In an industry defined by rapid change, the conversations at Advertising Week NY provided a clear and urgent playbook for marketers. This recap moves beyond the theoretical buzz to offer a practical blueprint for how to re-architect your marketing model for a real-time, AI-powered world. Here’s what’s covered:

• Re-engineering the creative model is no longer optional. Brands must move from a linear supply chain to a fluid, interconnected ecosystem that leverages AI for speed, scale and personalization.
• ROI must be framed in the language of business value. The pressure is on to move beyond last-touch attribution and embrace holistic measurement models that connect marketing efforts directly to pipeline and revenue.
• True relevance requires an infrastructure built for speed. Building a real-time brand is less about reacting to trends and more about proactively creating an agile system of people, processes and technology that can act on cultural insights immediately.

This year, from the sessions we hosted to the conversations we joined, a clear theme of pragmatism took center stage. Conversations moved beyond the theoretical promise of new technologies to confront the practical challenges of execution: how brands can fundamentally re-architect their organizations to make real-time, AI-powered marketing a reality. The week’s discussions provided a practical blueprint for modern marketing, one centered on reimagining creative models, redefining value and building true organizational agility.

The AI mandate calls for a re-engineering of the creative supply chain.

For years, brands have operated on a linear creative supply chain: brief, ideate, produce, distribute. The consensus from Advertising Week NY is that this model is no longer fit for purpose. In an AI-powered world, the goal now is to transform the old assembly line into a fluid, interconnected ecosystem. This requires a foundational shift in thinking, moving away from rigid processes and toward agile, tech-enabled partnerships that can unify disparate teams and technologies under a single, strategic vision.

This pivot is precisely the kind of transformation Monks is undergoing with clients like General Motors. During the “Under the Hood on the New Marketing Creative Model” session, our Global Chief Client Officer, Deborah Heslip, spoke alongside General Motors Executive Director of Global Marketing Excellence, Molly Peck, to discuss the new strategies and partnerships required to thrive in this new era. The ultimate aim is a powerhouse that moves at the speed of the market. “It’s bringing everything together... what does marketing look like in the 21st century as a brand?” Peck said of the early stages of General Motors’ transformation.

This sentiment was echoed in “The AI Horizon: Shaping the Future of Work” panel at the Female Quotient Lounge, which explored how emerging AI applications will revolutionize customer experiences. Unlocking this technological promise demands a profound cultural shift. For marketing teams wondering where to begin, the answer lies in education and experimentation. The first practical step is creating a culture of learning through consistent internal education—like weekly sessions on the latest tools—and empowering teams to start asking creative questions. Fostering the curiosity to ask, “I wonder if AI could do this?” one way to kick off such initiatives. This can be as simple as using generative AI for low-risk creative tasks like brainstorming copy variations, creating image storyboards or generating mood boards to test the waters and build confidence.

Brands must move from vanity metrics to clear business value.

Alongside the push for operational transformation is a renewed, intensified pressure to prove its value. The need to connect marketing efforts to tangible business outcomes has never been greater, and the session “The ROI Revolution: Moving B2B Marketing from Vanity to Value” highlighted a key challenge: traditional attribution models, often built for B2C, simply miss the mark in today's complex buying journeys, which involve multiple stakeholders and touchpoints over extended periods.

The solution lies in moving beyond vanity metrics and last-touch attribution. Marketers must learn to speak the language of the C-suite. As Jae Oh, Director of Product Management at LinkedIn, noted, sales team doesn’t care about CPCs; they care about pipeline and revenue. “Your job is not to prove that marketing is working. Your job is to make it better.” This means embracing a more holistic view of measurement and getting marketing and sales to the same table, armed with the same data and rowing in the same direction.

This need for a more sophisticated measurement mindset was also the focus of a TikTok Luncheon on media mix modeling (MMM). The session reinforced that in a fragmented media landscape, relying on last-click attribution results in a fundamentally flawed view of media effectiveness. For marketers looking for the catalyst to bring to their CFO, the discussion provided a powerful example: one study found that TikTok captures 23x higher return on ad spend (ROAS) in media mix modeling versus last-click attribution. This is the kind of business-focused data that can justify a pilot project to quantify how much value a company’s current measurement model is leaving on the table, reframing the conversation from marketing metrics to business impact.

