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. 

This new reality is forcing a series of tectonic shifts that are redefining the marketing landscape, starting with a fundamental change in the C-suite’s north star. For years, success was measured in clicks, conversions and short-term campaign ROI—with channels being measured in silos and teams optimizing towards their own set of KPIs without consideration for impact across other channels. But today, the top CX metric is customer lifetime value (CLV), especially as economic pressure tightens top of funnel media budgets and acquiring new customers is more competitive and expensive than ever. 

As a result, the focus has shifted to prioritizing the long-tail impact of fostering loyalty that leads to a customer’s second, third and fourth purchase. This strategic move toward CLV means also taking a closer look at which channels can be most effective for re-engagement. For years now, owned channels have been de-prioritized for the newer, more exciting formats, but brands are realizing that bringing your owned channel strategy to the forefront is critical to meet consumers' rising demand for personalization and re-engage effectively.

Success with owned channels hinges on the performance of the central CRM engine.

Owned channels are the primary vehicle for delivering the hyper-personalized experiences that build lasting loyalty and drive CLV. CRM platforms are the central hub for orchestrating this complex dance. Yet for most organizations, the operational engine required to act on these trends is often too slow, too cumbersome and too fragmented to keep up, putting the entire CLV strategy at risk before it ever gets started.

This operational gridlock is a widespread industry challenge, a fact confirmed by Forrester's recent Customer Relationship Management Marketing Services Landscape, Q3 2025 report. The report states that “marketers have long struggled to close the gap between insights and execution.” We’re proud to be recognized among the 28 notable providers in the Landscape, which validates for us what we see every day: a brilliant CLV strategy is powerless if the operational engine required to act on it is too slow, cumbersome, and fragmented to keep up.

The traditional production process is simply too slow for the real-time consumer.

For most enterprise brands, the core challenge stalling their personalization efforts on owned channels like email can be traced back to a single, pervasive bottleneck: long lead times for asset creation. The ambition to deliver timely, relevant messages is consistently crushed by a production process that is rooted in outdated practices. 

Consider the traditional workflow of creating a single promotional email: a linear, multi-stage relay race that can take up to eight weeks. It begins with a creative agency developing a brief and using that brief to then write copy, design a template and fill that template with relevant content—a process bogged down by multiple internal review cycles and handoffs across teams. 

Once approved, the static design files are handed off to a separate agency to handle turning it into a deployable email, which involves weeks of coding the asset into a functional HTML template, testing it across browsers and making the necessary tweaks. By the time an asset is finally approved, the customer moment has long since passed, and the option of now turning this into multiple variations that drive personalization is out the window. This glacial pace forces brands into the generic, batch-and-blast campaigns that do more to erode loyalty than to build it.

An AI-powered content engine provides the solution.

Breaking this cycle requires looking beyond just working harder and faster within a broken system, but embracing a re-invented model powered by AI-driven workflows. Rather than replacing the vital work of creative and strategic teams, this model empowers them with the speed and scale to escape the operational mire and focus on what they do best: understanding the customer and crafting a compelling narrative. The emergence of AI-powered orchestration tools is designed specifically to collapse that multi-month timeline into a matter of days.

With solutions like Email.Flow, our AI-powered email automation engine, this new reality begins when teams can feed campaign context directly into the system. With a simple prompt, the engine generates the entire email—producing copy, design and fully responsive HTML—all at once. Trained on all the necessary brand, audience and campaign context, it can create variations built for each segment and even show the user options based on different variables. The siloed, sequential stages of the traditional process are unified into a single, instantaneous action. Critical checks for legal and brand guidelines, once a manual and time-consuming step, are built into the workflow, making final reviews and time to market faster than ever. 

This shift fundamentally changes the nature of collaboration and review. Instead of circulating static files and leaving the technical execution to the very end, teams can export a functional preview for review. Feedback cycles are compressed from weeks to days because stakeholders are interacting with a near-final product, not an abstract design. Once feedback is incorporated, the final, deployment-ready HTML is exported, turning a cumbersome, multi-stage relay into a single, streamlined sprint.

Newfound agility allows brands to execute personalization strategies that were previously impossible.

