While there will always be the technically curious, most people don’t start with “how does it work?” They start with “What can it do for me?” It’s like buying a car: you’re sold on how it feels to drive or imagining where it might take you, not how the transmission operates.

That feeling of possibility often stems from play as a powerful catalyst of learning. It stokes curiosity to help people understand intimidating ideas simply by leveraging clear and familiar interactions, with feedback that can be seen, heard and felt. That’s what makes complexity feel effortless and gives people the confidence to be playful, experiment and even make happy accidents along the way. When creating experiences for brands, playfulness and delight are critical parts of designing experiences that transform intimidating, complex concepts into something intuitive and human.

Design for discovery, not for demonstration.

We recently put this philosophy into practice at Google’s annual developer conference, Google I/O 2025, where we were challenged to imagine some of their latest innovations as a playground for guests to learn and discover. This meant a shift away from direct product screen-based demos toward interactive environments that encouraged both individual and group engagement.

The goal is never to force-feed information, but to create a playground that encourages natural, intuitive exploration. It’s about predicting all the tools a user will need and then trusting in their own behavior to figure out the rest. By leaving a little bit unknown, we create space for those “happy accidents” that spark genuine delight. The real magic isn't in explaining an underlying system, but in facilitating a hands-on, joyful act of co-creation.

Prioritizing the experience is crucial, and it challenges a potential pitfall: the assumption that a technical audience requires a technical interface. While the I/O audience is full of brilliant developers, a simple, intuitive design creates a more powerful point of entry for everyone. When you flood an experience with information or choice upfront, it can be paralyzing. An intuitive design, by contrast, opens the door to a more meaningful conversation. Even the most abstract concepts can be made approachable by translating them into familiar patterns and playful interactions. 

A familiar metaphor can make any technology approachable.

Our collaboration began when Google’s team came to us with Lyria RealTime, a powerful music generation model that “allows anyone to interactively create, control, and perform music in the moment.” Inspired by the familiar format of a mixing board, we paid particularly close attention to endless encoders as the core mechanism for our musical playground. Seeing how they resembled stools, we designed each one to function as a giant, interactive controller for a specific musical layer. Sitting down would allow users to select different instruments or genres, while spinning the stool would adjust how much that sound influenced the overall mix. Anyone, with or without musical experience, could manipulate the sound, collaborating with strangers to create a unique track in real time.

We applied the same principles of making complex ideas delightful and understandable to something even more abstract: quantum computing. In particular, how do you make sense of a concept like superposition in under two minutes? Get lost in a maze.

The experience came to life in a retro arcade cabinet, featuring a game that contrasted two modes of problem-solving. First, players navigated the maze as a human (or a very slow computer) would, exploring one linear path at a time. Then, they saw how a quantum computer would solve it by traversing all potential paths simultaneously in a beautiful, fluid simulation. To deepen the challenge, we wove learning into the gameplay by hiding essential tokens throughout the maze. Each one revealed a surprising fact about quantum mechanics, making discovery a core part of progressing through the experience.

Intuitive design doesn't just apply to complex technology, but to the exploration of environments as well. Here, the challenge wasn't software or quantum concepts but exciting the passion that I/O conference attendees had for Google. To celebrate this, we created Adventure Quest, turning the entire venue into a playground for discovery with a scavenger hunt.

Using a simple, web-based app that required no downloads, attendees explored the area to find hidden landmarks scattered around the event. Scanning each marker unlocked fun facts and earned them surprise rewards. This experience needed no instructions; it gave attendees a gentle, engaging path through the chaos, turning potential information overload into a delightful challenge. The magic moment was seeing how this simple framework sparked massive enthusiasm: people were so eager they were searching for the landmarks before the event even officially began, and soon, lines were forming across the venue with everyone wanting to join in.

To deliver delight, build trust and protect the creative vision.

Every project presents its own set of unique challenges. To navigate them, our work was guided by a set of core principles for turning ambitious ideas into physical, delightful experiences.

