For several years, the industry’s focus remained almost entirely on training—the massive, capital-intensive process of teaching models to understand the world. The world now prioritizes inference: the moment those models are put to work to generate actual value. In his keynote, Huang underscored this by projecting $1 trillion in AI infrastructure orders through 2027, a signal that the global economy is now betting on the sustained production of intelligence.

Since the beginning of the year, we have maintained that the industry has moved beyond the AI pilot phase. This shift fundamentally redefines the creative supply chain, moving us toward the development of AI factories. So, in order to maintain real-time relevance, CMOs must now transition from managing manual tasks to orchestrating an autonomous, high-performance workforce augmented by AI. 

New architectures enable productivity at the speed of thought.

If the previous generation of hardware was the “big bang” of model creation, the new Vera Rubin architecture is about the work of model execution. This platform is a structural redesign of how AI is put to work. By integrating specialized processors—specifically the new Groq 3 LPX—NVIDIA has solved the primary bottleneck for global brands: the sluggishness of AI. While older systems felt like waiting for a high-powered calculator to finish a task, this new architecture allows AI to process information at the speed of thought.

For a brand, this technical leap translates directly into always-on productivity. In the past, AI was a “pull” technology—a tool that sat idle until a human prompted it. In contrast, the efficiency of the Vera Rubin platform changes the physics of the creative supply chain. It provides the horsepower required for AI teammates to work in the background, 24/7, without the prohibitive costs or lag times that previously stalled enterprise adoption.

Agents are increasingly executing more complex enterprise tasks. 

If the Vera Rubin architecture is the factory floor, then OpenClaw and NemoClaw are the workers. GTC 2026 showcased the maturation of agentic AI—systems that don't just process text, but can see, plan and act autonomously. Huang described OpenClaw as the "operating system for personal AI," a framework that allows these agents to move beyond simple chat interfaces and execute complex missions across enterprise workflows.

The challenge for any global brand is that autonomy without control is a liability. This is where NemoClaw enters the picture. While OpenClaw provides the raw capability for agents to act, NemoClaw provides the enterprise-grade "how." It’s a production-ready stack that layers in essential security sandboxes, privacy routers and policy engines. These ensure that an agent doesn't drift outside of brand guidelines or legal guardrails.

To bridge the gap between powerful technical frameworks and day-to-day brand operations, we deploy Monks.Flow, our AI ecosystem for marketing orchestration. Rather than treating agents as isolated tools, Monks.Flow creates a bespoke system of intelligent agents that reason, plan and execute across the entire marketing lifecycle. This approach transforms the traditional creative supply chain into a fluid, real-time engine, allowing brands to move from a morning strategy session to a full-scale deployment by the afternoon.

We deploy Monks.Flow as a systems integration partner, providing the connective tissue required to make this technical potential a practical reality. By orchestrating elite talent alongside agentic machines, we help brands move past fulfilling manual tasks and toward managing a high-velocity workforce that operates at the speed of social conversation.

Data is key to giving AI definitive direction.

If the hardware provides the horsepower and the agents provide the labor, data provides the direction. One of the most significant themes of GTC 2026 was the reinforcement of structured data as the definitive foundation for reliable AI. As Huang noted during the keynote, "Structured data remains the definitive ground truth for enterprise applications."

This is where many brands still face a silent bottleneck. While the industry has been enamored with the creative potential of unstructured data—images, videos, and conversational text—the reality is that autonomous agents require organized, governed data to act with precision. To address this, NVIDIA highlighted cuDF, its GPU-accelerated library that brings massive speed to data processing. By moving data analytics from CPUs to GPUs, tasks that previously took hours are now reduced to minutes, enabling the real-time feedback loops required for an agentic workforce.

In our talent and machines model, this data layer connects brand strategy directly to market execution. By mechanizing the Four Cs—company, consumer, competitor and culture—we can provide the agents in the factory with a real-time flight simulator, allowing them to pressure-test creative concepts against cultural white space before a single dollar of media is committed.

The success of this orchestration relies on a new standard of data accountability. Because every reasoning decision, content reference, and prompt seed is drawn from a structured data layer, it becomes part of a fully auditable trail. This transforms the black box of AI into a transparent system of record, ensuring that high-stakes marketing missions are grounded in proprietary brand DNA and meet enterprise-grade standards for safety while operating at the speed of social conversation.

