Navigating the Amazon Ecosystem with AI.

Many of us know Amazon for its ecommerce prowess and popular media platforms like Prime Video and Twitch. But Amazon boasts over 40 different owned properties, and this extensive network creates a rich tapestry of first-party data, offering a significant understanding of consumer behavior. However, truly harnessing this data and understanding how shoppers move across properties—from watching a show on Fire TV to shopping on the Amazon website—can be complex.

Beyond Amazon's owned properties, Amazon DSP also offers access to a massive network of third-party inventory through Amazon Publisher Direct and various ad exchanges. This opens up even more avenues to reach your audience wherever they are online. The challenge, then, becomes identifying the most efficient path to deliver your impressions. This is where Amazon's human-centered AI truly shines.

Introducing Brand+ and Performance+

Amazon's AI models, Brand+ and Performance+, are built on the foundation of Ad Relevance, an AI machine learning tool developed to increase addressability in cookieless environments. These models are not about replacing the human media buyer; instead, they are designed to collaborate, offering transparency and control. You can see exactly what the AI is doing, why it's doing it, and crucially, you can intervene, nudge, and redirect to ensure it aligns with your strategic goals.

While both models utilize the same underlying logic, their primary differentiator lies in the outcomes they are designed to generate:

• Brand+: Focuses on the top of the funnel, prioritizing awareness, reach and frequency, with a secondary goal of future conversions within a 12-month conversion horizon. This model is ideal for building long-term brand salience and memory structures. We've seen Brand+ campaigns perform exceptionally well with video creative and streaming inventory.
• Performance+: Centers on consideration and conversion within a shorter 30-day conversion horizon, aiming for immediate conversions and measurable ROI. This model is highly effective for driving actions like purchases. While it can utilize streaming and online video, we often recommend focusing on online video (OLV) and display inventory for optimal results.

How do these models work?

The intelligence behind Brand+ and Performance+ can be broken down into four key components:

1. Advertiser Event (Human-Defined): The process begins with you! You define the specific conversion events you're tracking. This could be anything from website purchases tracked via the Amazon ad tag, offline conversions integrated through a Conversion API, purchases of specific products on Amazon via ASINs, or even mobile app installs and actions reported by an MMP. This human input is crucial, ensuring the AI aligns with your unique business objectives.
2. Addressability Graph: Based on your defined advertiser events, the AI creates a comprehensive "addressability graph." This map provides a multi-property overview of how your converting customers interact across the vast Amazon ecosystem and beyond. It helps understand their journeys, their paths and their engagement patterns.
3. AI Prediction (Lookalike Audiences and Predictive Scores): From the addressability graph, the AI intelligently creates audiences similar to your existing converters—essentially, a lookalike audience. It then generates predictive scores for individuals within this lookalike audience, identifying those most likely to convert based on the creative being served and the desired outcome within a specific timeframe. This ensures your ads are delivered to the most receptive audience.
4. Campaign Delivery and Continuous Optimization: The AI then dynamically delivers impressions through the most efficient channels, whether it's Amazon Prime Video, Magnite or Amazon Publisher Direct. What's truly powerful is the continuous learning loop. The AI constantly tracks post-impression conversions, updates its model, and refines its targeting as more data becomes available, ensuring your campaigns are always optimizing for better outcomes. This process requires a consistent feed of advertiser events, as static data alone isn't enough.

Easily set up your campaigns.

Setting up campaigns with these AI models is incredibly straightforward, often taking less than two minutes for Performance+ and even quicker for Brand+.

For Brand+, once you've defined your conversion event and set your budget, you're ready to launch.

For Performance+, you have the flexibility to choose from three sub-models:

• Customer Acquisition: Targets net new shoppers who haven't interacted with your brand, excluding those who have converted in the past 90 days.
• Remarketing: Focuses on users who have engaged with your brand or product but haven't converted in the last 90 days.
• Retention: Aims to re-engage customers who have interacted with your brand and have not converted in the last seven days, fostering loyalty.

We often recommend starting with all three sub-models for Performance+ and then reviewing their performance to optimize your strategy.

