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.

Providing a digital experience that’s more closely aligned with what a user wants is a fairly obvious answer to why we experiment, but the way we approach it as an industry is changing.

Brands are now focused on delivering a well-timed cross-channel journey, to a wider volume of users with increasingly high expectations for personalized experiences. Isolated tests and experiments aren’t providing enough information on the entirety of a user's journey. 

So is experimentation really “dead,” as data thought leaders have been saying? Are we ready to move on to the next latest and greatest framework that generative and agentic AI have to offer?

Recently, reading Avinash Kaushik’s latest TMAI newsletter made me pause and reflect on where we’re heading as an industry. It hit the spot on many things I’ve been thinking about, specifically in how experimentation practices are evolving, and what that means for the future.

For years, A/B testing has been a cornerstone for optimization, and it’s a method that has taught the industry a lot. 

The truth is, A/B testing itself isn’t dead; it’s evolving. Or, better yet, it has to evolve. It’s not the method itself that’s to blame for the limitations we sometimes see, it’s the mindset attached to it. When A/B testing is treated as a checkbox activity, focused only on incremental changes, or siloed from other disciplines like search engine optimization (SEO), paid media, or product, its potential impact is greatly diminished.

The future of experimentation is more holistic, integrated and focused on delivering real-time, user-centric experiences. This doesn’t mean abandoning A/B testing, but expanding the lens to incorporate more sophisticated strategies that blend multiple disciplines, like SEO, conversion rate optimization (CRO), paid media and analytics.

It’s important to clarify that while experimentation as a function is typically iterative and methodical, its role extends beyond testing. Experimentation should inform and support the delivery of dynamic, real-time brand experiences, though delivering in real time itself is more closely tied to optimization, personalization and UX. 

The two complement each other: experimentation provides the precision and insights to optimize, while real-time strategies act on those learnings to adapt experiences dynamically.

This distinction matters because experimentation, while not inherently “real-time,” lays the foundation for modern, adaptable and user-centered approaches that make brands more agile. By focusing on how these disciplines work together, teams can move beyond traditional silos to deliver more innovative, more integrated strategies.

I’m not a classically trained CRO practitioner. My background is in digital marketing, product and growth for over 14 years. For me, experimentation has always been one tool in a much bigger toolbox: a way to make better, more precise decisions with proactivity and pragmatism at the forefront, always focused on ROI and accomplishing business goals.

When I approach experimentation, it’s never been in isolation. It’s always been CRO + Product, CRO + SEO, or CRO + Growth. I don’t see it as just tweaking elements or optimizing individual touchpoints. I’ve always viewed it as part of a bigger strategy to align business goals, user intent and data. This perspective has shaped how I think about the future of experimentation and how we do not need better tools, we need better thinking.

It’s important to recognize the incredible work already happening. Many teams and practitioners are pushing the boundaries of experimentation by adopting innovative approaches. They’re rethinking the entire user journey, using data in smarter ways and aligning optimization efforts with broader business goals.

And let’s give credit to the experimentation community as a whole: the content, tools and thought leadership that the community shares are invaluable for helping others grow and adapt. From blog posts to podcasts  or conferences to open discussions on social platforms, the spirit of collaboration and knowledge-sharing is what drives this industry forward.

One of the ongoing challenges with experimentation is how we define and approach it. The rapid evolution of testing has unintentionally muddied the waters with organizations vying for the latest and greatest way to provide better connected user journeys.

Experimentation, CRO, A/B testing: these terms often get used interchangeably, and sometimes the definitions create unnecessary silos. The truth is, good experimentation should already encompass the broader strategies we associate with "advanced" practices, like behavioral targeting or dynamic personalization.

The issue isn’t the name we give it, it’s how we implement it. When experimentation is reduced to tweaking surface-level elements like button colors or headlines, it misses the bigger picture. True experimentation involves connecting the dots across the customer journey, testing bold hypotheses, and focusing on business outcomes like lifetime value, retention, and revenue growth. 

While A/B testing remains a valuable tool, A/B testing’s greatest impact comes when it’s part of a larger ecosystem. 

Imagine a hypothesis born out of an SEO content strategy, validated through paid ad experiments and optimized for conversion through CRO. Creating and running tests now includes greater customer sentiment, considers how it fares against public interest and search volume, or creates the foundation for analyzing the entirety of a customer funnel. This kind of collaboration doesn’t focus on improving one metric, but aims to deliver a cohesive, end-to-end experience that aligns with both user intent and business objectives.

Moving toward this holistic approach requires more than just tools; it requires nurturing a culture of collaboration, curiosity and a strong foundation in data within your teams.

Based on my experience and the clients I have been servicing, I noticed that the teams that excel at experimentation have a few things in common:

1. They integrate data as a central pillar for decision-making, ensuring insights drive hypotheses and actions.
2. They embrace cross-functional collaboration, involving SEO, Paid Media, Product and UX teams in the process.
3. They prioritize big-picture hypotheses tied to business outcomes like customer lifetime value, retention and revenue growth.
4. They balance qualitative insights (customer voice, user research) with quantitative analysis to uncover deeper truths about their users.