Orchestration will win the relevance race.

The convergence of the Vera Rubin architecture and agentic AI signals a fundamental shift in the creative supply chain. GTC 2026 provided the definitive blueprint for this new industrial reality, moving the industry beyond the novelty of discovery toward the precision of execution. For global brands, the AI pilot phase has officially transitioned into the era of the high-performance AI workflow.

This shift signals the arrival of zero-distance marketing. As agentic systems collapse the legacy gaps between brand awareness and the transaction, the traditional marketing funnel is effectively flattened into a single point of interaction. Discovery and conversion now happen simultaneously, driven by intelligent agents that identify and capture intent in the exact moment of need.

Winning the race to relevance is now a matter of orchestrating at the speed of culture. Structural advantage no longer comes from manual tasks or isolated AI experiments, but from a CMO’s ability to scale operations. The post-agency era marks a definitive shift from fulfilling individual briefs to building proprietary AI factories—environments where elite talent and agentic machines collaborate in a continuous, real-time loop. 

The question is no longer "How can AI help our teams?" but "How quickly can we build the system that orchestrates our future?" By acting as a systems integration partner, we are helping brands bridge the gap between this technical potential and practical, day-to-day application, ensuring that the factory floor is ready for the demands of a real-time world.

The data backs up this friction. MIT’s 2025 report, The GenAI Divide, finds that while 80% of organizations have explored or piloted generative AI, only 5% of integrated enterprise AI pilots have reached production with measurable P&L impact. This stagnation happens because businesses attempt to force exponential technology into linear, outdated workflows. They treat AI as a high-speed intern rather than a reason to rebuild the marketing operating model.

These conversations increasingly suggest that competitive advantage no longer lives in the individual assets a brand creates, but in the systems that produce them. This focus on foundational plumbing necessitates a new kind of partnership—one that moves beyond fulfilling static briefs and toward building the architecture for autonomous marketing.

It’s time to shift from interfaces to architectural systems.

This evolution from interface to architecture is best captured by the transition from “human in the loop” to “human in the lead.” This shift represents a fundamental evolution in the creator’s relationship with technology. In the loop model, humans often act as a bottleneck, manually approving every incremental AI output. In the lead model, humans act as architects, designing the systems and agentic workflows that handle the heavy lifting of execution.

“You’ve always got to start with your brand strategy first,” said Leisha Roche, CMO, Picton Mahoney Asset Management. “Brands who understand their brand strategy, know what their conviction is in the world, understand what their identity is—their look and feel, their tone, how they're showing up—you're always going to be in a better place if you do that.” In this model, humans act as architects, designing the systems and agentic workflows that handle the heavy lifting of execution.

This architectural mindset was the focal point of our 25 Minutes of AI session, where the conversation shifted away from perfecting individual prompts to focus on the broader engine powering them. As Olivier Koelemij, Chief Innovation Officer at Monks, noted alongside Sneha Ghosh, EVP Data, NAMER, “It’s not about the creation of the asset anymore; it’s about the creation of the system—the underlying design system that produces not only that one asset, but the next thousand.” 

This change is driven by a velocity mandate. Cultural moments now move in minutes rather than weeks or months. To operate at this speed, brands require an orchestration layer that connects autonomous agents to handle essential but repetitive tasks like tagging, resizing, and legal checks.

Monks.Flow serves as the primary example of this intelligence layer in action. By automating deep research and creating concise, 360-degree brand views within seconds, it allows teams to skip the weeks of manual synthesis that traditionally stall a go-to-market strategy. This type of foundational plumbing enables creatives to prioritize strategic orchestration over high-volume manual labor.

By orchestrating interconnected agents rather than isolated tasks, organizations can bridge the vision-reality gap. This marketing operating model relies on agents for high-velocity production while humans provide the strategic conviction and taste that models cannot replicate.

Marketing and IT break silos to fuel growth.

Designing an agentic system is only half the battle; the other half is reorganizing the leadership that governs it.  For years, the tension between marketing's desire for speed and IT’s requirement for stability has created friction. In an era of autonomous orchestration, mismatch is no longer sustainable.