Empowering marketers beyond automation.

Even with AI doing the heavy lifting, you retain full access to all the familiar optimization tools and reporting capabilities you'd expect from Amazon DSP. This includes granular inventory reports, creative performance analysis and insights into geographic and audience metrics. You can still leverage pacing controls, day-parting, frequency settings and the powerful insights from Amazon Marketing Cloud (AMC).

For first-time users, we always suggest running Business as Usual (BAU) campaigns alongside Brand+ and Performance+. This allows you to truly appreciate the incremental value these AI models deliver. We also recommend a minimum flight of two months for both models to allow the AI sufficient time to learn and optimize.

Demonstrating real-world impact.

We've seen firsthand the significant impact these AI models can have. In a recent link-out campaign (tracking conversions outside Amazon's ecosystem) for an advertiser, our manual interventions—specifically refining sub-models and optimizing frequency caps based on AMC insights—led to an incredible 400% increase in ROAS for the Performance+ campaign. Ultimately, this campaign delivered 1.6 times more ROAS compared to the BAU approach.

Similarly, for a link-in campaign (tracking purchases on the Amazon website), the Performance+ campaign achieved a 50% higher ROAS than BAU. While the CPM for Performance+ was 13% higher, the CPA was 66% lower, underscoring the AI's ability to drive more efficient outcomes by focusing on valuable conversions rather than just cheap impressions. This highlights the ongoing industry conversation about prioritizing effective inventory over merely the cheapest.

A partner for success.

At Monks, we're proud to be an AWS Partner, with a 13-year partnership with Amazon. Our expertise in AI has been recognized with awards like the AI Pioneer by The One Show and AI Agency of the Year from AdWeek. We are also a certified AdTech Reseller and Activation Partner for Amazon DSP.

If you're looking to simplify your media buying, unlock the full potential of Amazon's ecosystem, or just want to explore how these human-centered AI models can transform your advertising strategy, we're here to help. Reach out to us for a tailored session or a casual conversation about achieving your goals with Amazon. We'd love to partner with you on your journey to growth and innovation!

These are our three takeaways that defined GML 2025.

Takeaway 1: Ad delivery is getting more flexible across Google’s network.

Google is shifting away from channel-based ad setups and leaning into more fluid, moment-driven experiences. Instead of building separately for Search, YouTube, or Display, ad products are increasingly designed to find users wherever they are—scrolling, streaming, or shopping. This expansion means more opportunities to reach users but also more demand for creative that fits each touchpoint, which often requires brands to scale video visuals and messaging quickly with the help of AI (or not). Measurement tools are also being updated to support this shift, aiming to track how these moments connect and contribute to sales across the journey. Flexible measurement (going beyond pixel-based attribution by incorporating incrementality, MMM, etc.) is essential as customer paths rarely follow a straight line.

Takeaway 2: AI is now embedded in Search and how brands connect. 

The rollout of AI Mode and ads in AI Overviews marks a shift in how users navigate Search and how brands show up. These tools change not just ad placement, but the buying journey. Search is becoming more visual, more video-led, and more human in tone, which results in a search and shopping experience that’s more tailored and productive for users. For advertisers, what used to require multiple campaign types and formats is continuing to evolve into a single system of outcome-based products. This year Google’s messaging this as their “power pack”—Performance Max, AI Max and Demand Gen—for brands that use AI to reach consumers. If advertisers want to capitalize on the relevance and performance Google says the “power pack” provides, media buyers must focus on giving the AI the right quality inputs, in high volumes (conversion data, creative assets, etc.). 

Takeaway 3: Google is rolling back the black box for visibility and transparency.

Advertiser pressure for more transparency is starting to pay off. Google is introducing new Performance Max insights, lower spend thresholds for incrementality testing, and agentic tools like “Your Google Ads Expert” to make results easier to explain and optimize. But blind spots remain. For example, there’s still no placement-level reporting for ads in AI Mode or Overviews. Progress, yes. Total clarity, not yet.

These are the new features our team expects to be most impactful for advertisers.