The push to operate in real time starts with building speed, strategy and smarter spend.

If AI provides the engine for transformation and ROI provides the map, then real-time agility is the vehicle that drives it forward. Building a real-time brand requires constructing an entire system that allows a brand to be truly relevant in the moments that matter.

The panel “When Imagination Meets Intelligence: Building Real-Time Brands with Data-Driven Precision” explored how to bridge the gap between creative storytelling and media effectiveness by emphasizing a test-and-learn methodology. The key is to design multi-dimensional creative systems that can be adapted and optimized on the fly, implementing real-time feedback loops that fuel both short-term performance and long-term brand growth.

Perhaps no group embodies this fusion of art and science better than today’s creator class. As Ronan O’Mahony, Senior Director of Brand & Advertising at T-Mobile, told our Head of NAMER, James Stephens, in one session, “You get on a phone with one [creator] and they will tell you, ‘What works for me is this, and here's what I see in my results, and here's how I think about that.’” This reality calls for a new collaboration model where creators are treated as strategic partners. Their value extends beyond content creation; they are a live feedback loop. For example, if a creator’s audience is consistently asking for a specific product feature, a real-time brand can use that insight to immediately inform a flash sale, test a limited-edition run or feed the data directly to the product team for the next iteration, turning cultural insights into business action.

Achieving this agility demands a specific organizational infrastructure and mindset, supported by technology. The “Anatomy of a Real-Time Brand” session tackled this topic head-on off-site at Adweek House, where leaders discussed how to equip their teams with the tools, data and—crucially—the risk tolerance needed to act on cultural moments immediately. This focus on proactive strategy was also central to the “Holiday 2025: Winning the Season with Strategy, Speed & Smarter Spend” discussion, where panelists emphasized a forward-looking approach. The goal is to create “seasons defined not by bigger budgets, but by smarter and more inspired marketing,” said Aisuluu Eralieva, AVP Data Driven Experiences & Audience Strategy, Consumer Products, at L’Oreal, ensuring that a brand is actively shaping the conversation.

Advertising Week NY culminated in a clear call to action.

The throughline connecting every major conversation at Advertising Week NY was clear: the modern marketing organization must be built for change. This transformation represents an immediate imperative, built on a holistic culture of innovation that seamlessly integrates AI into creative processes, measures success in terms of business value and operates with the speed and agility of a real-time brand. Advertising Week provided the forum and the focus; now, the work of putting that playbook into action begins.

At a glance:

Marketers can use AI to create differentiated and effective work by moving beyond generic tools and building a proprietary system trained on their own unique brand intelligence. Here’s how:

• True AI-powered differentiation comes not from the tools themselves, but from training them on your unique historical data, audience insights, and strategic knowledge.
• The key is to develop a custom agentic workflow—a network of specialized AI agents—that acts as a proprietary, intelligent system that understands and enforces your brand.
• This approach transforms your marketing archive from a static resource into a dynamic, compounding advantage, creating a defensible piece of intellectual property that competitors cannot replicate.

How can we use AI without losing our brand's unique voice?

When everyone is rushing to adopt the same AI tools with the same capabilities, how do you create work that actually stands out? Creative differentiation has long been a critical imperative for brands, which is taking on a new urgency as creative workflows become more automated—at the risk of becoming more homogenized in the process. So, the answer to the differentiation dilemma doesn't lie in mastering the same off-the-shelf tools as competitors. Instead, it lies in building a proprietary system fueled by a company's unique data, intelligence, and history.

As we move from the excitement and potential of the early hype cycle to the messy reality of implementation, many brands are finding that while generic AI tools are powerful, they can lack the brand-specific nuance required for high-stakes marketing if they aren’t trained on your brand’s unique intelligence. The initial thrill has been replaced by the practical challenge of making AI outputs not just faster, but better and—most importantly—distinct.

How can my brand's data create a competitive advantage in AI?