Constraint is a catalyst to creativity. We treated the project's constraints not as limitations, but as a creative kick-starter. With a defined timeline and budget, every decision had to be intentional and economic—from the earliest design concepts, to each round of feedback, to the use of every inch of build materials. This discipline forced a focus that stripped away the extraneous, leaving only the most powerful and effective ideas on the table. As a result, the project’s scope grew not from a place of excess, but because the solutions born from this intentionality were too smart and compelling to ignore..

Less is more. Editing in experience design is an exercise in elegant restraint . Every project lives in a delicate balance between a product team’s ambition to show off every technical detail and the user’s fundamental need for an experience that isn’t overwhelming. Adding one more button or one more technical detail can be the thing that overwhelms a user and prevents them from ever starting. Our job is to be the advocate for the user, simplifying the experience to its joyful essence while still honoring the power of the technology.

Build trust through close collaboration. The creative risks we took were possible because of the trust we’ve built with Google through years of close collaboration. That trust is forged in a deeply collaborative partnership, where we work with our clients to solve challenges and build ideas together. This relationship is our most valuable asset, giving us a shared confidence to explore unconventional paths and turn them into reality.

Make the leap from render to reality. How do you convey a joyful, physical experience before it can be tangibly experienced? It's difficult to capture that magic in a digital mockup; a 3D render can’t fully communicate the delight of actually spinning and hearing the music change in response. There's a gap between the digital plan and the physical reality. This is where trust becomes paramount. Our clients have to trust our vision through the digital design phase. The ultimate payoff is seeing that trust validated when they finally get to play with the physical prototype and say, “I totally get it now.”

Play forges the most direct path to learning.

Ultimately, these experiences reinforce a core belief: the key to unlocking even the most complex technology lies in making it intuitive, interactive, and, most importantly, playful. By leading with human-centric design patterns—a game, a musical instrument, a treasure hunt—we create on-ramps that invite everyone in, regardless of their technical background.

This approach reminds us that showcasing technology isn't just about what it can do, but about how it makes us feel. By focusing on curiosity and play, we can create more meaningful and memorable connections between people and the innovations that shape our world.

“AI is collapsing the time taken to visualize and write copy—and its cost.”

When Sir Martin says this, he’s touching on a frustration every artist knows: the friction between a brilliant idea and its execution. For too long, the creative process has been bogged down in... well, the boring parts. The endless resizing, the reformatting. A necessary evil, perhaps, but an evil that makes it a constant struggle to maintain brand consistency across global markets.

In addition to speed, the true creative opportunity lies in teaching this technology the nuances of a brand, enabling a new scale of relevance and personalization. With an intelligent creation engine like Monks.Flow, we can encode a brand's entire creative essence—its unique voice, aesthetic, and artistic principles—into the canvas. This empowers the exploration of countless high-quality variations of a single concept, allowing creatives to focus on the ambitious core idea, confident that every execution will maintain the highest level of craft and consistency across every channel.

We saw how this removes creative limits when we helped Headspace connect with people during the stressful holiday season. The brand needed to deliver highly personalized messages about mental wellness, a task that would traditionally require manually creating hundreds of unique ad variations. Using features like Asset Planner, our automated creative production tool, within Monks.Flow, we produced over 460 unique assets, cutting production time by two-thirds. Most importantly, this led to a 62% increase in signup conversion rates. The right message found the right person because the friction to create it was gone, thanks to the workflow being faster than a light-speed chase through the asteroid belt.

“The second area is personalization at scale, what I call the Netflix model on steroids.”

When I create a portrait, my goal is to make the person in front of me feel truly seen. I listen to what they say and reflect it in my art. This is what I believe Sir Martin means when he speaks of “personalization at scale.” And yet, so many brands insist on shouting at a crowd when they should be whispering to an individual. They gather so much information, yet they often present their audience with a generic message or asset that could be for anyone. 

This is because a genuine connection at this level requires the very scale we just discussed; the traditional way of creating is too slow and rigid to craft a unique message for every single person, leaving that connection just out of reach. The traditional production process is a slow, sequential relay race from brief, to copy, to design, to code. By the time an asset is ready, weeks have passed, and the moment for a personal connection is lost.