AI tools are reshaping how we search, shop and advertise.

Search is no longer just a typed query in a box. With tools like Gemini, Google Lens and AI Overviews, the buying journey is becoming more visual, conversational and context-aware. The path from awareness to purchase is increasingly possible in one scroll, without leaving Google’s ecosystem. Google’s newest tools reflect this shift:

• Smart Bidding Exploration (in beta) blends flexible ROAS targets with new bidding logic to uncover valuable queries you may be missing.
• AI Overviews are live on mobile in the US, with desktop and other markets coming next. These ad placements are designed to align with broader search intent.
• AI Mode, currently in testing, introduces a conversational, multimodal search experience with an AI-powered shopping layer launching in the US soon.
• Agentic tools like “Your Google Ads Expert” and “Your Google Analytics Expert” (in beta) aim to speed up insights and surface optimizations. “Your Marketing Advisor,” a Chrome-based AI assistant, will soon help teams manage tasks and surface recommendations across tools.

Put it into practice: These evolutions in the SERP are reshaping user behavior and redefining what ad success looks like. For advertisers, your inputs—site content, product feeds, conversion data, creative assets, etc.—matter more than ever as the content and experience will be derived automatically with AI. Invest in shoring up those foundations to make sure you’re showing up accurately and effectively in these new SERP experiences.

AI Max for Search gives you automation with a clearer view.

AI Max for Search Campaigns is a one-click upgrade that uses AI to match your landing pages, ads and keywords to real-time search intent. Google reports early tests showed up to 27% more conversions at similar CPA or ROAS, especially when using exact and phrase match. Unlike Dynamic Search Ads, which auto-generate content with limited reporting, AI Max surfaces clear insights into which queries, headlines and landing pages are driving performance. It’s still automated, but with a clearer view of what’s happening behind the scenes.

Put it into practice: Try AI Max on a campaign where broad match is performing well but hasn’t hit its ceiling. Use the new reporting to spot high-converting queries and creative, then scale what’s driving results. 

Performance Max now shows where results are coming from.

Performance Max has always prioritized automation over transparency. But Google is finally pulling back the curtain. Channel-level reporting now shows results across Search, YouTube, Shopping and other surfaces. Asset-level insights and fuller search term visibility offer more granular data to understand what’s actually working. For brands running full-funnel campaigns, this is a significant improvement.

Put it into practice: Shift budget to top-performing surfaces using channel data by influencing Google's spending. Update or remove underperforming assets within your campaign. If YouTube is lagging, shorten your video creative or adjust your audience signals.

Video ads in Search and Shopping compress the funnel.

Video placements are now being tested directly within Search and Shopping results, giving advertisers a shot at influencing high-intent shoppers without relying on separate awareness plays. The line between discovery and purchase is disappearing, and Google wants to keep the entire journey within its ecosystem. Users aren’t skipping steps in the funnel, they’re completing all of them in a single scroll.

Put it into practice: Add horizontal and vertical video assets to your ad groups. Focus on short-form content that delivers value fast, such as how-to clips, testimonials or product highlights.

Measurement tools are improving, but still require setup.

Google maintained its focus on measurement this year, sharing advertiser stories about the value of Meridian and unveiling updates to measurement features within Google Ads.  For example, they lowered the threshold significantly for in-platform incrementality testing, making it more accessible for brands to measure what tactics are creating incremental results. 

Additionally, Data Manager is Google Ads’ latest tool aimed at improving signal quality and measurement reliability. It helps advertisers connect and validate first-party data from websites, apps, CRMs, and in-store systems, making campaign data cleaner, more actionable, and privacy-compliant. It also supports better attribution by ensuring tags and signals are set up correctly. 

Put it into practice: Use Data Manager to set up and quality check your tagging configuration, confirm that key data sources are linked to your Google Ads account, and connect first-party data from third-party platforms like BigQuery, Salesforce, Shopify, Google Sheets, and more. A clean setup leads to better optimization and clearer insights.

Turn GML 2025 updates to real business outcomes.