In overcoming these challenges, we must move our focus from the AI model itself to the fuel that powers it. When brands use the same public models with similar prompts, they will inevitably get similar, generic results—a race to the bottom that creates content not worth people's time.

True differentiation comes from the quality and uniqueness of your inputs. The most valuable assets your brand possesses are your own historical performance data, your nuanced audience insights, your risk tolerance, and your strategic knowledge. Altogether, these make up your brand’s DNA. More than just quantitative data, brand DNA includes the qualitative elements that are harder to measure but essential to your brand’s identity: your unique point of view, your strategic decisions about which markets to enter (and which to avoid), and your specific interpretation of complex industry regulations. These are the inputs that contain your competitive edge.

So, how do you activate this unique brand DNA, transforming this treasure trove of data from a static archive into a dynamic, intelligent engine? The path to doing so lies in building a custom, agentic workflow.

What is an agentic workflow for marketing?

A marketing-focused agentic workflow operates as a network of specialized AI agents, all trained on your specific brand ecosystem to work in concert. These agents function like a bespoke, in-house team of experts that thinks and acts like your brand, but with the capacity to operate at limitless scale.

At the heart of this system is the brand model. Training this model involves feeding the system your entire history: your visual identity system, your tone-of-voice playbooks, your compliance policies, and your competitive positioning. We enrich this with the very language from your past high-performing campaigns and the specific nuances of your regulatory landscape. The model learns not just the rules, but the spirit of the brand. This in turn delivers consistency, ensuring every piece of AI-assisted work is instantly and recognizably on-brand.

Within the agentic workflows we build for our clients, we deploy specialist agents to support the core brand model. For example, our Insights Agent surfaces relevant trends and conversations, enabling brands to move at the speed of culture and engage authentically with relevant trends. Meanwhile, Strategy Agents act as powerful accelerators for growth, helping brands identify opportunities and activate new audience segments by analyzing market data through the lens of their unique strategic goals. Insights that in the past could take one of our CPG clients 18 months to identify and activate are now reviewed and responded to in close to real time.

How does an agentic workflow become a defensible asset?

This approach transforms AI into a strategic asset that serves as a powerful engine for differentiation. While competitors rent access to generic models and compete on similar prompts, you’ve built distinct intellectual property that produces creative work no one else can.

This "brand brain" is a living system that grows smarter and more effective with every campaign it analyzes and every data point it absorbs, a virtuous cycle that turns your brand’s history into compounding advantage. In practice, this means the agents work in concert to uncover unique creative opportunities. An Insights Agent might identify a burgeoning cultural trend through the specific lens of your brand's DNA, surfacing an angle invisible to your competitors. This is passed to the Strategy Agent to propose a micro-campaign, which is then validated by the brand model to ensure it aligns perfectly with your tone. This interconnectedness is what allows you to move on distinct cultural moments with speed, relevance, and safety.

What’s even better is that insights from a successful campaign automatically inform the strategy for the next one. The nuances of your highest-performing creative become ingrained in your brand model’s DNA, making the entire system better at finding the unique intersections between what your brand stands for and what the culture is talking about, leading to increasingly differentiated work over time.

Where should we begin in building a custom AI workflow?

In the age of AI, the most durable competitive advantage won't come from a tool you can buy, but from an internal capability you build. Activating your unique data through a custom agentic system is how you make your own intelligence scalable, turning your brand’s history into a strategic asset.

Making this operational and strategic shift is a significant undertaking, and every brand is at a different stage of its journey. That’s why we engage with our clients flexibly. For some, it begins with advisory to navigate the change. For others, we stand up managed agent services to take the burden from in-house teams. And for those ready to invest in self-service, we help execute full custom builds. Regardless of where you start, the most critical step is recognizing that your brand’s history is the most powerful dataset you own.

But talk of agility is easy. The real challenge, and the focus of the conversation in Amsterdam, was how to ground these ideas in reality. How can broadcasters break free from decades-old production models? Where are the real revenue opportunities in vast, dormant media archives? And how can businesses adopt powerful new technologies without being crippled by cost and complexity? The discussion needed to shift from conceptual promises to tangible solutions, with many on display throughout the conference.