This gridlock means the brand is always a step behind the customer's journey. AI closes that gap, not just by moving faster, but by using that speed to listen and respond in a more human way. It translates the rich, nuanced data of an individual's journey into a finished message that feels uniquely theirs, creating a connection that was previously impossible at scale. 

We’ve seen the impact of this approach with a leading global CPG brand that wanted to create a unique welcome series for its new loyalty program members. Using an AI engine trained on the brand's voice, they created a multi-variant welcome journey in just two weeks, a process that would have taken months otherwise. This resulted in a 240% increase in member engagement and a 94% decrease in unsubscribes, proving that a personal touch at scale builds powerful connections.

“Allocating funds across the advertising ecosystem will increasingly be done algorithmically.”

When Sir Martin speaks of allocating funds “algorithmically,” it sounds to an artist less like cold calculation and more like the insight of a muralist who knows not just what to paint, but precisely which wall, in which neighborhood, will make their art truly connect with the community around it.

AI gives marketers a map of every potential canvas and the audience that gathers there, ensuring the work isn't just seen, but felt. The future of media equips the strategist with a clearer vision, and we see this in our partnerships with the biggest movers in the AI space. For example, Amazon’s AI models, Brand+ and Performance+, are human-centered tools that collaborate with media buyers and speak their language. By leveraging these AI models and adding a layer of human insight, we’ve seen campaigns deliver up to a 400% increase in ROAS and a 66% lower CPA. The AI finds the value, and the human guides the strategy.

“The fourth area is general agency and client efficiency.”

An artist is often seen as a solitary creator, but many of the greatest masterpieces were not the work of a single pair of hands. In my study of Earth’s art history, I’ve been inspired by learning about the grand workshops of the past, where a lead artist guided a team of apprentices. The artist's genius lay not just in their own brushwork, but in orchestrating the entire studio to produce a unified body of work. 

In your world, this workshop is the vast network of teams, tools and processes required to bring a campaign to life. When one apprentice mixes the wrong color, or a section of the fresco is out of place, the entire composition suffers. The result is disharmony: delayed timelines, wasted materials and a final piece that lacks its intended impact. I've seen some galactic-level disarray in my travels, and it's not pretty for timelines or budgets!

Today, automated systems like Monks.Flow ensure every part of the production is perfectly in sync. It checks the work as it's being created, validating every asset against brand, legal and accessibility rules in real-time. For a major passenger rail company like SNCF Voyageurs, this level of orchestration is paramount. Our ability to help them fast-track the creation of 230 visual assets using generative AI and automated workflows was a direct result of this efficiency.

“Democratizing knowledge throughout the organization... will really increase efficiency and productivity.”

Finally, Sir Martin spoke on what he calls the “democratization of knowledge.” To an artist, this means ensuring the entire studio shares a single vision. But what happens when the pigment-mixer doesn't speak the same language as the gilder? Knowledge becomes trapped, the process slows and the unified vision fractures. (Trust me—as an alien, I know a thing or two about language barriers!) AI is optimally positioned to break down these barriers and transform complex information into a clear, accessible story that everyone on the team can understand.

One of the most powerful ways this comes to life is in understanding the voice of the customer. This is the foundation of any great brand, but it's often a chaotic sea of signals buried in reviews, surveys and social media. Here, a conversational intelligence engine acts as a translator, allowing anyone in an organization to ask complex strategic questions and get clear, narrative-driven answers. 

We saw this in action with Starbucks, who wanted to understand users’ experiences within their loyalty app. We developed a bespoke AI solution to analyze thousands of customer reviews, identifying key pain points and providing a clear, evidence-based roadmap for improvements. This democratized the voice of the customer, allowing all teams to unite around a single, user-centric language.

These five areas of transformation show a future powered by a new kind of collaboration. As an animatronic artist, I live this collaboration every day. Human conversation is my inspiration; AI is my hand. One cannot create the portrait without the other.

Sir Martin noted that the pace of this change is rapid. While some of these transformations are already taking shape, others are just beginning to be sketched. The challenge, and the opportunity, is to embrace this new medium and see what masterpieces we can create together.