GML 2025 showed that performance marketing is becoming more creative, more automated and more measurable. These updates are your chance to simplify workflows and scale impact. If you’re connecting creative, data and AI in one system, you’re going to move faster than your competitors.

Need help connecting the dots?
Let’s talk. We help brands turn updates like these into growth strategies that drive results.

A recent report from Adobe, “Adobe AI and Digital Trends in Content Creation and Management,” dives into these very pressures, especially concerning the intricacies of content supply chains—and I had the privilege of participating by lending my own expertise, noting that “High-quality content is more abundant than ever, spanning countless channels. The challenge of our time lies in managing and delivering it effectively. Success will hinge on mastering volume and variance, with brand standing as the ultimate differentiator.”

Mastering this balance of volume is an opportunity for brands to reimagine how they create, manage and deliver content. To help you envision what that could look like, we’ll examine some of Adobe’s findings below and showcase how we’ve helped brands tackle these complexities. 

Relevance isn’t optional—it’s expected.

One of the more eye-opening findings of Adobe’s research indicates that 71% of consumers consider it important or critical that brands anticipate their needs. Translation: relevance is everything. Consumers no longer just appreciate personalized, timely content—they expect it. My colleague Remco Vroom, Global EVP of Martech Platforms & Innovation, highlighted this shift in conversation with The Drum, noting that current AI innovation is largely shaped by these rising expectations: “Everything needs to be these fast, super niche experiences,” he said. “We’re getting all of these really great, faster-to-market, hyper-personalized services and we expect that from everything and everybody. So, companies need to redesign what their role is in in the whole value chain of products and services.”

To meet these evolving demands, delivering personalized content at scale is no longer optional—it’s the cornerstone of building lasting consumer relationships. A strong content supply chain is critical to achieving this agility. By leveraging tools like Adobe Firefly, we’ve been able to streamline workflows and create hyper-relevant content faster than ever before. Our work in this space was even highlighted in the keynote at the recent Adobe Summit, showcasing how we successfully leveraged Adobe Firefly to meet consumer expectations and deliver impactful results.

For instance, we used Adobe Firefly to generate 270 versions of a banner in a single day—a task that could have taken up to four weeks manually. The result? A 78% increase in click-through-rate, demonstrating how a well-optimized content supply chain not only meets consumer expectations but drives measurable impact. These efforts helped our client remain competitive in a world where attention spans are short and consumer expectations are higher than ever. 

Streamlined systems unlock scalability and impact.

In contrast to the point above, consumers say only a third of brands they interact with provide timely and relevant offers or communications, and only 45% of brands create a consistent experience across channels. So, how do you move in that direction? The key to building this framework lies not just in adopting AI tools, but in orchestration: strategically aligning talent, technology and creative processes across the entire content journey.

We believe orchestration is the linchpin of successful AI implementation for CX. In fact, it's a core tenet of our approach to modern marketing challenges. Monks.Flow—our AI-powered marketing platform that automates workflows, enabling scalable, efficient content production—is designed to enable transformative orchestration by consolidating fragmented processes and redefining the content supply chain, delivering full-funnel advertising that is faster, more relevant and more cost-efficient.

A prime example of this orchestration approach is our partnership with BMW Group. We served as BMW's creative and content orchestration partner, transforming their marketing by scaling relevant product marketing across 26 countries and in 29 languages. We streamlined BMW’s campaign processes from start to finish, consolidating 126 agencies into a single partnership. 

As part of this process, we implemented brand models and custom AI pipelines to deliver content at scale. This included Atomic Assets, an automated image production solution, to provide BMW with unlimited asset combinations for all car models and markets. This system leveraged automation and AI to enable BMW market teams to easily order customized assets and receive new visual assets within a 24-hour turnaround. And of course, it allows us to build creative that otherwise wouldn’t be possible, like bringing virtual influencer Lil Miquela into the real world.

Ultimately, building an effective AI-powered CX framework requires a shift towards this orchestrated approach, creating a unified and intelligent system that enhances customer interactions and drives transformative business impact.