The impact of this newfound agility is transformative, allowing brands to execute personalization strategies that were previously impossible. A leading global CPG brand, for instance, wanted to personalize the welcome series for its new loyalty program to drive deeper engagement from day one. Their goal was to create unique welcome messages for different customer personas based on how they entered the program. Using their traditional process, creating the desired variations would have taken months of coordinated effort across multiple teams, making it impossible for them to respond to new entry points that were popping up each month. Instead, we trained Email.Flow to understand the program, the unique benefits and the brand's voice. We then prompted it with information on different program entry points that it used to identify personas and create personalized versions of the welcome email tailored to each group. 

The results were staggering. The brand saw a 240% increase in member engagement compared to their previous, generic welcome email. The unsubscribe rate plummeted by 94%, a clear signal that the personalized approach was resonating deeply. Most critically, the time-to-market for this complex, multi-variant welcome journey was reduced from a months-long marathon to just two weeks. This unlocked the ability to make a powerful, relevant first impression with their most valuable new customers.

This versatility extends far beyond welcome journeys. Imagine predictive personalization for cross-selling and upselling, hyper-personalization enabled by dynamic triggers, post-purchase feedback and more.  This approach can be applied to any campaign in a CRM program where more personalization and variation are needed to drive results.

Capturing the strategic value of CLV requires a new kind of operational agility.

The ambition to build long-term relationships and capture the strategic value of CLV is a noble one, but it's a journey that depends entirely on the operational engine that powers it. If that engine is riddled with bottlenecks and outdated processes, the journey is doomed to fail before it can even begin.

While our focus here has centered on email, the principle applies to the entire content ecosystem. Being a real-time brand requires a new kind of operational agility that traditional, siloed models simply cannot provide. It demands a smarter way of working, where technology empowers creativity rather than stifling it. Building loyalty today depends on having the right partner and processes to activate platform and data assets with speed, relevance and intelligence. The brands that win will be those that combine the best data with the fastest, smartest engine to turn that data into a conversation.

For more information on Email.Flow

Discover how Email.Flow can help you achieve these results and redefine the possibilities of your email marketing campaigns by watching the following video, in which Emily Golden Stein, Director of Marketing Automation at Monks, explains how Email.Flow works.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here .

In fact, the concept that echoed through every session was the rise of the AI agent. Far from being just another chatbot, Salesforce’s powerful agentic technology, Agentforce, is positioned as a new face for the brand—a concierge, a personal shopper and a problem-solver, all powered by a company's unique data and content. This vision has the potential to reshape everything from customer service to a marketer's daily workflow.

So, let’s unpack the key announcements that build toward this vision. We’ll explore what it means to redefine customer interactions through AI agents, look at the underlying platform changes in Data Cloud and Marketing Cloud that make it possible, and contextualize how this could transform your day-to-day reality.

AI agents are transforming brand communication into a dialogue. 

For years, digital marketing has largely been a monologue where brands broadcast messages and hope customers are listening. The vision presented at Connections signals a definitive move away from this dynamic. The future showcased by Salesforce is one of dialogue, where every touchpoint becomes an opportunity for a meaningful, two-way conversation orchestrated by AI.

This marks the end of the "do not reply" email. Imagine sending a promotional email with a curated set of product recommendations. Instead of that being the end of the interaction, it becomes the beginning. A customer could reply directly to the email with, “I like those pants, but do you have them in blue?” and Agentforce, acting as a personal shopper, would respond with available options, check inventory and even help complete the purchase. This transforms a static campaign into a dynamic, personal shopping experience.

This shift also forces us to rethink the purpose of content. We’ve traditionally viewed content as a destination; the goal was to get a visitor to land on a blog post or a product page. In the new paradigm, content becomes fuel for the AI engine. That blog post about new product features is no longer just for human readers; it’s a critical input that teaches AI Agents how to intelligently discuss those features with a customer. Every article, every product description, every FAQ becomes a knowledge source that makes the brand’s AI smarter and more helpful. 

Ultimately, this leads to a new front door for your website. AI Agents will become the primary way visitors interact with a brand online, moving beyond static navigation and simple search bars. It will be a conversational interface that can answer complex questions, make nuanced recommendations and even take action on the user's behalf, like booking a meeting or making a reservation.

