Raul Delapena Setiawan
The marketing industry is currently gripped by an AI fever. From the boardroom to the creative studio, the mandate is clear: deploy AI agents, automate workflows, and slash overhead. Yet, beneath the surface of this technological gold rush, a sobering reality is beginning to emerge. Senior marketers are discovering that despite the hype, the anticipated transformation is yielding only marginal gains.
The core issue? Brands are attempting to force-fit sophisticated AI agents into broken, antiquated processes. In their rush to innovate, they are simply building faster versions of the wrong things. As the industry grapples with this disconnect, a new consensus is forming among leaders: the most critical AI lesson is not how to deploy an agent, but how to re-engineer the process that precedes it.
The Foundation Problem: Process Before Product
The current state of AI adoption in marketing is characterized by a "bolt-on" mentality. Companies map AI tools onto existing, often siloed, organizational structures and then express frustration when the results fail to deliver exponential returns.
Vinny Rinaldi, who has spent the past year spearheading AI infrastructure at Hershey’s, offers a stark analogy for this misstep. "When you build a home, it can be the most beautiful thing on the outside, but if you forget to pour the concrete foundation, the first storm is going to blow it over," Rinaldi explains. For Hershey’s, the foundation wasn’t the AI model itself; it was a grueling, year-long effort dedicated to cleaning data, fixing taxonomy, and establishing the infrastructure necessary to make AI prompts actionable.
"It’s the most unsexy part of the job," Rinaldi admits, "and I think it’s the first thing people overlook."
This sentiment was a recurring theme at Digiday’s recent Programmatic Marketing Summit. Emily Proctor, managing director of data and technology solutions at OMD, emphasized that clients often confuse a desire for efficiency with a desire for AI. "We really start with building a solid foundation in the workflow before we even get into the fun, innovative, transformative things," she says. "You’re plugging agents into a very siloed workflow—agents and tools that are not talking to each other, no orchestration. That’s where it really starts to break down."
Chronology of the Shift: From Cost-Cutting to Strategic Infrastructure
The trajectory of AI adoption has moved through three distinct phases over the last 24 months, and the brands that are succeeding are those that have moved beyond the initial "efficiency" phase.
- The "Efficiency" Phase (Early Adoption): Driven by board-level pressure to reduce costs, brands began by applying AI to the most obvious bottlenecks: content production and copywriting. Companies like General Motors focused on the immediate win—lowering the cost of creative production.
- The "Structural Realization" Phase: As firms successfully automated production, they hit a new hurdle: the "what." Once the supply chain bottleneck was removed, the question of what to actually make—and whether it was aligned with brand goals—became more urgent.
- The "Workflow Transformation" Phase (Current): The leading edge of the market is now moving toward total organizational restructuring. This involves moving away from external agency dependencies and toward in-house, AI-native ecosystems where creative, media, and data are integrated at the source.
Supporting Data and Industry Observations
The shift toward a "process-first" mentality is backed by significant industry movement. Market Singer, who leads marketing transformation at Deloitte Digital, reports that the most successful firms are now prioritizing workforce transformation over tool selection.
In three notable cases, Deloitte has guided clients through fundamental pivots:
- Retail Transformation: A large U.S. retailer is phasing out external agency dependencies in favor of in-house AI capabilities.
- Global Restructuring: A global brand is completely rebuilding its marketing function, applying a supply chain mentality to the interaction between creative and data.
- Tech Consolidation: A large tech company is streamlining its fragmented agency portfolio into a single, AI-enabled model.
The Numbers That Define the Market
- 78%: The percentage of millennials expected to use a second screen during World Cup matches, highlighting the fragmented nature of modern consumer attention that AI must manage.
- 1.8x: The increase in affiliate link CTR from YouTube creator integrations by the eighth partnership, proving that consistency—rather than one-off "innovative" campaigns—is where the real value lies.
- $158 Billion: The forecasted value of the European creator economy by 2032, a space where AI-driven content scaling is becoming the primary barrier to entry.
The Rise of the "Brand Brain"
Perhaps the most radical shift in the marketing landscape is the transition from "agency-as-vendor" to "tech-powered service model." Rob Wrubel, founder of the innovation lab Silverside, argues that the traditional agency architecture is inherently incompatible with the speed of AI.
"You spend four weeks just scheduling meetings," Wrubel says. "You’ve got everybody in a Slack channel with 50 people, not sure who has creative decision rights."
Silverside is championing the concept of the "Brand Brain"—a centralized AI system trained on a specific brand’s visual identity, product knowledge, and compliance rules. By embedding the insights of a creative director directly into a system, the need for constant re-briefing is eliminated. The result is a system capable of launching multi-channel campaigns in weeks, not months.
"Once you learn and work with a creative director or brand strategist and embed their insights… you really are building that brand brain that can then do a lot of helpful work," Wrubel notes.
Implications: A New Era for CMOs
The implications for the C-suite are profound. The role of the CMO is shifting from managing talent to managing "orchestration."
The End of the Time-Based Fee
The traditional agency billing model—charging for hours—is under direct threat. As AI enables a three-person team to do the work that previously required fifty, the value proposition of the agency must shift. We are moving toward a "tech-powered service" model, where agencies are paid for the efficacy of their systems rather than the duration of their labor.
The Recession Catalyst
History suggests that true technological transformation is rarely born of optimism; it is born of pressure. Just as the 2008 recession accelerated the shift toward cloud computing and paid search, the current economic climate is forcing brands to treat AI as a survival strategy rather than an experimental luxury.
As Wes ter Haar, chief AI officer at S4 Capital’s Monks, observed during meetings with CMOs, the demand for cost-cutting is currently outpacing the clarity of how to achieve it. The brands that are quietly investing in their data foundations today will be the ones that capture market share when the broader industry is still struggling to integrate its first wave of agents.
The Human Element
Despite the march of automation, the consensus remains that humans are not being replaced; their focus is being elevated. As Tyler Romasco, EVP of commercial at OpenX, noted, "I buy relationships, people, and strategy. They still want teams of people who understand their business as a marketer."
Conclusion: The Path Forward
The "AI era" of marketing is not defined by which LLM a company chooses or which agentic workflow they test. It is defined by the willingness of leadership to do the unglamorous work of data hygiene and organizational redesign.
The companies that win will be those that realize the "agent" is not the solution—it is the final step in a long process of modernization. For the CMO, the message is clear: if you are not prepared to change the way your team works, no amount of automation will save your output from irrelevance. The foundation must be poured before the house can be built.
Rifan Muazin
Ammar Sabilarrohman