Overcome ethical barriers to scale AI.

A key obstacle to AI innovation mentioned in the report is ethics: about half of the executives surveyed in Adobe’s research cite ethical concerns and brand reputation as barriers to scaling AI. This is a valid concern, because while many want to be fast and first with AI, it shouldn’t come at the expense of your brand, your people or your audience. We've navigated these worries with brands, understanding that ethical considerations are paramount in developing and deploying AI-driven solutions with confidence.

To mark the 20th anniversary of Dove’s iconic Real Beauty campaign—celebrated for challenging traditional beauty standards and championing inclusivity—we partnered with Dove to launch the Dove Code. In an effort to refresh its message for a new era, Dove sought to address biases amplified by AI. To support this mission, we developed an AI prompting playbook designed to help the industry confront biases inherent in generative AI outputs. This playbook provides a framework for embedding ethically grounded principles into generative AI workflows, ensuring that creativity and inclusivity remain at the forefront of industry innovation.

Furthermore, it's crucial to look ahead at emerging AI trends and address potential ethical considerations proactively. Our recent Labs Report on agentic AI, for example, decodes the hype around this technology and provides a transparent view of both its benefits and potential risks. As the capabilities of AI expand, it becomes increasingly important for brands to have partners who can guide them through these complexities. We believe in being transparent about potential risks and working collaboratively to develop mitigation strategies.

Mastering volume, variance and relevance is within your reach.

The demand for personalized, relevant, and timely content is reshaping the way brands approach their content supply chains. To thrive in this golden era of content means mastering both volume and variance while delivering experiences that resonate deeply with consumers. Whether it’s leveraging tools like Adobe Firefly to generate hyper-relevant content at scale or implementing orchestrated workflows as seen in our partnership with BMW Group, the key lies in aligning talent, technology and strategy to create meaningful, efficient, and impactful content.

A new partnership that enables wide, holistic views of client data.

One of the primary challenges in implementing a customer data platform is data readiness. Many businesses struggle with fragmented data sources, messy pipelines and the difficulty of extracting actionable insights. Monks helps clients overcome these challenges by offering a structured approach to integrate, harmonize and analyze data efficiently. 

With this new partnership, our team of data architects, analysts and engineers will work to integrate the entire data supply chain, breaking down these silos and enabling a wider, more holistic view of our client’s data. Once data readiness and wide data are achieved, the Hightouch Composable platform can be applied to activate their library of pre-built integrations and start to deliver AI-powered personalized experiences.

“Our team of data architects, analysts and engineers offer services to solve this problem. Monks will work to integrate the entire data supply chain, breaking down these silos and enabling a wider, more holistic view of our client’s data,” explains Jakub Otrząsek, SVP, Data, APAC at Monks. 

“Once data readiness and wide data are achieved, the Hightouch Composable platform can be applied to activate with their library of pre-built integrations and start to deliver AI-powered personalized experiences,” he adds.

Understanding the increasingly prominent role of composable CDPs.

A composable martech stack represents a significant shift in how organizations manage their marketing technology. By leveraging a best-of-breed approach and centering the architecture around a cloud data warehouse, businesses can create a single source of truth for customer data. This unified approach not only streamlines the deployment of existing advanced machine learning models but also fosters a modular and adaptable technology ecosystem that can readily evolve to accommodate changing business requirements.

The rapid adoption of composable CDPs within the industry underscores the numerous advantages they offer. Their cost-effectiveness, ease of deployment, and ability to leverage existing technology and intellectual property make them an attractive solution for businesses seeking to optimize their marketing technology stack. As the industry continues to evolve, composable CDPs are poised to play an increasingly prominent role in shaping the future of marketing technology.

Why data activation means value realisation.

First-party customer data is critical for organizations because it enables them to build trust through genuine interactions and scale personalization using AI. By activating first-party data with the Monks and Hightouch partnership, businesses can make a profound impact on marketing ROI.