Gaurav Mallick, Senior Global Industry Strategist at Adobe, noted that the organizations making the most progress have leaders who design workflows together from the start. This approach moves away from isolated pilots and toward shared accountability. When marketing, IT and legal teams align on outcomes first, technical constraints stop being blockers and instead become design inputs for the system.

The most effective organizations are replacing traditional department silos with integrated squad or pod models. These multidisciplinary teams combine media, tech and creative roles to manage the flow of data and content in real-time. This structural change ensures that the data plumbing—the technical foundation required to ingest, label and activate customer insights in milliseconds—actually fuels the creative output. As Ryan Fleisch, Head of Product Marketing, Real-Time CDP & Audience Manager at Adobe, emphasized, this plumbing provides the real-time context needed to make every creative impression relevant. Every data point must be ready for immediate activation to avoid the delays of traditional processing.

As Wes ter Haar, our Chief AI & Revenue Officer, summarized, the industry is moving toward a moment where the commercial and operational models must collapse. “AI allows you to start collapsing those steps and silos,” he noted, emphasizing that the ability to transform quickly depends entirely on the connection between the CMO and CIO. Scaling AI requires a unified architecture that provides both the creative freedom to move at cultural speed and the technical guardrails to protect the brand.

Human taste remains a key differentiator.

As the technical barriers to high-volume production fall, the primary challenge for brands shifts from execution to differentiation. Leadership teams are finding that the ease of AI generation has created a new crisis: a flood of generic, automated content often described as AI “slop.” When every brand has access to the same models and optimization tools, content risks regressing toward a bland, predictable average.

This human element provides the conviction needed to take risks—and the oversight to ensure the machine isn't hallucinating its own success. AJ Magali, Head of Performance Marketing at Cadillac (General Motors), highlighted this during our discussions, noting that as brands become more dependent on automated tools, a human must still be there to ensure the “story actually makes sense” and to step in when the underlying data—like a broken tracking pixel—fails the system. This intuition is what allows a brand to spot the unconventional strategies that are invisible to binary testing.

This focus on human connection creates what leaders are calling “emotional ROI.” In a marketplace saturated with prompts, brands are leaning back into high-fidelity storytelling and physical presence. Jess Kessler, Head, Brand & Content Marketing North America at Audible, pointed out that while AI can mimic digital trends, it cannot replicate the energy of a physical space. "AI can mimic any trend online now, but it can’t fake a room," Kessler noted. "That is the magic you can’t generate with a prompt."

In the agentic era, the role of the creator is evolving into that of a curator and a designer of meaning. While the machine handles the scale, the human provides the soul. As ter Haar observed, while AI progress puts many skillsets on the table, taste will remain a predominantly human skillset for years to come. Enduring brands will use their agentic architecture to clear the path for human intuition, ensuring their messages resonate with an authenticity that no model can replicate.

Design for the speed of culture.

The prevailing sentiment from SXSW 2026 is that the era of experimentation is over. For brands to survive the transition to an agentic future, leadership must move beyond isolated pilots toward a total reorganization of their marketing operating models.

This transformation requires modern leadership teams to prioritize infrastructure over interfaces. Success no longer depends on finding the perfect prompt for a single tool, but on building the foundational plumbing that allows autonomous agents to work in concert across the entire organization. This shift naturally forces the collapse of traditional C-suite silos, moving toward a unified architecture where marketing, IT and legal teams share accountability for real-time outcomes. 

Central to this new model is the preservation of taste. As automated content begins to saturate the market, human intuition and emotional ROI remain the only sustainable methods for achieving true brand differentiation.

The speed of this evolution can feel overwhelming, but it also presents a unique window of opportunity. As Koelemij noted in closing his presentation: “Today is the worst this technology will ever be.” The capabilities of these systems are improving exponentially every hour. 

The gap between those who use AI as a tool and those who use it as an architecture is widening. Closing that gap requires technical adoption coupled with the strategic conviction to rebuild for a world where humans lead and machines orchestrate. The infrastructure built today will determine which brands can move at the speed of culture tomorrow.