This maturity doesn’t happen overnight, but the teams that focus on nurturing and growing this culture are the ones driving the future of experimentation—and there are many companies that do that very well, like Starbucks and 
Nissan to name a few.

The future of experimentation lies in creating Real-Time Brand experiences, which means dynamic, adaptive interactions that resonate with users in the moment. While this might sound similar to concepts like personalization or CRO, it’s essential to clarify their differences. Their overlap often contributes to the definitional confusion in our industry, but when understood, can unlock an exponentially optimized user experience.

Personalization focuses on tailoring content or interactions to individual users based on data signals, such as first-party data or behavioral patterns. Conversely, CRO is about optimizing specific touchpoints to drive better outcomes, often through structured experimentation. Real-Time Brands combine these elements into a cohesive, adaptive system that evolves with the user’s journey, leveraging insights from experimentation and the execution capabilities of personalization.

What makes Real-Time Brands distinct is their focus on delivering seamless interactions at scale, powered by advanced technologies like machine learning. These experiences aren’t about static “best practices” or one-size-fits-all solutions. With a dynamic, and increasingly personalization-driven audience, each individual user is constantly creating and adapting what they want. 

Instead, they are about continuously adapting to user needs in their journey’s context, whether recommending the next best product, tailoring messaging based on previous interactions or optimizing navigation flows based on intent.

This approach goes beyond personalization; it creates context-driven experiences that feel intuitive to the user while driving measurable outcomes for the business. The key is to view experimentation, personalization and real-time capabilities as complementary, not interchangeable, with each playing a critical role in building connected, user-centric strategies.

Real-time brand experiences are meant to create a sense of connection. 

Users are more online than ever before, and the trend is increasing every year. The average adult in the US spent over 11 hours a day interacting with media in 2018. 

In 2025, the same segment is estimated to spend over 12 and a half hours 

Making your brand, content, platform or campaign stand out against the collective trillions of individual hours spent seeing content requires providing the “perfect” interaction: a digital experience that matches the exact criteria a user needs to interact, engage and complete the ideal key action. 

Providing a Real-Time Brand experience shows users that the brand understands them, values their time and is committed to meeting their needs in ways that feel both seamless and meaningful.

For teams, this shift requires thinking beyond isolated experiments and embracing the complexity of modern user journeys, and necessitates the ability to orchestrate an ecosystem of touchpoints that work together to build trust, drive engagement and deliver measurable business outcomes.

At its core, A/B testing was designed to do just that: find an optimal version of a touchpoint that more-closely resonates with what users respond to. With AI, machine learning and a holistic approach to create iterative experiences from real-time data, brands can essentially create individualized A/B tests on a segmented and individual level that automatically self-adjust as interaction data comes in. 

As technology evolves, Real-Time Brand experiences will increasingly become the standard for experimentation. They represent the future of connecting with users in a way that feels personalized, proactive and perfectly aligned with their expectations.

The future of experimentation isn’t a rejection of the past but an evolution toward something bigger.

Remember, A/B testing is not dead, but the mindset around it must evolve. To unlock its full potential, teams need to move beyond isolated, surface-level tests and embrace holistic approaches that connect experimentation across SEO, paid media, product and CRO.

Modern experimentation prioritizes dynamic, real-time brand experiences that adapt to individual user needs. By blending personalization, behavioral targeting and experimentation, brands can create seamless, meaningful interactions that drive both user satisfaction and business outcomes.

The success of experimentation lies not in the tools alone, but in the mindset and culture behind it. Teams must nurture and reward collaboration, prioritize bold hypotheses tied to business goals and focus on delivering real value to both customers and the organization.

To everyone in the industry, whether you’re just starting or leading the way, thank you for pushing the boundaries and keeping this space vibrant.

Experimentation will always be a cornerstone of digital optimization. The core components of why we experiment are exactly the same: provide an opportunity for interaction that works a little bit better. Its future, though, lies in the connections we build, the problems we solve and the meaningful experiences we create together. 

Want to explore how to take your experimentation program to the next level? Let’s get in touch below.

As EVP of Innovation, Products & Solutions Development, I’ve had firsthand experience helping brands innovate through our brand model practice. This service, strengthened by our recent partnership with Adobe, delivers three key components: fine-tuned models, updated brand prompt guidelines for the AI era, and a prompt bank to jumpstart AI usage. Together, this enhances our ability to help brand marketing teams quickly plan, create, manage, activate and measure on-brand content.

As you can imagine from that description, building brand models relies on more than just technology. Change management and the people element are just as crucial. With that in mind, here is a recipe to bring data, people, processes and technology together to create powerful, bespoke brand models—and some of the successes we’ve seen so far with their implementation.

Data is essential to building brand models—but is only part of the equation.

First, it’s no secret that artificial intelligence relies on vast amounts of data for training, including customer insights, media data and historic brand IP. Therefore, it may seem as though the more data you have, the better—but it’s not that simple.