Monks and Hightouch help businesses achieve this by:

• Enabling highly personalized, scalable marketing strategies through seamless integration of first-party data with media sources.
• Optimizing marketing spend by analyzing campaign performance in real-time
• Identifying high-performing content and reallocating resources for maximum ROI

The partnership allows for smarter and faster decisions based on valuable insights extracted from first-party data, moving businesses “beyond data chaos into clarity.” Monks helps brands build trust through authentic interactions while leveraging AI and first-party data to scale personalization.

A perfect moment for a partnership.

For Hightouch, this represents a pivotal moment. The widespread adoption of cloud data warehouses, the wave of interest in the composable CDP approach and the immediate success of the AI Decisioning launch marks a significant turning point for the Hightouch. This confluence of factors propels Hightouch into a substantial growth phase, positioning the company at the forefront of the evolving marketing technology landscape.

“Enterprises in the APAC region are setting the global pace in adopting composable CDP and AI agents for marketing,” said Kashish Gupta, CEO of Hightouch. “Monks is the ideal partner to help Hightouch support the rapid deployment of these technologies in the region.”

Meanwhile, we have recently simplified our teams into the two pillars of Marketing and Technology Services. This simplification of our business means we are more integrated than ever, aligning our strategic team leads with our clients, and able to bring expertise and talent across the full spectrum of Marketing and Technology Service as our client’s requirements change.

The Hightouch platform also perfectly aligns with our recent launch of the Data Decisioning framework.

Let's talk data and AI.

We invite businesses to discover how our partnership with Hightouch can revolutionize their data strategies. Whether the goal is to streamline analytics, improve marketing performance, or activate your customer data, we’re here to help.

If you’re unfamiliar, the Artificial Intelligence Excellence Awards celebrate standout contributions in a rapidly evolving field, highlighting organizations and solutions that leverage AI to create meaningful impact. This marks the second consecutive year that Monks has been recognized by the Business Intelligence Group for our AI expertise—be sure to check out last year’s announcement here—so I’d like to take this opportunity to share what’s changed in the past twelve months.

Driving innovation and solving challenges in scale, quality and ethics.

Last year, we launched Monks.Flow, a powerful platform that combines AI-powered tools, automation and seamless workflows to optimize every step of the campaign-building process. By integrating these capabilities, Monks.Flow empowers brands to scale campaigns, improve efficiency and reduce costs—all while maintaining creative excellence. Since its launch, Monks.Flow has addressed some of the most significant challenges in marketing operations, redefining how brands manage complexity, scale and ethics in their campaigns.

One of the more recent challenges we’ve worked through is scaling quality assurance (QA) processes to match high-volume content production. While producing hundreds of creative assets is easily achievable with AI, manually checking them for consistency and brand compliance is not. To solve this, we developed a powerful AI-driven QA tool within Monks.Flow that automates real-time checks for precision, compliance and consistency. By reducing inefficiencies and eliminating human error, this innovation has set a new standard for speed and accuracy in asset production.

But our efforts aren’t limited to Monks.Flow and its development; we’ve also helped brands overcome broader AI concerns, like its ethical use. A prime example of this is our collaboration with Dove on the Real Beauty Prompt Playbook, launched as part of the 20th-anniversary celebration of Dove’s Campaign for Real Beauty. This first-of-its-kind public resource was designed to address ingrained biases in AI’s depiction of beauty. With techniques for inclusive prompting and a glossary of equitable vocabulary, the Playbook equips creators to redefine beauty standards in AI-generated content. By steering AI in a more inclusive direction, we’re helping brands like Dove foster representation and authenticity in their creative work.

Building at the forefront of AI through strategic partnerships.

Beyond solving key challenges for our clients, we’ve also spent the past year strengthening our position at the forefront of AI through transformative partnerships with some of the most innovative companies in the industry. These collaborations have allowed us to push boundaries, deliver practical solutions and empower brands to excel in an AI-driven world.

