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 This stagnation happens because businesses attempt to force exponential technology into linear, outdated workflows.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.

At its core, Smaller is Better dismantles the long-held belief that bigger always means better in the enterprise, presenting a compelling case for why smaller teams are, in fact, the secret weapon for achieving agility, innovation and competitive advantage in today's fast-paced market. Through a blend of rigorous research, real-world examples, and Brim-DeForest's own experiences leading our Technology Services practice, the book offers a transformative approach for organizations of all sizes to become more efficient, adaptable, and ultimately, more successful.

Whether you're a startup founder, a leader in a large organization, or somewhere in between, Smaller is Better provides a practical guide to reimagining how work is organized and executed. It invites readers to rethink leadership, collaboration, and performance, making a compelling case for the small team model as not just a strategy for success, but as a necessity for survival in the modern business landscape.

We sat down for an interview with Brady—here is what he had to say:

Your book, Smaller is Better, challenges the traditional model of large, siloed teams within enterprises. What are some of the specific drawbacks you've observed in these environments?

The drawbacks are multifaceted and deeply impact an organization's ability to innovate and respond to competitive pressures. Such teams, structured within a traditional large-organization culture, often operate in an environment that extinguishes risk and discourages failure. This risk-averse culture significantly stifles innovation, as it prevents team members from taking meaningful risks and exploring new solutions to complex problems.

Innovation inherently involves a degree of risk and failure; it requires doing things that are not fully understood or mapped out. When failure becomes anathema, team members tend to opt for safer, more predictable paths, even if they lead to suboptimal outcomes. This leads to a culture of learned helplessness, where innovation atrophies and the organization becomes less adaptable and more vulnerable to external changes and competition.

Traditional models of decision-making in large enterprises often place the power in the hands of executives who are far removed from the day-to-day operations and front-line information. This disconnection between decision-makers and the operational realities of their business not only slows down the decision-making process but also leads to decisions that may not reflect the best interests of the organization or its customers.

The "teams model" outlined in Smaller is Better challenges this status quo by inverting the decision-making hierarchy, thus empowering individual contributors and smaller teams to take meaningful risks within their specific missions. This model fosters an environment where small-scale failures are not only allowed but celebrated as learning opportunities, leading to faster innovation, improved productivity, and ultimately, greater organizational agility.

Our industry is evolving rapidly, with new trends and technologies emerging constantly. How do you see the "small teams" approach fitting into this fast-changing landscape?

The "small teams" approach is increasingly relevant and effective in the context of rapidly evolving market dynamics and technological advancements. Small team structures, contrary to being unrealistic for large companies, are ideally suited for enterprises of any size. They scale beautifully, whether the organization is managing one team or a thousand, as long as each team is aligned with an appropriately sized mission. This flexibility and scalability ensure that the basic structure and culture supporting small teams can lead to replicable success across the entire organization.

Moreover, the "teams" framework is highly adaptable, working just as well for remote and distributed teams as for those that are co-located. This adaptability proves that autonomy is a critical structure for teams, facilitating better performance irrespective of their physical work environment. Additionally, in the era of AI and other technological innovations, the move towards smaller, autonomous team structures becomes even more necessary. As teams become smaller, the use of AI tools allows them to make an outsized impact, driving organizations towards more nimble, innovative and efficient operations. The AI revolution, in essence, necessitates and amplifies the effectiveness of the small teams model, making it an indispensable approach for navigating the complexities of today's business landscape.

Let's talk about building and optimizing small, autonomous teams. How do you define and align teams around clear missions within a larger organizational context?

It involves a meticulous process that integrates the team's purpose with the organization's broader objectives. It starts with establishing a clear and compelling mission for each team, which is crucial for ensuring that the team's efforts are not only aligned with the organization's goals but also imbued with a sense of purpose and direction. This mission must be well-defined, measurable, and achievable, serving as a guiding star for the team's activities.

To ensure effective collaboration among these teams, it is vital to promote a culture of transparency and communication. This involves regular check-ins, where teams share their progress, challenges, and learnings with one another, facilitating a supportive environment where teams can learn from each other's experiences and coordinate their efforts more effectively. Additionally, leveraging collaborative tools and platforms can enhance this inter-team communication, allowing for seamless sharing of ideas and resources.

Moreover, aligning teams around clear missions within a larger organizational context requires a robust framework that supports autonomy while ensuring coherence with the organization's strategic direction. This may involve setting up cross-functional liaisons or integrating shared goals that encourage collaboration towards common objectives. By fostering an environment that values autonomy, mastery, and purpose, organizations can optimize the performance of small, autonomous teams, ensuring that they not only work effectively within their own scope but also contribute to the overarching success of the organization.

