The Transformative Power of Gen AI in Creative & Media Strategy
Tatev Malkhasyan
November 22, 2024
7
minutes read
Generative AI (GenAI) is not just a new technology; it's the force driving the media and creative industries to a significant shift. By 2030, GenAI is expected to contribute $13 trillion to the global economy, reshaping how agencies plan and execute strategies.
This technological revolution is dramatically transforming media planning, creative production, and campaign optimization, offering agencies unprecedented opportunities to deliver superior results for their clients. This article explores how leveraging GenAI can elevate media strategy and why aligning with a forward-thinking partner like AI Digital is essential for success.
Understanding the GenAI Revolution
GenAI represents a paradigm shift in creative and media planning processes. Unlike traditional AI systems that follow predetermined rules, GenAI can create, iterate, and optimize content across multiple formats while enabling sophisticated media planning and optimization capabilities. This technology learns from vast datasets to generate new, original content and strategic insights that maintain consistency with brand guidelines while pushing creative boundaries. According to recent industry studies, agencies implementing GenAI solutions have reported a 40% reduction in creative production time and a 35% increase in campaign performance metrics.
The impact extends beyond operational efficiencies – it's fundamentally reshaping brand-agency relationships. Recent research from Marketing Week reveals that while 95% of marketers believe AI will significantly impact their work, agencies that effectively blend AI capabilities with strategic expertise are seeing stronger client partnerships. Rather than threatening agency relationships, AI is enabling agencies to deliver more strategic value through data-driven insights and enhanced creative capabilities. This evolution reinforces the importance of maintaining human oversight while leveraging AI's capabilities to drive better outcomes.
This strategic integration helps agencies address common challenges such as fragmented data, budget allocation inefficiencies, and maintaining alignment with business-specific KPIs.
Elevating Media Strategies with a Balanced Approach
The Power of Human Intelligence in the AI Era
While technology evolves rapidly, success arises from a balanced integration of AI and human intelligence.
Expert strategists bring nuanced understanding of market dynamics, brand contexts, and consumer psychology that AI alone cannot replicate. This human element provides:
Strategic vision: Deep understanding of business objectives and market dynamics
Creative intuition: Ability to recognize emerging trends and cultural nuances
Emotional intelligence: Understanding of human behavior and consumer motivation
Ethical oversight: Ensuring brand safety and maintaining strategic control
GenAI complements this by analyzing vast datasets to provide insights that inform and refine human-led strategies.
This integrated approach—where human intelligence sets the vision and AI amplifies it—results in campaigns that are not only efficient but also resonate more deeply with audiences.
Customization and Strategic KPI Focus
A key benefit of GenAI lies in its ability to focus media strategies on unbiased and customized KPIs. By moving beyond broad, conventional metrics, agencies can align their campaign goals with unique business outcomes, ensuring that strategies drive meaningful results. This transforms the entire media optimization approach, as traditionally, agencies were following DSP’s goals and KPIs without a word on a table. The DSP-agnostic approach empowers teams to tailor strategies that reflect specific business objectives and adapt them dynamically as campaigns unfold.
Real-Time Adjustments and Predictive Planning
One of GenAI’s transformative aspects is its capability for real-time analysis and strategic adjustment. The continuous loop of feedback, data interpretation, and optimization enables campaigns to stay relevant and effective throughout their lifecycle. Predictive planning tools that learn from past and current data enhance this process, allowing agencies to anticipate trends and adapt strategies with confidence.
Optimized Campaign Performance
The agility GenAI provides is invaluable for media agencies navigating fast-paced markets. With the ability to generate content iterations rapidly, agencies can test multiple creative directions, measure outcomes, and adjust strategies in near real-time. This cycle of creation, feedback, and iteration significantly shortens timelines, allowing campaigns to go live faster and with greater confidence in their effectiveness.
Enhancing Creative Production
The creative process, traditionally requiring weeks of ideation and execution, is being streamlined through GenAI innovation. Here's how:
Rapid prototyping: Generate and test 100+ creative concepts in under an hour, compared to traditional 1-2 week timelines.
Cost efficiency: Reduce creative production costs by up to 60% while maintaining quality.
Scale and personalization: Create thousands of personalized ad variations in minutes, achieving 2-3x higher engagement rates.
Real-World Success Stories
The transformative power of GenAI in media strategy is best demonstrated through the success of industry leaders. L'Oréal revolutionized their digital approach by using GenAI to create over 500 unique ad variations across their luxury brands, including Lancôme and YSL Beauty. This resulted in a 45% increase in click-through rates and slashed creative production time from weeks to days across 23 countries.
L’Oreal’s Success with GenAI in media
Similarly, Nike's implementation of GenAI for dynamic creative optimization generated thousands of personalized ad variations based on real-time factors like weather conditions and local events. The result was impressive: a 3.2x return on ad spend and 52% higher conversion rates compared to standard campaigns.