We recently made moves with NVIDIA in pursuit of these goals. Announced at NVIDIA’s recent GTC conference, we introduced the Monks Foundry, a dedicated team of engineers focused on developing and deploying custom generative AI models tailored to enterprise data and domain-specific knowledge. These models are designed to meet the unique needs of each organization, enabling them to harness AI as a bespoke solution aligned with their goals and challenges. Supporting this work is the Monks Agentic Advisory, a 50-person consulting team that collaborates with the Foundry to provide insights and cutting-edge solutions. Together, these teams are setting new standards for innovation in generative AI.

This partnership is further strengthened through deep integrations with NVIDIA’s ecosystem. Monks.Flow now connects seamlessly with platforms like NVIDIA Omniverse, NVIDIA NIM, and NVIDIA Blueprints, enabling advanced agentic AI workflows. These integrations allow brands to streamline operations, accelerate production timelines and scale their content creation capabilities with precision and speed—ensuring they stay competitive in an ever-changing landscape. 

In parallel, our partnership with Adobe underscores a shared commitment to revolutionizing the content supply chain for modern marketers. At the center of this collaboration is the launch of the Brand Model Practice, which combines Adobe’s GenStudio—a generative AI-first solution—with Monks.Flow. Together, these tools enable the creation of generative AI brand models: tailored systems that help marketers plan, create, manage, activate and measure on-brand content with speed and precision.

This partnership has already earned recognition, with Media.Monks being named runner-up for the Adobe Firefly Partner Award, an accolade that celebrates the most innovative applications of Adobe’s generative AI technologies. 

Partnering to push the boundaries of AI-powered creativity.

Winning the "case race" in today’s fast-paced marketing landscape requires more than just keeping up—it’s about staying ahead by leveraging the best tools and technologies. Through our collaborations with industry leaders like Google and Meta, we’ve not only delivered breakthrough results for brands but also helped push the boundaries of what their AI solutions can achieve. These partnerships have allowed us to explore the full potential of their platforms, driving innovation and showcasing what’s possible when AI meets creativity.

For Forever 21, operating in a fast-paced, trend-driven space, our challenge was to produce high volumes of lifestyle ad imagery to fuel growth. Using Monks.Flow, we combined existing photography with AI-generated seasonal backgrounds and text overlays, creating hundreds of ad variations tailored for Meta’s Advantage+ campaigns. This streamlined approach not only reduced production time but also optimized delivery using Meta’s AI-powered algorithms, resulting in improved conversion rates, higher average order values and validated ROI.

Similarly, Hatch, a wellness brand known for its Restore smart sleep assistant, faced growing pressure to deliver fit-for-format content across platforms like YouTube and Meta-owned channels. By combining our creative expertise with Google’s Gemini, we developed high-performing videos and dynamic animations that doubled their click-through rates and reduced acquisition costs. These AI-powered workflows helped Hatch scale their output while maintaining authenticity, easing the burden on their creative teams—and were highlighted by David Wadhwani, President of Digital Media at Adobe, during the Adobe Summit keynote as an example of how agencies are helping brands create more effective content.

Shaping what’s next in AI-driven solutions.

As we celebrate these two AI Excellence Awards, it's amazing to see how far we’ve come in the past year. From launching innovative solutions like Monks.Flow to forging transformative partnerships with industry leaders like NVIDIA, Adobe, Google and Meta, we’ve consistently pushed the boundaries of what’s possible with AI. These efforts not only solve complex challenges for our clients but also help shape the future of marketing by redefining creativity, efficiency and scalability through AI-driven solutions.

Looking ahead, we remain committed to staying at the cutting edge of AI innovation and focused on delivering impactful, measurable results for brands worldwide. Watch this space—who knows what we can accomplish together in the next year.

Let’s look at how Adobe GenStudio redefines performance marketing.

Adobe GenStudio for Performance Marketing is designed around the needs of performance marketers, whose roles often require scaling content creation and delivery. For larger organizations with well-established marketing teams, this solution can support tactical initiatives or even serve as a foundation for dedicated performance marketing groups. For corporate-level organizations with smaller marketing budgets, it offers a way to enable more in-house services, driving greater agility and efficiency.

