GenAI models are only as effective as the data they’re trained on. Industry leaders like OpenAI have acknowledged that model progress is nearing a plateau, prompting a shift toward integrating proprietary data to elevate the quality and relevance of outputs.
For creative work, incorporating unique assets—such as brand-specific visual elements, messaging patterns, recent performance information, and audience engagement trends—into AI-powered creative tools allows brands to bridge the gap between automation and authenticity. This approach can effortlessly and more consistently, produce tailored, content that is on-brand and on-point with audiences.
Anonymized first-party creative data enables brands to maintain consistency and distinctiveness while responding to evolving consumer behaviors. The result is not only engaging content— it’s also strengthened brand equity and creative integrity. By building a foundation on proprietary data, brands can truly elevate their approach beyond what off-the-shelf solutions offer, creating a competitive edge that transforms their marketing impact.
Brands must start building their first-party data asset now to move beyond generic outputs and lay the foundation for a differentiated creative strategy. Those that embrace this opportunity will set the standard for the future of creative innovation.
The Future of AI for Marketers
AI’s role in marketing and content creation is rapidly evolving and achieving its full potential will remain a moving target. Every brand has unique first-party creative data at its fingertips; it just needs to be accessed. Tapping into this data transforms content creation into a process that delivers both differentiation and authenticity, helping brands stand apart in increasingly competitive markets.
However, AI will always need to be guided by context and quality data. The future is not about letting AI run on autopilot but about strategically amplifying the creative process. By combining smart AI deployment with robust data strategies, brands can elevate human creativity, drive efficiency, and deliver on-brand, impactful campaigns that truly resonate.
By Alexandra Clark, Vidmob Global Client Partner