Yesterday's marketing developments revealed significant AI adoption acceleration across industries, with video creation seeing a dramatic 128% year-over-year increase in AI usage. Meanwhile, homegrown martech solutions are flourishing as AI simplifies development, and marketers gain powerful new measurement capabilities through conversion lift testing.
Story #1: 41% of brands now using AI for video production, more than doubling from last year
What Happened: New research shows 41% of brands are now using AI for video production, compared to just 18% last year—a dramatic 128% year-over-year increase. The study surveyed over 1,200 marketing professionals across various industries and company sizes, revealing that video production has become one of the fastest-growing areas for AI implementation.
Why It Matters: Video remains the most engaging content format across platforms, but has historically required substantial time, expertise and budget to produce at scale. AI tools have democratised video creation, allowing brands of all sizes to produce professional-quality video content more efficiently and cost-effectively. Companies using AI for video report producing 3.7 times more video content while reducing production costs by an average of 53%.
Suggested Actions:
Evaluate emerging AI video production tools to identify ones that align with your brand aesthetic and content needs
Consider reallocating some production budgets toward AI-enhanced video creation while maintaining strategic human oversight
Experiment with AI-generated variations of successful video concepts to optimise performance
Story #2: State of the Stack 2025: Homegrown martech surges as AI accelerates development
What Happened: MarTech's annual State of the Stack report reveals a significant shift in the marketing technology landscape, with organisations increasingly developing custom solutions in-house rather than relying solely on third-party vendors. The average enterprise now uses 107 martech tools, up from 91 last year, with 28% of these being homegrown—an increase from 19% in 2024. AI development tools are cited as the primary enabler, with 73% of organisations reporting they've built at least one custom marketing application using AI assistance in the past 12 months.
Why It Matters: The balance of power in the martech ecosystem is shifting as companies gain the ability to build tailored solutions rather than relying exclusively on third-party vendors. This trend enables more customised marketing technology stacks that precisely address specific business needs and integrate more seamlessly with existing systems. Organisations report 42% higher satisfaction with homegrown solutions compared to third-party alternatives, particularly in areas requiring deep integration with proprietary data.
Suggested Actions:
Audit your current martech stack to identify gaps that might be addressed through AI-assisted custom development
Establish cross-functional teams combining marketing and technical expertise to explore homegrown martech opportunities
Develop governance frameworks to ensure homegrown solutions maintain security and compliance standards
Story #3: How conversion lift testing works across advertising platforms
What Happened: Search Engine Land published an extensive guide on conversion lift testing methodologies across major advertising platforms, including Google, Meta, TikTok, and Amazon. The analysis details how these tests scientifically measure the true incremental impact of advertising by establishing proper control groups and isolating campaign effects. The guide reveals significant disparities between conversion lift results and traditional attribution models, with the latter often overstating campaign impact by 30-45%.
Why It Matters: As marketing budgets face increased scrutiny, proving the true incremental value of advertising spend has become essential. Conversion lift testing provides a significantly more accurate view of campaign effectiveness than traditional attribution models, helping marketers optimise their spending decisions with greater confidence. The methodology addresses fundamental attribution challenges by establishing what would have happened without advertising exposure—the true counterfactual that attribution models typically fail to capture.
Suggested Actions:
Implement conversion lift testing on platforms where this capability is available to establish true ROI baselines
Use test results to recalibrate attribution models and better inform cross-channel budget allocation
Design campaigns with measurement in mind, ensuring proper test and control group segmentation
Story #4: IAB report: AI adoption accelerating across marketing landscape
What Happened: The IAB's comprehensive "State of Data 2025" report reveals AI adoption is accelerating across the marketing landscape, with 87% of organisations now implementing some form of AI technology—up from 76% last year. However, the report highlights a growing divide between leaders and laggards, with just 23% of companies having what the IAB classifies as "strategic AI implementation" versus 64% still focused primarily on tactical automation. The gap between these groups is widening, with AI leaders showing significantly stronger performance metrics across customer acquisition, retention, and lifetime value.
Why It Matters: The growing gap between AI leaders and laggards suggests competitive advantages are already forming. Organisations falling behind in strategic AI implementation risk losing market share and operational efficiencies to more technologically advanced competitors. The report indicates AI leaders are experiencing 2.7x higher marketing ROI and 34% lower customer acquisition costs compared to organisations still in the tactical implementation phase.
Suggested Actions:
Move beyond tactical automation toward strategic AI implementation with clear business objectives
Develop an organisational AI competency roadmap with defined milestones and measurement frameworks
Invest in upskilling current team members while recruiting for specialised AI expertise in key areas
Story #5: Google extends Gemini-powered Smart Campaigns to all advertisers
What Happened: Google has removed all restrictions on its Gemini-powered Smart Campaigns, making the technology available to all advertisers effective immediately. The announcement follows a six-month limited release that showed impressive results, with beta participants experiencing an average 41% improvement in conversion rates and 27% reduction in cost per acquisition compared to standard automated campaigns. The system uses Google's most advanced AI to create, test, and optimise ad variations while continuously refining audience targeting based on real-time performance data.