Discussion about the potential of agentic AI isn’t particularly new. But if the beginning of 2025 was about the promise of autonomous agents, re:Invent was about implementing the plumbing required to make them work at scale reliably, with proper governance, and at scale—moving past simply building agents to building them well. This maturation of infrastructure, from silicon to software, is a continuous effort focused on the reliability, resilience and enterprise compliance needed to support the agentic era. By simplifying these foundational layers, AWS is accelerating the work we do for global customers, allowing us to move faster from concept to secure, end-to-end autonomous workflows.

For customers, this shift requires a new strategic roadmap. Here is what you need to know about the transition to an agent-led future.

Frontier agents are transitioning from reactive chat to complex, 24/7 orchestration.

The headline coming out of AWS leadership is a strategic pivot from simple assistants to autonomous AI agents, governed with strong foundations and data-driven development. To understand the difference, think of a chatbot as a reactive tool that waits for your prompt to generate a single response. An agent, by contrast, is designed to collaborate over time, handle multi-step tasks, and work independently to achieve a goal.

AWS introduced the concept of Frontier Agents, or AI teammates capable of handling highly technical tasks like DevOps and Security. While these initial use cases are technical, the implication for marketing operations is profound. We are moving toward a reality where an AI agent can not only write a campaign email but orchestrate the entire deployment: autonomously segmenting the audience, setting up A/B tests, monitoring performance in real-time, and even adjusting ad spend based on ROI targets without needing a human to click “send” at every step.

This creates a “serverless-first” culture where the bottleneck is no longer content creation, but orchestration. To succeed, customers will manage a workforce of silicon agents executing strategy at the speed of software.

Expert agents require more than just a powerful model.

Building high-quality agents requires a closed-loop system, not just a smart LLM. It starts with trusted, permissioned data that is transformed into a rich, multi-layer context. By moving beyond basic search methods and using techniques like hybrid retrieval (combining keywords and context) and graph traversal, organizations can give agents the precision and "common sense" they need for enterprise use.

However, data is only one piece of the puzzle. At re:Invent, AWS emphasized that agents must operate within a strict architectural contract to remain safe and predictable. This includes "least-privilege" security—giving agents only the specific tools they need—and clear decision boundaries. Observability has also become foundational; every decision an agent makes and every tool it calls must be traced and attributable back to a source. By embedding automated quality checks and human-in-the-loop safeguards, organizations can turn unpredictable AI into reliable, enterprise-grade systems of record and action.

Expertise delivers the AI “last mile” of value.

A consistent theme across the 2025 tracks was that while AWS provides the powerful building blocks, like Amazon Bedrock, the “last mile” of value is found in the integration. The industry is moving away from treating AI as a standalone tool and toward integrated AI services that bridge the gap between cloud infrastructure and specific business outcomes. Closing this gap is how organizations are finally escaping proof-of-concept purgatory and realizing significant gains in efficiency and engagement.

On the operational side, we are seeing the emergence of brand intelligence systems that solve the “hidden tax” of internal friction. A representative example is a solution we recently built for a global technology leader, which moved beyond a standalone tool to become a core enterprise integration. By seamlessly connecting agentic architecture with the brand’s existing data environments and daily workflows, we provided over 1,800 users with definitive, reference-backed answers instantly. This integrated enabler cleared manual bottlenecks and reduced the message cycles previously needed to approve time-sensitive assets.

On the engagement side, a focus at re:Invent was the transformation of live media and broadcast workflows. The challenge in modern media isn't just storage, but the inability to identify and extract moments of value within a live stream in real-time. Our demo at the event illustrated this industry shift through the lens of a “sneakerhead” basketball fan. By using agentic workflows to scan live footage for visual cues and automatically triggering rendering pipelines, we demonstrated how live video can evolve from a passive broadcast into a searchable, personalized experience. Such innovations show how the media supply chain is becoming a dynamic revenue engine by connecting fan interests to personalized content at scale—provided you have the integrated architecture needed to bridge the gap between cloud infrastructure and the complex, real-time demands of a live broadcast. 

The move to micro-models allows for specialized, cost-effective intelligence.

Finally, re:Invent 2025 addressed the cost barrier that has kept many customers from building bespoke AI solutions. The prevailing trend isn't just about bigger models anymore; it is about specialization.

