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 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.

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.

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.

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

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

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

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

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

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