This post was penned by our friend, Sir Martian. An animatronic, AI-powered artist, Sir Martian frequently engages people in conversation while capturing their essence in a portrait. Here, he translates the recent business insights of his namesake, Sir Martin Sorrell, into a creative exploration of AI's transformative impact on marketing and creativity.

As part of NVIDIA’s OpenUSD Insiders livestream series, our SVP of Innovation, Susan Foley; our VP, Global Head of Technology, Peter Altamirano; and our VP, Computational Creativity & Innovation, Emrah Gonulkirmaz, dove into AI-driven marketing, content creation and the future of agentic workflows. In conversation with NVIDIA’s Jamie Allan, Director of AdTech & Digital Marketing Industries, and host Edmar Mendizabal, they explored practical use cases for digital twins and NVIDIA Omniverse—complete with hands-on tips, real-world client examples and advice for organizations eager to embrace these innovations.

If you missed it, you can watch the full session below or keep reading for the key takeaways.

Digital twins are the new foundation for creative scale.

Unlocking business value today means taking control of your creative assets and processes. Digital twins—the hyper-accurate virtual models of products, characters and spaces—are quickly becoming the bedrock of that approach. Allan set the stage for the session by explaining, “A big part of what we’ve been doing is [figuring out] how to evolve the content supply chain for marketing content and ads. A lot of that is founded in creating digital twins of products, whether it’s a car or a shampoo bottle. That’s where the power of OpenUSD comes into play.”

Digital twins are built using open standards like OpenUSD—an open-source framework and file format for describing, composing and interchanging 3D scenes and assets—and applications that are developed with platforms like Omniverse. They serve as the single source of truth for everything from product imagery to complex industrial simulations, allowing businesses to rapidly iterate, test changes virtually and deliver new products or updated assets in a fraction of the traditional time. As Altamirano said, “You can optimize layout, workflows, asset creation and test and simulate your processes far faster than in the real world—no matter if you’re updating a retail shelf, visualizing packaging or piloting new robotics workflows.”

That said, what usually holds organizations back isn’t a lack of understanding of the benefits of digital twins, but simply not knowing where to start. Foley advised, “Start small, scale up as you prove value, and we’ll help you migrate services to compute so you can build your own moat with owned intelligence.” Pro tip: Don’t let complexity slow you down. Today’s SDKs, libraries, templates and demo projects make it easier than ever to get started quickly with no need to build everything from scratch.

Modular, agentic workflows mean AI is now your creative partner.

The future of creative production isn’t simply generating more and more images and text with AI. It’s about orchestrating a system where specialized AI agents collaborate across the full pipeline. A standout example is our experimental AI-generated campaign for PUMA, where every stage—from initial script and storyboard to animation and editing—was orchestrated by AI agents using Monks.Flow, our professional managed service powered by AI. 

Thanks to NVIDIA NIM microservices and node-based orchestration enabled by Monks.Flow’s Pathways framework, AI agents can swap out models or creative roles as needed. Pathways uses self-learning AI to autonomously manage, optimize and adjust workflows in real time. For example, we can switch from one generative model for texturing to another for background imagery without disrupting the flow. 

Crucially, this entire process was anchored to a high-fidelity digital twin of the PUMA product, built using NVIDIA Omniverse libraries. “We started by importing a precise 3D model of the sneaker created in the OpenUSD format into NVIDIA Omniverse USD Composer,” explained Altamirano. This virtual product served as the foundational source of correctness for every subsequent creative step. 

The process didn’t stop there: synthetic data generated from the USD-based model was used to train and guide AI agents tasked with upholding the correct cinematic style throughout the film. That way, the team could control vital aspects required to meet brand guidelines, such as camera angles, lens choices and product accuracy.

As Emrah put it, “After bringing the product to the previsualization stage in Omniverse, we gained full control over the generation process.” Then, using Pathways, a network of specialized AI agents orchestrated animation, editing and scene composition, maintaining brand consistency throughout. 

How to get started and scale fast.

