Historically, the playbook for the game has relied on the big idea. It’s an arms race of celebrity cameos and mascot-driven stunts designed to manufacture buzz through sheer scale. But a recent Forbes piece—which recaps a CES panel featuring S4 Capital’s Executive Chairman, Sir Martin Sorrell, about how consumer engagement is evolving—notes that 30-second spots during the game have crossed the $10 million mark for the first time. That hefty price tag is pushing the industry to a tipping point, where the traditional stunt is being replaced by the need for a sustained, technical and emotional ecosystem. 

Reclaim the lead-up as a cinematic world.

Traditional sports marketing treats the weeks preceding the Sunday showdown as a series of breadcrumbs leading to a single, 30-second reveal. This approach views the lead-up as secondary, a supporting setup for the main event. But for a brand to truly resonate, the pre-game phase should be elevated to equal footing with the game itself, functioning as an emotive, standalone cinematic journey.

When a brand shifts its focus from the scoreboard to the raw human stories surrounding a national or even global event, it moves beyond the limitations of standard broadcast conventions. Strategy centers on a multi-layered narrative rollout: a cornerstone long-form feature supported by episodic vignettes that document the cultural moment in real-time. This structure allows the brand to pivot from observer to active participant, sustaining engagement through a consistent release schedule. Whether through intimate character studies, process-driven narratives that explore the local logistics behind the spectacle, or archival journeys that lean into the mythos of a team’s legacy, these layers build a world that fans actually want to inhabit.

This strategy changes the ROI of a major event sponsorship. Instead of a one-off stunt that captures a moment, it builds sustained momentum. It allows for brand integration that goes deeper than a logo on a screen, embedding the brand within the authentic gestures, joys and stories that define the spirit of the sport. Rather than simply watching a commercial, the audience is experiencing the finale of a story they’ve been living with for weeks.

Transition from traditional broadcast to intelligent spectatorship.

While the final game is a single event, the ecosystem surrounding it—from betting markets and social sentiment to real-time player telemetry—is exceptionally dense. For modern broadcasters and their brand partners, the objective has shifted from simple video delivery to the seamless ingestion and synchronization of massive data sets and branded experiences that enhance the viewing experience in real-time.

Our experience in supporting global broadcasting platforms has shown that the true differentiator lies in technical infrastructure capable of handling extreme data density. When a platform can ingest diverse leaderboards, qualifiers and live statistics across dozens of concurrent threads, it transforms the screen from a flat image into an interactive dashboard. For an event like the Sunday game, this could mean moving beyond simple graphic overlays toward intelligent content delivery, where the broadcast itself reacts to the flow of the game and the pulse of the audience.

This level of agility is driven by edge-computing solutions like LiveVision™, which analyze multi-camera feeds in-flight. By utilizing real-time intelligence to suggest optimal shots, prioritize key content and dynamically optimize delivery, broadcasters can reduce the friction between the action on the field and the second-screen experience. This technical superiority allows brands to move faster, creating context-aware moments that resonate with fans as they happen. In this model, technology goes beyond merely supporting the broadcast, indexing the culture of the game in real-time and turning every play into a data-rich opportunity for engagement.

Cultivate the “long tail” through generative content and fandom.

Traditional campaigns often struggle with a content hangover: a sharp drop in engagement once the game ends. Moving from a one-to-many broadcast model to a one-to-one personalization strategy allows brands to sustain momentum by turning passive viewers into active co-creators. This shift relies on utilizing AI to bridge the gap between a massive, shared event and the individual fan’s specific journey.

While the technical agility of LiveVision™ provides the infrastructure to ingest live data, its real value to the viewer experience lies in industrializing creativity. Once the data is synchronized, it serves as the fuel for content generation. For a brand, this means the ability to instantly transform live action into customized assets. A viewer who is specifically following a certain player’s performance or interested in specific tactical stats, for example, could receive a dynamically generated highlight reel tailored to those preferences in real-time. This transition turns the technical intelligent lens into a personal storytelling tool, creating new inventory for engagement that scales to millions of individual streams.