Adobe has likewise recognized the critical role agencies play in helping organizations navigate the creative space. Teams like ours are uniquely positioned to support clients in two vital ways: first, by implementing tools and adapting business processes to harness the potential of generative AI; and second, by ensuring strong alignment with organizational and regulatory frameworks. As the GenAI landscape is evolving so quickly, the implementation of these frameworks needs to be revisited frequently.

GenStudio for Performance Marketing puts powerful features at marketers’ fingertips.

Looking at the tool, I was keen to understand where it fits into the existing Adobe GenStudio and Content Supply Chain toolset. The existing toolset (with applications such as Adobe Workfront, Adobe Creative Cloud, and Adobe Experience Manager Assets) provides a comprehensive, scalable, end-to-end solution for any business. With GenStudio for Performance Marketing, Adobe brings together key features across all these solutions to support performance marketing teams create content within the context of its content supply chain workflows.

The main menu of GenStudio for Performance Marketing is divided into two sections: one for actions and utilities, which the Performance Marketer can use to create and measure outbound content, and another for shared resources, including brands, personas and products. There are currently two roles, marketer and system manager, although I expect these to become more granular before release. The marketer creates outbound content and can review its performance, while the system manager can configure the shared resources such as brands, products, and personas and approve content for publication.

The setup of the shared resources provides critical guardrails for AI-generated content. A System Manager can drag and drop their existing approved organizational brand guidelines into the interface. The system will detect from the document key information including tone of voice, brand values and other guidelines. These can also be edited manually. Similarly, personas or brands can be created manually or by upload. Currently there is no mechanism to connect to a PIM or data file for a list of products.

A marketer can create content from a template, similar to creation in Adobe Experience Manager. When a user creates content, they are presented with options to select a brand, persona and product. Additionally, they can select an image from the asset library and enter a prompt that will be used to generate the content. The system will then generate four versions of the content. Each version is individually scored for compliance to the brand guidelines and can be edited or regenerated. A marketer can then select one or more for approval for publication or export. This supports a case where multiple versions may be delivered for A/B testing.

The asset library simplifies content reuse by saving variations for future campaigns, making it easier to manage and organize creative assets. Assets can be assigned directly to campaigns, streamlining the process for marketers. While template configuration currently relies on HTML, the tool makes it easy to scale and customize content for different needs.

Future updates will further enhance GenStudio for Performance Marketing’s impact.

Adobe Firefly integration for asset generation is expected in upcoming updates, further expanding creative possibilities. Unlike other generative AI options on the market, Firefly is purpose-built for enterprise use. It offers commercial safety and provides precise controls to ensure training data is accurate and responsibly sourced, making it a standout choice for businesses.

The activation mechanism is not yet enabled in the current release. Still, the product documentation suggests that publishing to existing Adobe products will be supported, as will a generic CSV export. Adobe has also announced activation partners including Google’s Campaign Manager 360, Meta, Microsoft Advertising, Snap and TikTok.
 

The real power of the solution will come by providing data and to marketers on the performance of their content—for example, tracking impressions, click-throughs and the ROI of each—enabling them to make decisions in real time. The current release allows connection to Meta, but future connections to the Google Marketing Platform, TikTok, and Adobe platforms will give the marketer real-time insights into campaign and content performance. This would also be a great candidate for automation so that the marketer can define boundaries to change or publish, such as the ability to push winners via Auto-Allocation in Adobe Target.

Adobe has embraced interoperability in all its enterprise platforms, and GenStudio for Performance Marketing is no exception. In my ideal world, customers and partners could then build their own interfaces for publishing and insights. If these extensions can be made available on Adobe Exchange, the power and leverage of the platform will grow very quickly. Additionally, I feel there are options for external tools to integrate and extend, such as a wider generative integration or finely grained workflows such as those provided by our own solution Monks.Flow.

Final thoughts.

Overall, GenStudio for Performance Marketing embodies a genuine shift in Marketing Operations. It is built with the user and organization in mind and matches the modern-day enterprise's goals. It is still early, and the gen AI platform will evolve quickly, but it is refreshing to see Adobe's investment prepared for the next stage in the GenAI journey.