A unified platform makes all your data ready for AI.

This conversational future requires an immense amount of power running behind the scenes. The strategy presented at Connections rests on two pillars: unifying the marketer’s experience with the Marketing Cloud Next platform and making all data AI-ready with major enhancements to Data Cloud.

One of the key enablers of this vision is the forthcoming Marketing Cloud Next platform, built on Salesforce Core. It’s designed to solve the problem of platform fragmentation that has long challenged marketers who navigate disparate tools for different channels. Instead, the new vision is one of consistency, where the experience is seamless from one tool to the next. The HTML editor for building a web campaign will look and feel just like the one for creating an email. The same powerful product recommendation engine will be used across every channel, from web to email to mobile. And a single, unified AI brain—Agentforce—will power every interaction. This consistency will not only streamline workflows but also make it significantly easier to cross-train teams, allowing them to become true cross-channel experts.

Of course, any AI is only as good as the data it’s trained on. This is where the enhancements to Data Cloud become critical. Salesforce showcased features designed to supercharge the process of getting data ready for AI agents and recommendation engines.

• A renewed focus on data foundations: While new features are exciting, they amplify the need for a solid data foundation. Without it, brands face common challenges like low-quality data that erodes trust, data sprawl across disconnected systems, and limited access that prevents teams from acting on insights. Getting the foundation right is the prerequisite to unlocking the true power of AI.
• Smarter, broader data resolution: We’re familiar with identity resolution for customer profiles, but a key enhancement is the extension of these resolution rules to all Data Model Objects (DMOs). Imagine being able to resolve your entire product catalog, ensuring clean, consistent product data for your AI to use.
• Making unstructured data usable: One of the most powerful announcements was the ability to "vectorize" unstructured data. In simple terms, this is a kind of technological magic that allows AI to understand the meaning and context of content without needing manual tags. The full text of a blog post can now be used as if it were perfectly structured data, allowing the AI to mathematically determine which parts are most relevant for answering a specific question.
• Secure, seamless collaboration: To further break down data silos, Salesforce announced Data Cloud-to-Data Cloud zero-copy connectors. This will allow different business units, or even different companies, to join their Data Cloud instances together. These “data clean rooms” enable data sharing for richer insights in a completely privacy-compliant way.

AI will become an essential co-pilot for every marketer.

So, what does this all mean for the marketer? Their role will evolve from a manual builder to something more akin to a creative director. Using Agentforce, a marketer will be able to generate campaign ideas, customer journeys and even certain components of creative assets simply by providing a strategic brief. Crucially, this output is fully editable. The marketer remains in complete control, but their time is freed up to focus on strategy, creative oversight and optimization, rather than the nuts and bolts of campaign setup and creative production.

Monks is also building tools that empower marketers to take their ability to scale content to the next level. Monks.Flow can be integrated into Salesforce to supercharge a marketer's ability to build net new creative assets that power these journeys and are incorporated into these HTML templates to support better, smarter dynamic personalization.

Collaboration will also become more fluid. These AI agents can be invited directly into Slack channels, acting as a new kind of teammate. Teams will be able to brainstorm with the agent, review its proposals in real-time and collaborate on campaign elements in the same digital space where they conduct the rest of their work.

Finally, this changes how we approach analytics. Instead of digging through complex dashboards and manually cross-referencing reports, marketers will be able to use AI dashboards to get instant, natural-language summaries of campaign and channel effectiveness. This boosts the productivity of not just marketers, but data scientists as well, allowing them to focus on deeper strategic questions instead of routine reporting.

Brands can prepare now for this conversational shift.

The message from Salesforce Connections was clear: the future of marketing is a shift from monologue to dialogue. This approach is built on the pillars of a unified platform that simplifies execution, smarter data that makes every piece of content valuable and AI co-pilots that empower marketers to work more strategically and creatively.

The key takeaway for brands isn't simply to "buy more AI." It's to start thinking differently today. How can we prepare our content to be AI fuel? How can we structure our data to be ready for these new conversational experiences? Those who begin asking these questions now will be the ones best prepared to lead in the conversational future. The time to prepare is now.

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.