The opportunity for impact extends far beyond the final whistle into a post-game phase defined by “souvenir” memories, too. Strategy here involves harnessing individual fan data—collected from stadium interactions or digital touchpoints—to feed proprietary AI engines. By processing vast libraries of event footage through these personalized filters, brands can generate hyper-personalized video narratives for every attendee or remote viewer. These unique, AI-orchestrated films therefore serve as a bridge between the shared cultural moment and a personal emotional connection. In this model, the game is no longer the conclusion of a campaign, but the catalyst for a sustained, personalized dialogue that converts immediate buzz into long-term brand value.

Move from moment to momentum.

The Sunday showdown remains the ultimate test of brand relevance, but the metrics of success have fundamentally shifted. Winning this moment now demands an integrated architecture that treats the event as a beginning rather than a finale. Brands can move beyond the constraints of the 30-second spot by weaving a cinematic narrative through the lead-up and anchoring the live experience in data that indexes culture in real-time. When this technical and emotional foundation is paired with generative AI to scale personalization, the broadcast window effectively disappears, replaced by a continuous, individual connection to the game.

The transition from a big idea to a big ecosystem ensures that a massive, shared moment doesn’t evaporate the second the screen goes dark. Instead, it becomes the foundation for a lasting, personal legacy. That emphasis on technical depth keeps the brand integrated into the fan’s journey, allowing the impact of the event to persist and grow well after the stadium has emptied.

The signals surfacing across 2025 have now crystallized into a strategic mandate: the industrialization of intelligence through workflow orchestration, proprietary data flywheels, and the persistent activation of brand DNA. From these signals, we can define the strategic conditions brands will navigate throughout 2026.

Marketing operations are entering the era of orchestration.

In 2025, marketing teams began moving away from isolated AI pilots to instead implement coordinated, agentic systems capable of executing work across multiple steps, continuously and at scale. These orchestrations, which redesign how collaboration is structured within the organization, connect strategy, creation, execution, and measurement within a single, connected system rather than as handoffs between silos.

This shift also presents brands with a clear exit from “pilot purgatory,” the cycle of fragmented, small-scale tests that often lack the structural weight to drive meaningful business change. By moving beyond isolated experiments and into full-scale orchestration, organizations are replacing curiosity-led pilots with a strategic architecture that connects thinking across the marketing lifecycle. This ensures that intelligence isn’t just a bolt-on tool, but a foundational component capable of dismantling legacy silos and driving high-velocity growth.

What this means for 2026: Orchestrated workflows will drive the industrialization of intelligence, serving as the bedrock for always-on marketing operations that unify creative production, commerce and optimization. Marketing teams will increasingly realign their structures, moving beyond the bottleneck of manual execution toward the strategic orchestration of agentic systems. 

Experience became the primary competitive lever.

As marketing operations became more orchestrated in 2025, experience design evolved to generate new data that could enable further personalization and consumer insights, operating as a sort of flywheel. By inviting consumers to collaborate and co-create within a generative framework, brands can capture rich, contextual signals that were previously trapped in black-box media or biased polling. This turns every interaction into a dual-purpose event: providing a meaningful consumer experience while simultaneously filling critical data gaps with owned, actionable information. When experiences are architected this way, the strategic starting point changes, leading with the fundamental question: “What data am I after?”

Under this architecture, participation is no longer just an engagement metric; it functions as a primary data-generation event, feeding high-fidelity, first-party signals directly into a brand’s agentic ecosystem.

AI serves as the connective tissue here, enabling experiences to ingest real-time data and output hyper-personalized assets without the friction of manual production. A primary example of this is our work with the Boomtown music festival, “Boomtown Unboxed,” which transformed attendee engagement into a scalable data engine and hyper-personalized creative. The platform utilized first-party event data captured throughout the festival to dynamically assemble high-fidelity recap footage unique to each individual attendee.

By treating the experience itself as a massive data-capture environment, AI became the unlock to transform attendance into insight, informing creative assembly and deepening emotional resonance. Creative automation allowed the experience to adapt to each participant at a level of granularity that legacy workflows simply cannot match.

What this means for 2026: As content saturation renders traditional engagement episodic, experience design must shift into an always-on system that continuously harvests intelligence to sustain

Authenticity emerged as a strategic asset.

