Brands winning with AI start by fixing processes, not buying tools
The most successful marketers deploying AI are asking one question before building anything: what process needs to change first?
Most brands are doing the opposite. They plug agents into existing workflows, map tools onto current organization charts, and wonder why the gains barely register. What they end up with, as one executive put it, are faster versions of the wrong thing.
The difference matters. Brands facing acute competitive pressure - automotive companies rattled by Chinese EVs, financial services firms fighting fintech - don't have the luxury of moving slowly. They start with process. Everyone else keeps the organization chart intact and slots agents around it.
The foundation comes first.
Vinny Rinaldi spent a year building AI infrastructure at Hershey's. His work wasn't glamorous: cleaning data, fixing taxonomy, building the infrastructure that lets a system answer a prompt with a relevant output in 10 minutes.
"You've got the ability to plug AI on top of anything right now, but if you don't plug it into something that's structurally built, your outputs are not going to be relevant whatsoever," Rinaldi said. His analogy: "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."
This pattern repeated at Digiday's Programmatic Marketing Summit. Emily Proctor, managing director of data and technology solutions at OMD, said it plainly: "We really start with building a solid foundation in the workflow before we even get into the fun, innovative, transformative things."
When clients ask for AI, what they often really want is a more efficient workflow. More ambitious applications follow if the groundwork exists. Skip it and problems compound. "You're plugging agents into a very siloed workflow," Proctor said. "Agents and tools that are not talking to each other, no orchestration. That's where it really starts to break down."
The guardrails have to come before the automation.
Wes ter Haar, chief AI officer at S4 Capital's Monks, watched CMOs recently struggle with board pressure to cut costs and adopt AI - but without clarity on what comes next. They hadn't asked the question that matters: not how do we use this, but what needs to change about the way we work before we do.
General Motors illustrates why sequencing matters. Ter Haar's team started with the obvious problem: content costs too much and takes too long. That work generated real results. But it surfaced a harder question that only became visible after solving the first one.
Once the supply chain bottleneck disappeared, the question shifted. Not how fast can we make content, but what should we actually make with that capacity? "Just doing more doesn't necessarily help the business," ter Haar said. His team is now working through persona agents and consumer versioning tools that inform what gets made, not just how fast it gets made.
Market Singer, who leads marketing transformation work at Deloitte Digital, keeps making the same argument to clients: "It's not what agent do I need to build. It's what process do I need to change. And then go build an agent."
In at least three instances, that argument is working. A large U.S. retail advertiser is shutting down most external agency relationships after building sufficient in-house AI capability. A second is restructuring its entire global marketing function from scratch, applying supply chain thinking to how creative, media and data interact. A third is consolidating a fragmented portfolio of agencies and freelancers into a coherent model.
"The common theme amongst all of them," Singer said, "is workforce transformation first."
The brands that earn their agents actually use them.
The ones that did the process work - data cleaning, taxonomy fixing, organizational restructuring - are finding that agents work when they arrive. The ones that skipped ahead are learning why that matters.
Rob Wrubel, founder of Silverside, an innovation lab behind several high-profile AI campaigns, sees a structural argument against the traditional agency model itself. Large agencies were built for a world where scale required headcount. "You spend four weeks just scheduling meetings," Wrubel said. "You've got everybody in a Slack channel with 50 people, not sure who has creative decision rights."
AI removes that friction. A three or four person agile team can now do what hundreds could before.
What brands are starting to demand is a trained AI system built around brand assets, visual identity, product knowledge and compliance rules that can produce content across channels without starting from scratch each time. Wrubel calls it a "brand brain."
Panasonic launched a Porsche-designed washing machine across China entirely in Chinese in three and a half weeks from brief to live. Svedka created two broadcast spots in three weeks, then powered point-of-sale, social and TikTok integrations from the same trained system when the Super Bowl arrived. One system. Multiple channels. No re-briefing.
The business model is shifting.
Time-based fees - the bread and butter of agency economics for decades - are giving way to what Wrubel calls a "tech-powered service model." Brands come in for a proof of concept, get comfortable with results, then ask if Silverside can run their social permanently because it's 60% cheaper and 90% faster.
From there, the brand brain becomes infrastructure. The question shifts from what do we want to make to how do we want to operate.
"It's a new hybrid breed of company," Wrubel said, "combining the best of craft and technology and brand expertise, but with systems that are automated."
His argument against pure software-as-a-service: the technology moves too fast for brands to navigate alone. "People don't just need technology that no one helps you with. You need the expertise and the services as everything changes so quickly."
The model that wins bundles both - and owns the client relationship across the entire journey from concept to scaled production infrastructure.
Transformation accelerates in downturns.
Every major technology shift of the last 25 years - SaaS, cloud, paid search - emerged from economic pressure, not optimism. Wrubel expects the same pattern with AI. Most brands don't have to transform yet. Things are working well enough.
But when growth slows, adoption accelerates. The brands quietly building foundations now will have a significant head start on the ones still running proof of concepts when that moment arrives.
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