Nike’s Success with GenAI in media
These successes highlight how combining GenAI technology with human expertise – the cornerstone of AI Digital's approach – delivers transformative results while maintaining strategic control and brand consistency.
Preparing for Future Challenges with Strategic AI Adoption
The media landscape continues to evolve, bringing challenges such as data privacy regulations, fragmented digital platforms, and complex audience behaviors. GenAI provides agencies with the tools to navigate these complexities, offering capabilities like predictive analytics, real-time optimization, and data consolidation.
However, the true power of GenAI is unlocked when agencies combine its technical capabilities with strategic human oversight. This approach fosters a culture of continuous learning and adaptation, where technology serves to empower creative professionals and strategic leaders alike.
Laying the Foundation for Future Success
The integration of GenAI in media strategy represents not just a technological advancement but a fundamental shift in how we approach creative and media planning. However, success in this new era requires more than just adopting new tools – it demands a strategic partner who understands how to harness these technologies while maintaining human oversight and strategic control.
As we look to the future, the winners in this space will be those who can effectively combine the power of GenAI with human creativity and strategic thinking. AI Digital stands ready to help media agencies navigate this transformation, ensuring they remain at the forefront of innovation while delivering measurable results for their clients.
By embracing this balanced approach – where artificial intelligence enhances rather than replaces human intelligence – media agencies can unlock new levels of creativity, efficiency, and performance. The future of media strategy is here, and it's built on the powerful combination of human expertise and AI innovation.
Blind spot
Key issues
Business impact
AI Digital solution
Lack of transparency in AI models
• Platforms own AI models and train on proprietary data • Brands have little visibility into decision-making • "Walled gardens" restrict data access
• Inefficient ad spend • Limited strategic control • Eroded consumer trust • Potential budget mismanagement
Open Garden framework providing: • Complete transparency • DSP-agnostic execution • Cross-platform data & insights
Optimizing ads vs. optimizing impact
• AI excels at short-term metrics but may struggle with brand building • Consumers can detect AI-generated content • Efficiency might come at cost of authenticity
• Short-term gains at expense of brand health • Potential loss of authentic connection • Reduced effectiveness in storytelling
Smart Supply offering: • Human oversight of AI recommendations • Custom KPI alignment beyond clicks • Brand-safe inventory verification
The illusion of personalization
• Segment optimization rebranded as personalization • First-party data infrastructure challenges • Personalization vs. surveillance concerns
• Potential mismatch between promise and reality • Privacy concerns affecting consumer trust • Cost barriers for smaller businesses
Elevate platform features: • Real-time AI + human intelligence • First-party data activation • Ethical personalization strategies
AI-Driven efficiency vs. decision-making
• AI shifting from tool to decision-maker • Black box optimization like Google Performance Max • Human oversight limitations
• Strategic control loss • Difficulty questioning AI outputs • Inability to measure granular impact • Potential brand damage from mistakes
Managed Service with: • Human strategists overseeing AI • Custom KPI optimization • Complete campaign transparency
Fig. 1. Summary of AI blind spots in advertising
Dimension
Walled garden advantage
Walled garden limitation
Strategic impact
Audience access
Massive, engaged user bases
Limited visibility beyond platform
Reach without understanding
Data control
Sophisticated targeting tools
Data remains siloed within platform
Fragmented customer view
Measurement
Detailed in-platform metrics
Inconsistent cross-platform standards
Difficult performance comparison
Intelligence
Platform-specific insights
Limited data portability
Restricted strategic learning
Optimization
Powerful automated tools
Black-box algorithms
Reduced marketer control
Fig. 2. Strategic trade-offs in walled garden advertising.
Core issue
Platform priority
Walled garden limitation
Real-world example
Attribution opacity
Claiming maximum credit for conversions
Limited visibility into true conversion paths
Meta and TikTok's conflicting attribution models after iOS privacy updates
Data restrictions
Maintaining proprietary data control
Inability to combine platform data with other sources
Amazon DSP's limitations on detailed performance data exports
Cross-channel blindspots
Keeping advertisers within ecosystem
Fragmented view of customer journey
YouTube/DV360 campaigns lacking integration with non-Google platforms
Black box algorithms
Optimizing for platform revenue
Reduced control over campaign execution
Self-serve platforms using opaque ML models with little advertiser input
Performance reporting
Presenting platform in best light
Discrepancies between platform-reported and independently measured results
Consistently higher performance metrics in platform reports vs. third-party measurement
Fig. 1. The Walled garden misalignment: Platform interests vs. advertiser needs.