In 2025, authenticity shifted from a philosophical ideal to a critical operational capability. As generative tools lowered the technical barrier to content creation, the market saw a surge in homogenized, generic outputs that lacked the distinct soul of the brands behind them. On the flip side, strategic brands sought to encode their unique visual heritage, tone of voice and proprietary audience insights into their AI systems, enabling creative at scale that is deeply authentic to the brand.

The most durable competitive advantage no longer comes from mastering off-the-shelf tools, but from training foundational models based on the brand's own history. By ingesting proprietary mascots, intellectual property, and creative principles, brands can ensure their AI-assisted work is instantly and recognizably their own. This move, from one-off prompting to a living brand brain, allows for the scaling of expression without the dilution of meaning.

Conversely, the market has seen the consequences of misalignment. When brands rely on generic public models to represent their identity, they risk falling into the uncanny valley of brand representation. You’ve likely seen a handful of high-profile missteps throughout the year, where the use of artificial, generic models felt misaligned with the brand’s core values or the diversity of its audience. Such outputs often feel like an intrusion rather than an extension, eroding the very trust the brand worked for decades to build.

What this means for 2026: As AI becomes embedded across content operations, authenticity will function as a performance driver. Governance and brand-specific foundational models will become essential components of modern marketing systems, ensuring that scale strengthens recognition rather than creating fragmentation. 

Discoverability is being redefined by AI interfaces.

As AI agents become central to everyday planning and retrieval, discoverability is no longer a matter of simple keyword ranking. Over the past year, discoverability has come to depend on branded content’s ability to be reliably retrieved, understood and cited by generative systems as a definitive source of truth.

This has birthed the era of Generative Engine Optimization (GEO). While traditional SEO optimized for visibility on a results page, GEO optimizes for inclusion within an AI-generated synthesis. This shift demands a move away from keyword density toward contextual accuracy, structured metadata, and verifiable credibility. 

Consequently, discoverability has transformed from a tactical marketing challenge into a foundational infrastructure requirement. Brands that invest in structured knowledge bases and machine-readable content ecosystems create the conditions for AI agents to reference them with confidence, reducing the risk of ambiguity or hallucination. Content must now serve two audiences simultaneously: it must remain emotionally resonant for humans while being architecturally legible for machines. Modular formats, authoritative sourcing and multimodal assets are the new table stakes for reducing inference guesswork by AI intermediaries.

What this means for 2026: Search strategy will expand beyond the logic of search result rankings. Success will be defined by citation and trust, as brands architect content ecosystems that serve as the primary nodes of recommendation within agentic interfaces. 

In 2026, intelligence maturation becomes a structural necessity.

The shift from 2025’s experimentation to 2026’s execution represents the final maturation of the AI-native enterprise. Competitive advantage now follows the industrialization of intelligence, moving past task-level gains toward a cohesive agentic architecture that unifies strategic intent, creative craft, and operational execution.

This evolution has transformed what was once a luxury of curiosity into a foundational structural necessity. Performance in this landscape is defined by the depth of system design and the purposeful activation of a brand’s proprietary DNA. By dissolving legacy silos and architecting unified flows, organizations can finally turn the complexity of orchestration into their most enduring source of compounding advantage.

So, you go to your Global Brand Hub, where you find several 100+ PDF documents full of various guidelines. You search for “colour” and find a matrix that says “Use Pantone 299C for print, #00A3E0 for digital.” The agency used #00A4E0. Is that a typo? Or a holdover from another guideline deck, “Digital-First Brand Refresh_v3_FINAL.pptx,” from last quarter?

You Slack a senior director, but they're in back-to-back meetings. You email the brand compliance alias and get an auto-reply: “We will respond within 48 business hours.” But the agency is pinging. The media slot is booked. It’s a simple, 10-second question that has blocked a time-sensitive asset.

This seemingly small frustration is actually a symptom of brand governance chaos—a massive, hidden tax on your speed, budget, and morale. Fixing this chaos requires more than just a clearer guide or a better folder structure; the real solution is to evolve from static repositories to dynamic, intelligent agents capable of delivering a single, correct answer instantly.

Agentic architectures help solve for relevant retrieval.

For the last decade, improving access to information largely meant adding a better search box to static, file-based brand hubs. However, a search box only fetches documents; it still forces the user to do the work of finding the answer within those documents. This is the critical failure point in the 4:00 PM panic scenario described above. A better solution is to move from a static repository to a system powered by orchestrated agents that retrieve data.