While a data lake is nice to have, its benefits will diminish over time. Brands seeking to build AI models tailored to their specific business needs require a constant stream of fresh data to adapt and improve over time. This begins with the adept orchestration of data pipelines: the process of designing, managing, and optimizing the flow of data from various sources to ensure it’s current, relevant and seamlessly integrated into your AI systems.

But equally critical is fostering a collaborative environment where your team can effectively leverage the technology and ultimately keep these models up to date. A Forrester report by Forrester VP, Principal Analyst Jay Pattisall—titled “Brand AI Models Will Reinvent How Marketing Creates Business Value”— discusses brand models in detail and hits on the role that people play in filling in those gaps left by historic data. There, he quotes me saying, “[Foundation] models are a picture from the past with data from the past. The future will be continuing to iterate with the model, and that’s the people.” Let’s dig deeper into how aligning your team around these data-driven initiatives can drive transformational change.

Organize people and pipelines into efficient, automated workflows.

We help brands re-engineer their workforce for AI readiness with a consultative phase focused on change management and breaking down silos. This involves pragmatic advisory to understand where you are in your AI journey and implementing a workflow infrastructure for your tech stack and teams, because ultimately the job of the orchestration layer—or the part of the tech stack that manages, coordinates and optimizes interactions between services, application and processes—is to amplify the best of the technology you have and the best of the people you have.

By leveraging technologies like Monks.Flow, for example, we can streamline how people and AI work together. Monks.Flow is an AI-powered professional managed service that connects employees, vendors, AI and enterprise software into efficient, automated workflows. This platform-agnostic environment is designed to work seamlessly across an organization’s existing tech stack, helping businesses achieve benefits like the integration of tools, people and processes; building business intelligence; and maximizing the impact of their marketing efforts to drive growth. Those who marry skillful use of the technology with deep brand expertise can provide strategic oversight throughout this workflow, thereby achieving stronger outcomes than if you relied on the technology alone.

A perfect example of this is an incredibly promising brand lift study we did with Forever 21. The fashion retailer leveraged Monks.Flow and Meta’s AI Text Generation to elevate the creative strategy of their shopping campaigns. We created 50 variations of Forever 21’s business-as-usual (BAU) creative and used Meta’s AI to generate copy variations powered by Llama 3. This conversion lift study aimed to measure the impact of these AI strategies on campaign performance. By incorporating AI to generate diverse copy and imagery, we were able to test and optimize for the best-performing creative. The results were impressive, showing significant improvements in click-through rates and conversion rates compared to BAU assets alone.

Talent is key to domesticating brand models and AI-generated output.

Just as it's crucial to prepare and collaborate with your internal workforce, it's equally important to engage with partners within your vertical for AI consultation and implementation. These experts are deeply immersed in your market of choice, understanding its nuances and dynamics, and can leverage this knowledge to fuel your strategic AI efforts. Moreover, a partner who knows your brand inside and out can provide tailored insights that align perfectly with your business objectives, ensuring a cohesive and effective AI strategy.

For instance, I spoke with April Huff, who has successfully incorporated AI into her role as VP of Research & Insights at Media.Monks to build audience personas and aggregate data from focus groups. As a researcher and strategist, she seeks intriguing differences in focus group data, because those unique viewpoints often trigger the best creative ideas.

She emphasized that because AI models excel at predicting the next most likely word when generating text, there’s a tendency toward homogenization of experiences—which can be valuable in market research, but not always. “What makes humans interesting is that they will  say something that is unpredictable,” she says, underscoring the role of talent who can parse these nuances and add depth to AI-generated insights.

One example of how humans teased a surprising creative insight from AI data is our work with Sephora on its “mAI colpevoli” campaign, which launched on International Day for the Elimination of Violence Against Women. We used AI to analyze online data and create three scripted monologues that retold typical episodes of gender-based violence.

The team realized the AI-generated scripts had something in common: written as monologues, they each featured a speaker who blamed herself for what happened to her. The output highlighted the pervasiveness of victim blaming, mimicking societal biases, and we realized we could use this insight to encourage audiences to reflect on their own propensity for blaming victims (or themselves) of injustices. By marrying AI insights with strategic oversight, we were able to create a powerful, purpose-driven campaign that supported women's freedom and self-expression, demonstrating how deep brand expertise and AI can work together to drive transformative change.

The secret to brand model success: people and tech working together.

The integration of artificial intelligence within the enterprise is a multifaceted endeavor that goes beyond mere data accumulation or technological foundations. To truly unlock the potential of AI, brands must focus on creating self-evolving models that adapt and improve over time, driven by continuous data generation and strategic human oversight. This approach ensures that AI systems remain relevant and effective in an ever-changing market landscape.

Ultimately, the successful implementation of AI hinges on the symbiotic relationship between advanced technology and skilled human operators. It requires breaking down organizational silos, fostering collaboration, and engaging with partners who understand both the market dynamics and the brand’s intricacies. As brands navigate the complexities of AI adoption, focusing on these strategic elements will pave the way for sustained success and innovation.