Decades old broadcasting models are evolving to meet modern audience demands.

For decades, the broadcast industry has stood on what seemed like solid ground, but that foundation has irrevocably shifted. As Lewis Smithingham, EVP MEGS at Monks, noted on stage, “Media production has been done effectively the same way for something like 58 to 59 years.” But that traditional model is breaking under the strain of a new reality where audiences are no longer a monolith, but a diverse collection of interests scattered across countless platforms. To reach them, he explained, “We can't deliver with a straight line of sight anymore, because there isn't a straight line, and there is no primary platform. It's all over the place.”

This new landscape demands a new approach that breaks free from the institutional inertia of how things have always been done. Reaching modern audiences requires the agility of cloud-native production, which is more a fundamental change in mindset than merely a technological upgrade. It means letting go of old “golden rules,” such as never turning off a generator for fear of a system collapse, and instead embracing an agile, software-defined approach that manages systems through adaptable software rather than rigid, physical hardware.

Nowhere is this shift more apparent than in the evolving role of the media archive. For too long, valuable content has been locked away in dusty vaults. Now, AI is rewriting that playbook, transforming stagnant libraries into living, breathing performance archives. 

This evolution means moving beyond thinking about rights on a per-title basis and seeing the monetization opportunities in the underlying IP. For a sports broadcaster, this could mean using AI to instantly find and package player highlights following a high profile trade—a process that would traditionally take significant manual effort. For a studio, it means transforming a classic radio show into an animated series for social media. By democratizing archives with AI-powered tools, we give editors, producers, and even fans the ability to unearth new value, create new stories, and generate novel revenue streams from content that was once forgotten.

Ultimately, these technological shifts point to a single imperative. As Smithingham simply put it: in today's environment, “if you're not real time, you’re history.” Success now comes from using technology to transform dormant IP into the dynamic, personalized experiences that connect with audiences in real time. This is the new frontier of broadcasting, and it's a future we are actively building.

AI-powered tools empower teams to deliver better viewer experiences.

The ultimate goal of all this innovation is to deliver a better, more personal experience for the audience. One of the central themes of the talks throughout the show was the need to empower creators with agile, real-time tools that make this possible. Our LiveVision™ demo illustrated this point in action.

LiveVision™ is an AI-powered tool that operates within live production workflows. Built to run on a full stack of NVIDIA hardware and software technologies including NVIDIA RTX PRO Server, Video Search and Summarization Agent, and Holoscan for Media , LiveVision™ can be deployed on edge, in the cloud, or in a hybrid environment. It brings real-time intelligence to the broadcast by analyzing multiple camera feeds simultaneously, introducing object detection and analysis into the broadcast pipeline for shot prioritization, scene description, and audio transcription. This frees up production teams for the creative process and provides end-of-day summarizations.

New tools are turning dormant media archives into new revenue streams.

Our Time Addressable Media Storage (TAMS) demo offered a powerful solution to the challenge discussed above of unlocking value in dormant media archives. This demo showcased a practical application of the industry's shift from static storage to “performance archives”—systems that actively surface valuable content.

TAMS tackles the time-intensive process of manual search by using AI to analyze and index the content itself, making footage searchable by actions, objects or people within seconds. This provides instant, frame-accurate access to massive media libraries, transforming a dormant archive into a dynamic, monetizable asset. For example, a sports rights owner could use TAMS to instantly pull clips of a specific player's key moments to create a personalized highlight reel for fans, or quickly curate footage for live betting markets.

The industry’s future depends on a fundamental shift in mindset.

Ultimately, conversations at IBC 2025 painted a clear picture of an industry at a crossroads. It's clear that the transition to a real-time, AI-driven world requires more than simply adopting new tools and platforms. The real challenge is to overcome the inertia of legacy workflows and embrace a more agile, experimental and software-defined approach to creating and monetizing content.

Success in this new era requires orchestrating creativity and technology to build systems that are not just innovative, but deeply relevant, efficient and profitable. It’s this synthesis of vision and execution that will define the next chapter of media.

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.