Unlike a search box, an agentic solution can perform tasks on behalf of the users. It can understand context, like knowing who you are (for example, a brand manager) and what you're working on (in our example above, a digital display). From there, it can reason, retrieve information across multimodal sources (PDFs, databases, websites), verify accuracy, resolve data conflicts, and compose an answer. 

It doesn't give you ten blue links to sift through; instead, it offers a single, definitive, reference-backed response. If conflicting data appears—for example, Marketing_v1.pdf says #000000 but Poster_v1.pdf says #000011—the agents use context, role and logic to determine the most accurate answer. If a clear resolution isn't possible, it flags the conflict so the user can make an informed decision: “The correct hex code for digital-first applications is #00A3E0. The #00A4E0 value is an outdated code from the Q1 refresh.” 

This shift is a powerful new driver of enterprise efficiency today. In fact, agentic assistants like this move beyond being passive tools for answering questions, evolving into Brand Intelligence Systems that retrieve accurate brand data, enforce multi-modal compliance, and generate on-brand, multi-modal content at scale.

Proving a measurable lift in efficiency for over 1,800 users. 

We recently partnered with a global technology leader facing this exact challenge. With thousands of employees and partners across the globe, they needed to provide instant, reliable and source-attributed answers to brand questions, at scale, as their MVP. We designed and deployed a bespoke, enterprise-grade AI assistant powered by orchestrated agentic workflows using a tailored Retrieval-Augmented Generation (RAG) architecture.

This orchestration ensures two things. First, the agents don’t hallucinate. Second, the system understands context (who you are, your role and your task) to deliver the right answer, often by combining multiple verified sources. 

The impact of our solution was immediate. Within four months of its rollout, over 1,800 unique users were interacting with the assistant per month, with engagement trending positively. More importantly, we proved we were solving the slow bleed of inefficiency. User sessions became measurably more efficient, dropping from an average of 1.64 messages to just 1.41, because they were getting the right answer, faster, on the first try.

Crafting solutions that integrate into real workflows. 

There are plenty of off-the-shelf and decent tools already available for building a chatbot. The real challenge lies in building bespoke systems that integrate seamlessly into daily workflows, also known as complex enterprise integrations, that are also secure, reliable, highly accurate and personalized.

This is essential not just for performance, but also for compliance, data protection and user adoption. When the experience feels like a natural extension of how teams already work, that’s when transformation sticks.

However, connecting every piece of the puzzle requires a holistic approach. The development of our solution for this specific client was rooted in a single, unified team that covered everything from initial strategy and user pain point understanding to UX and design. This was made possible by engineering teams who orchestrated models to deliver secure answers at scale, all while our QA and delivery teams ensured everyone remained focused on achieving enterprise-grade outcomes.

In practice, this meant that our strategy team mapped pain points, the AI Core team built datasets and evaluation frameworks, the UX team distilled complexity into intuitive experiences, and engineering ensured scalability, resilience and security.

The hardest part? Balancing accuracy, latency and cost across deep enterprise-grade system integrations. It took many iterations over the past three years to achieve team maturity. Key project members had already worked on five similar conversational AI deployments across industries, and that collective experience was crucial. The learning curve has been steep but transformative.

A unified system built by a holistic team frees creativity.

The 4:00 PM panic is just the surface symptom of deeper inefficiencies that off-the-shelf tools can’t fix when accuracy, latency and cost all matter. 

True success comes from integrating bespoke AI systems seamlessly into the creative process—not as add-ons, but as enablers. This is what a holistic approach delivers in practice: a unified system where strategic insights, intuitive design and enterprise-grade engineering work as one. It is this system that ultimately solves the hidden tax of brand friction, giving your most valuable creative people back their time, budget and energy to focus on the work that actually moves your brand forward.

At the heart of it all is the user. We are driven every day by the goal of building next-generation AI interfaces that are not only intuitive and meaningful but also truly smart. For brands and enterprises seeking to achieve this same level of clarity and efficiency, our bespoke agentic AI architecture can be fully tailored. It adapts to your unique workflows and data environments while respecting all governance requirements, empowering your teams with intelligent systems designed precisely around your needs.

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.