Key dimension
Challenge
Strategic imperative
ROAS volatility
Softer returns across digital channels
Shift from soft KPIs to measurable revenue impact
Media planning
Static plans no longer effective
Develop agile, modular approaches adaptable to changing conditions
Brand/performance
Traditional division dissolving
Create full-funnel strategies balancing long-term equity with short-term conversion
Capability
Key features
Benefits
Performance data
Elevate forecasting tool
• Vertical-specific insights • Historical data from past economic turbulence • "Cascade planning" functionality • Real-time adaptation
• Provides agility to adjust campaign strategy based on performance • Shows which media channels work best to drive efficient and effective performance • Confident budget reallocation • Reduces reaction time to market shifts
• Dataset from 10,000+ campaigns • Cuts response time from weeks to minutes
• Reaches people most likely to buy • Avoids wasted impressions and budgets on poor-performing placements • Context-aligned messaging
• 25+ billion bid requests analyzed daily • 18% improvement in working media efficiency • 26% increase in engagement during recessions
Full-funnel accountability
• Links awareness campaigns to lower funnel outcomes • Tests if ads actually drive new business • Measures brand perception changes • "Ask Elevate" AI Chat Assistant
• Upper-funnel to outcome connection • Sentiment shift tracking • Personalized messaging • Helps balance immediate sales vs. long-term brand building
• Natural language data queries • True business impact measurement
Open Garden approach
• Cross-platform and channel planning • Not locked into specific platforms • Unified cross-platform reach • Shows exactly where money is spent
• Reduces complexity across channels • Performance-based ad placement • Rapid budget reallocation • Eliminates platform-specific commitments and provides platform-based optimization and agility
• Coverage across all inventory sources • Provides full visibility into spending • Avoids the inability to pivot across platform as you’re not in a singular platform
Fig. 1. How AI Digital helps during economic uncertainty.
Trend
What it means for marketers
Supply & demand lines are blurring
Platforms from Google (P-Max) to Microsoft are merging optimization and inventory in one opaque box. Expect more bundled “best available” media where the algorithm, not the trader, decides channel and publisher mix.
Walled gardens get taller
Microsoft’s O&O set now spans Bing, Xbox, Outlook, Edge and LinkedIn, which just launched revenue-sharing video programs to lure creators and ad dollars. (Business Insider)
Retail & commerce media shape strategy
Microsoft’s Curate lets retailers and data owners package first-party segments, an echo of Amazon’s and Walmart’s approaches. Agencies must master seller-defined audiences as well as buyer-side tactics.
AI oversight becomes critical
Closed AI bidding means fewer levers for traders. Independent verification, incrementality testing and commercial guardrails rise in importance.
Fig. 1. Platform trends and their implications.
Metric
Connected TV (CTV)
Linear TV
Video Completion Rate
94.5%
70%
Purchase Rate After Ad
23%
12%
Ad Attention Rate
57% (prefer CTV ads)
54.5%
Viewer Reach (U.S.)
85% of households
228 million viewers
Retail Media Trends 2025
Access Complete consumer behaviour analyses and competitor benchmarks.
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Michaels Stores: Implemented a genAI platform that increased email personalization from 20% to 95%, leading to a 41% boost in SMS click through rates and a 25% increase in engagement.
Estée Lauder: Partnered with Google Cloud to leverage genAI technologies for real-time consumer feedback monitoring and analyzing consumer sentiment across various channels.
High
Medium
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Showmax: Partnered with AI firms toautomate ad creation and testing, reducing production time by 70% while streamlining their quality assurance process.
Headway: Employed AI tools for ad creation and optimization, boosting performance by 40% and reaching 3.3 billion impressions while incorporating AI-generated content in 20% of their paid campaigns.
High
High
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
L’Oréal: Analyzed millions of online comments, images, and videos to identify potential product innovation opportunities, effectively tracking brand sentiment and consumer trends.
Kellogg Company: Used AI to scan trending recipes featuring cereal, leveraging this data to launch targeted social campaigns that capitalize on positive brand sentiment and culinary trends.
High
Low
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
DoorDash: Leveraged Google’s AI-powered Demand Gen tool, which boosted its conversion rate by 15 times and improved cost per action efficiency by 50% compared with previous campaigns.
Kitsch: Employed Meta’s Advantage+ shopping campaigns with AI-powered tools to optimize campaigns, identifying and delivering top-performing ads to high-value consumers.
High
High
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
JPMorgan Chase: Collaborated with Persado to develop LLMs for marketing copy, achieving up to 450% higher clickthrough rates compared with human-written ads in pilot tests.
Hotel Chocolat: Employed genAI for concept development and production of its Velvetiser TV ad, which earned the highest-ever System1 score for adomestic appliance commercial.
High
High
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Stitch Fix: Uses genAI to help stylists interpret customer feedback and provide product recommendations, effectively personalizing shopping experiences.
Instacart: Uses genAI to offer customers personalized recipes, mealplanning ideas, and shopping lists based on individual preferences and habits.
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