Why It Matters: This democratisation of advanced AI campaign technology levels the playing field for smaller advertisers who previously lacked access to sophisticated marketing AI. The system's ability to autonomously manage creative testing, audience discovery, and bid optimisation effectively puts enterprise-grade campaign intelligence within reach of organisations with limited marketing resources. Google's analysis indicates the greatest performance gains occur in accounts that previously struggled with optimisation due to resource constraints.
Suggested Actions:
Evaluate Gemini-powered Smart Campaigns for product lines or services where current performance lags expectations
Prepare high-quality assets and detailed conversion tracking to maximise the AI's optimisation capabilities
Consider reallocating specialist time from routine campaign management to higher-level strategy and analysis
Comprehensive Summary
Yesterday's marketing developments highlighted the accelerating transformation of the industry through AI adoption. The most striking statistic comes from video production, where AI usage has jumped 128% in just one year, with 41% of brands now leveraging these technologies. This dramatic shift represents a democratisation of what was previously one of the most resource-intensive content formats, allowing smaller brands to compete more effectively with larger enterprises that have traditionally dominated video marketing.
Simultaneously, the MarTech landscape is experiencing significant restructuring as AI simplifies software development. The State of the Stack 2025 report reveals marketers are utilising more tools than ever, but with a growing proportion being developed in-house rather than purchased from third-party vendors. This shift toward homegrown solutions provides organisations with more tailored technology that precisely addresses their specific business requirements, with companies reporting 42% higher satisfaction with these custom tools compared to off-the-shelf alternatives.
Measurement capabilities are also evolving, with conversion lift testing emerging as a critical tool for determining the true incremental impact of advertising investments. By establishing proper control groups, marketers can now scientifically verify which portion of conversions would have occurred naturally versus those directly attributable to campaign exposure. This methodological improvement represents a significant advancement over traditional attribution models that often overstate marketing impact by 30-45%.
The IAB's State of Data 2025 report contextualises these developments within the broader AI adoption landscape, suggesting that while implementation is accelerating across the industry, a concerning gap is emerging between organisations with sophisticated AI strategies (23%) and those still focused primarily on basic automation (64%). This disparity indicates that strategic approaches to AI implementation—rather than tactical point solutions—will likely determine competitive advantage in the coming years.
Google's decision to extend its Gemini-powered Smart Campaigns to all advertisers represents another significant democratisation of AI technology, potentially levelling the playing field between resource-constrained smaller advertisers and their enterprise competitors. With beta participants experiencing an average 41% improvement in conversion rates, this technology could significantly reshape the digital advertising landscape as it becomes more widely adopted.
Collectively, these developments suggest marketing is entering a new phase where AI capabilities are becoming foundational rather than experimental. Organisations that establish clear AI implementation roadmaps with specific business objectives will likely outperform those approaching these technologies in an ad hoc fashion. The data indicates this performance gap is already forming, with AI leaders experiencing 2.7x higher marketing ROI compared to organisations still in early implementation stages.
Key Takeaways
Prioritise AI video production tools: With 41% of brands now using AI for video creation and reporting 3.7x more content output, evaluate how these technologies can enhance your content production workflow while maintaining creative quality.
Reassess your build vs. buy calculus: AI is dramatically reducing the barriers to custom martech development; consider which gaps in your stack might now be better addressed through internal solutions rather than third-party vendors.
Implement scientific measurement: Conversion lift testing provides significantly more accurate insights than traditional attribution; implement these methodologies wherever available to optimise marketing spend and understand true incremental impact.
Develop strategic AI competencies: Move beyond tactical automation toward a comprehensive AI strategy with clear business objectives, dedicated resources and formal measurement frameworks to avoid falling behind the 23% of organisations already implementing AI strategically.
How-To Spotlight: Measuring True Advertising Impact with Conversion Lift Testing
Yesterday's detailed guide on conversion lift testing provides a practical framework for implementing this powerful measurement methodology. The approach involves:
Dividing your audience into randomised test and control groups
Exposing only the test group to your advertising
Measuring conversion rates in both groups
Calculating the difference to determine true incremental impact
The guide explains platform-specific implementation methods for major advertising channels including Google, Meta, TikTok, and Amazon. It provides case studies demonstrating how results often differ significantly from standard attribution models, with the latter typically overstating campaign impact by 30-45%. Most importantly, it offers practical advice for translating these insights into optimised budget allocation decisions across channels.
For organisations new to lift testing, the guide recommends starting with a single platform where spend is highest, then expanding to additional channels once the methodology is understood. It emphasises the importance of statistical significance, recommending minimum audience sizes and test durations based on conversion volume to ensure reliable results.
Subscribe to our daily updates: Indexify Substack
Share this post