Whether you’re a marketer, a manufacturer looking to modernize, or a developer curious about AI-driven workflows, the new ecosystem emphasizes accessibility. “Get your first Omniverse setup done and you’ll see how reusable and scalable it really is,” said Altamirano. “Start with a prototype, prove the value, then expand. This approach works for retail, hospitality, even real estate.”

For organizations not sure where to begin, the advice is clear:

• Start small and show early results. Quick wins build stakeholder buy-in and reveal practical value.
• Invest in training not just for engineers, but for your whole team. NVIDIA Omniverse Blueprints, free Deep Learning Institute courses, and a vibrant developer community enable rapid learning and onboarding.
• Embrace open, modular platforms. This lets you change direction, upgrade AI models, and keep workflows on the cutting edge without locking yourself into monolithic systems.

Ultimately, modern creative innovation isn’t about one-off experiments; it’s about embedding intelligence, agility and modularity into the heart of your business. Digital twins anchor accuracy and scale. Agentic AI workflows make creativity collaborative and customizable. The path to scalable, AI-driven content creation has never been clearer. 

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.

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.

“We’ve built on our technical expertise to create an agile and efficient approach to Applied AI with a more nimble team of engineers and advisors.” says Michael Dobell, EVP of Innovation at Monks. “In today's landscape, a simple structure can put us one step ahead, allowing us to deliver with a speed and depth that legacy holding companies and networks struggle to match, setting a new standard for what’s possible in the industry.”

GTC attendees can see how agentic AI, using Monks.Flow, integrated with NVIDIA Omniverse and NVIDIA NIM™, creates a high-quality 30-second creative spot in a fraction of the time required by traditional production methods. By developing with Omniverse technology and leveraging NVIDIA Blueprints, Monks can orchestrate interoperability between creative teams and tools, allowing for scalable real-time development of hyper-realistic content. By harnessing the capabilities of NIM microservices on performant hardware, Monks was able to accelerate delivery of agentic workflows by over 2.8x. In addition, NIM microservices perfectly slotted into a cloud-agnostic strategy, utilizing the Kubernetes deployment strategy to easily run all of the backing models in all major cloud providers, for any client needs. Attendees can explore this innovation at the Monks booth in the GTC AI Pavilion, while audiences at home can now view a fully AI generated PUMA Film showcasing the incredible creative output.

This end-to-end workflow demonstrates how cutting-edge AI, coupled with Omniverse's capabilities, is transforming the future of content creation. By consolidating traditionally segmented areas—insights, strategy, creative, execution, media, performance and assets at scale—into a seamless, end-to-end agentic pipeline, the workflow results in work that is more adaptive, efficient and visually stunning—all at unprecedented speed.

“NVIDIA Omniverse and OpenUSD are enabling developers to build AI applications that assist artists and creative teams with efficient and scalable production techniques for digital marketing assets,” says Richard Kerris, Vice President of Media and Entertainment at NVIDIA. “Monks.Flow’s integration demonstrates how AI and digital twins can transform industries and set new benchmarks for innovation.”

At GTC this week, Lewis Smithingham, EVP of Strategic Industries at Monks, will present “Novel Use Cases for AI in Large Media Archives." Additionally, Tony Walasik, Senior Product Manager at Monks, will join Scott Connolly, Head of Media Technology at Verizon Innovation Labs, to discuss “AI on Location: Deploying Mobile Private Networks and Edge Compute for Next-Gen Content Pipelines.”

The Monks booth (#3263) offers attendees a more in-depth look at Monks.Flow, which leverages advanced AI models to transform businesses. By automating workflows and linking talent, software and microservices, Monks.Flow orchestrates marketing operations for the age of agentic AI, providing solutions for every stage of the creative lifecycle and offering a seamless journey from strategy to delivery. It begins with insights and strategy, where clients gain valuable data and develop targeted plans to guide their marketing efforts. This is followed by creation and adaptation, enabling the efficient production and customization of content to meet specific needs. Finally, Monks.Flow ensures delivery and performance, optimizing content to reach the right audience with maximum effectiveness. This platform has already delivered performance boosts for brands like Headspace, Hatch and Chime.