How does AI factor into the "smaller is better" approach? How can empowered, small teams best leverage AI tools to further enhance their performance?

AI plays a crucial role in enhancing the "smaller is better" approach by enabling small, autonomous teams to make an outsized impact. In a landscape where headcount is shrinking for many organizations, the integration of AI tools within team structures allows individual contributors to amplify their capabilities, automate routine tasks, and focus more on strategic, creative and problem-solving activities. This shift not only increases efficiency and productivity but also fosters an environment of innovation where teams can quickly adapt and respond to new challenges.

AI tools can help small teams analyze vast amounts of data, identify trends, and make informed decisions much faster than traditional methods, which is particularly beneficial in fast-paced industries. This ability to leverage AI effectively allows teams to maintain their agility and creativity, ensuring they can continue to deliver impactful results despite their small size. The "teams" framework, therefore, not only supports but thrives on the incorporation of AI, making it more relevant and necessary in the context of modern organizational challenges.

Can you outline a strategy for how to maintain agility as these small, autonomous teams begin to grow, scale and replicate across an organization?

A deliberate and phased approach is essential. Here is how I recommend an organization ensures sustainable growth while preserving the agility of small teams:

1. Start with success: Begin by conducting a carefully designed sandbox experiment. This initial success serves as a proof of concept for the small teams model within your organization.

2. Enlist support: After demonstrating success, gather support from stakeholders and secure additional resources. This backing is crucial for scaling the approach across the organization.

3. Limit scope: As you add new teams, carefully limit the scope of each addition. This ensures that the growth of teams remains manageable and focused on specific missions.

4. Incubate slowly: New teams should be incubated slowly and meticulously, allowing them to develop the capability to operate independently. Only after they are fully functional should more teams be introduced to the system.

It's important to move at a pace that allows teams to deeply understand their roles and objectives, learning through experience. By starting small, validating the model and expanding carefully based on success, organizations can scale their small, autonomous teams effectively, ensuring that agility and innovation remain at the heart of their growth strategy.

Your experience spans both startups and large corporations. How can leaders within established organizations, often with entrenched cultures, begin to implement the "start small" approach, especially if they lack C-suite support?

Leaders in such a situation can adopt a "start small" approach by focusing on actions that require minimal initial consensus-building and bypassing traditional gatekeeping wherever possible. An effective strategy involves empowering small teams to operate with autonomy, allowing them to directly interact with and sell to customers without necessarily seeking permission from sales or marketing departments traditionally seen as gatekeepers. This approach emphasizes the importance of agility, speed and the ability to learn from mistakes, which are critical for fostering innovation within constrained environments​​.

For leaders who find themselves in a situation where bypassing the traditional consensus is not feasible and stakeholder buy-in is necessary, the key is to frame the small teams model in terms of potential outcomes that align with the organization's broader goals. This could involve highlighting how the model will enhance quality, increase velocity or improve capabilities without committing to a specific measurable end goal within a fixed timeline. Instead, focus on selling the concept of a measurable improvement that the transformation is expected to bring about, thus aligning with the organization's overall objectives and demonstrating the potential value of the approach​​.

How are you applying the principles of "Smaller is Better" in your current role with Formula.Monks?

In my role as CEO of Formula.Monks, the Technology Solutions practice at Media.Monks, I apply these principles by tackling complex challenges with small, empowered teams. A striking example of this approach in action was when our organization was brought in to assist a two-hundred-person company that had been struggling for years to refactor their software for municipal agencies. Despite a product delivery team of over sixty people, they had made no progress and were rapidly approaching a failure state​​.

We discovered that the solution to their problem lay within the talents of just two engineers who were capable of using modern tools and moving the software to the cloud. The bureaucracy of the larger team structure was stifling these engineers' abilities to effect change. This situation underscored how larger groups can inadvertently limit innovation by adhering to the lowest common denominator, rather than leveraging the exceptional talents within​​.

By focusing on smaller, autonomous teams, we allow for greater agility, innovation and responsiveness to the unique challenges faced by our clients. This approach not only streamlines problem-solving but also harnesses the full potential of each team member, leading to more successful outcomes and transforming the way ambitious companies operate. This strategy demonstrates the profound impact of "Smaller is Better" principles, highlighting the importance of flexibility, focus and leveraging individual strengths in achieving organizational goals.