While the “teacher-student” architecture—using massive, high-intelligence models to train and evaluate smaller micro-models—has been a known engineering strategy for some time, AWS is now making it accessible for every enterprise. Announcements like Amazon Nova 2 and Nova Forge are designed to democratize this process, lowering the barrier for organizations to build their own frontier models.

This enables marketing or technical teams to build proprietary micro-models that are hyper-specialized. You might have one small model specifically trained to write in your brand voice, another dedicated to checking legal compliance, and a third for analyzing customer sentiment. This approach reduces latency and cost while dramatically improving accuracy, as each model is an expert in its narrow lane.

Adapt to become the architect of the future.

The experimental phase of generative AI is evolving into an era of industrial-grade execution, moving past the novelty of chat interfaces and into a reality where success depends on the sophistication of your infrastructure. The ones who win in this new landscape won't just be those with the best creative ideas, but those with the most robust agentic plumbing: structured data, specialized micro-models, and autonomous workflows that run 24/7.

For customers, the mandate is to look beyond the immediate output of AI and focus on the architecture behind it. By investing in structured knowledge graphs and embracing the shift from human-in-the-loop to human-on-the-loop orchestration, organizations can unlock a level of personalization and efficiency that was previously impossible. The infrastructure is built; the agents are ready. The question is no longer what AI can do for you, but what you are prepared to let it build.

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.

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.

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.

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.

Discounting hurts your brand long-term.

As tempting as sales spikes are, aggressive discounting has hidden costs: 

• It erodes margins. Short-term revenue gains are offset by lower profit margins. 
• It trains consumers to wait. Shoppers begin expecting frequent discounts, hurting your ability to sell at full price.
• It damages brand value. Consumers associate your products with price cuts instead of quality, undermining your brand’s premium positioning. 

Once shoppers become accustomed to discounts, shifting their mindset becomes increasingly difficult. Recent industry insights highlight that frequent discounts can significantly reduce consumers’ willingness to pay full price, leaving brands vulnerable to sustained margin pressures. Amazon thrives on a cycle of continuous promotions, encouraging consumers to hold out for lower prices and brands to participate in sales events to stay visible. But this turns pricing into a race to the bottom. When a brand tells me, “Sales are up, but our margins are suffering,” one of the things I ask is: Are you controlling your discounting strategy, or is Amazon controlling it for you? 

Here are smarter ways to win Amazon sales events.

To protect profitability while still benefiting from Amazon’s major sales events, brands need strategic alternatives to pure discounts: 

1. Bundle for value. Instead of slashing prices, create bundles to enhance perceived value. This naturally increases your Average Order Value (AOV) and maintains healthier margins.

Example: A skincare brand combined popular full-priced products with trial sizes in bundles for Prime Day, preserving margins and driving higher sales volumes.

2. Leverage Subscribe & Save and repeat purchases. Use sales events as a springboard for customer retention. Promoting Subscribe & Save discounts can drive long-term customer loyalty rather than one-time transactions. 

Example: A pet food brand promoted a 10% Subscribe & Save offer during Prime Day, securing ongoing monthly purchases and consistent long-term revenue.

3. Emphasize brand building and premium positioning. Sometimes the most effective strategy is not discounting at all. Instead, invest in branding, storytelling, enhanced product pages and high-quality creative content to justify premium pricing.

Example: A luxury kitchenware brand eliminated discounts entirely, focusing instead on premium branding, optimized listings and strategic ad placements to maintain full-price sales throughout Amazon’s sales events. 

4. But if you must, be selective with discounts. Not every product needs a price cut. Discount strategically, focusing on slow-moving inventory, new product launches or lower-priced items that attract new shoppers without sacrificing premium product margins. 

Example: A home goods brand offered discounts on select introductory items during Prime Day to attract new shoppers while preserving full prices on premium core products. 

Take control of your Amazon strategy.

Amazon’s promotional strategy doesn't have to dictate yours. If you control how and when you discount, you can win sales events without losing pricing power. But if you let Amazon dictate your pricing cycle, you risk becoming just another brand in the discount bin. At Monks, we prioritize your long-term success, guiding strategic decisions rather than short-term sales spikes. Our expert strategists are here to help your brand achieve sustainable, profitable growth on Amazon. Reach out today to start winning smarter.