How Adobe’s CMO is preparing for AI-driven brand discovery

Discussions about budgets and brand strategy still happen, but they are increasingly overshadowed by a more urgent set of questions. Which marketing metrics still matter when consumers begin their search in ChatGPT rather than on Google? How should companies structure marketing organizations when AI can produce campaigns, analyze performance, and personalize experiences at a scale that once required entire departments? Which skills matter most when the technology changes every few months?

One executive hearing those conversations firsthand is Lara Balazs, Adobe’s chief marketing officer. She spends much of her time speaking with peers who are navigating those questions.

“For years it was always, ‘Spend less with more impact,’” Balazs says. “Now I hear, ‘There’s AI. Do that.’”

The directive is vague and expansive because no established playbook exists. AI is evolving faster than most marketing organizations can adapt, leaving CMOs to build one in real time.

The pivot is already showing up in the metrics that once anchored the function. As product discovery shifts toward AI interfaces, marketing leaders are already seeing declines in traffic and revenue that were once driven by search. Brands that fail to appear in AI-generated recommendations risk being excluded from consideration before a customer ever reaches their website.

Those conversations are expanding marketing’s scope beyond campaigns into enterprise-level decisions, from technology and data infrastructure to workflow design and capital allocation alongside creative and media.

“If you are not talking to your CFO all the time, your CIO, your CTO, any business constituent around that C-suite table, you really are at a disadvantage,” Balazs says.

Marketing’s customer insights now shape decisions about technology, data, and product development, pulling CMOs into closer collaboration with finance, engineering, and IT. Financial fluency, technical literacy, and organizational leadership sit alongside brand building and demand generation as core executive responsibilities.

Rebuilding the marketing org

Early AI efforts centered on experimentation, with teams testing where the technology could deliver measurable value. That phase is giving way to more deliberate decisions about where AI should be embedded in day-to-day work and which initiatives deserve sustained investment.

Balazs encourages marketing organizations to start with a use case tied to a specific business objective. Each initiative should have an executive sponsor, clear accountability, and a team willing to test, learn, and refine before scaling.

Team structures are evolving in parallel. Companies are assembling multidisciplinary groups organized around business objectives rather than traditional functions. Balazs calls them mission teams, while others use terms like swarms or tiger teams.

The structure brings together marketers, engineers, product managers, and data specialists, enabling organizations to move more quickly as AI capabilities advance.

The metrics are changing

Search engine optimization has shaped digital marketing for more than two decades, offering a clear framework for how consumers discover products and services. That framework is beginning to give way as more consumers task AI systems with comparing products, summarizing reviews, explaining features, and recommending purchases.

For marketing leaders, that raises a new question: How often does a brand appear inside AI-generated answers, and how does that visibility influence purchasing decisions?

Adobe began examining that shift after seeing declines in traffic tied to traditional search, long a measurable revenue source. As consumer behavior evolved, the company worked to understand how much of that change was linked to large language models.

That effort led to the development of LLM Optimizer, a tool designed to track and improve the frequency with which Adobe’s products appear in AI-generated responses.

After deploying it, the company saw a 200% increase in brand visibility for its products such as Acrobat and Firefly, according to Balazs. Marketers are still assessing how to measure visibility within AI-generated responses, including how often a brand is mentioned or recommended. Still, it offers an early signal of how product discovery is changing.

For marketing leaders, understanding how AI systems surface and recommend products is becoming as important as understanding how consumers search.

From marketer to orchestrator

Taken together, these shifts are reshaping the role of the CMO.

Balazs describes today’s CMO as a chief marketing orchestrator, reflecting the move from overseeing individual functions to coordinating an interconnected system of people, technology, data, and AI.

That evolution is also changing what it takes to lead. The job increasingly requires CMOs to guide technology-driven decisions without coming from engineering backgrounds.

“I am not an engineer,” Balazs says. “Most marketers aren’t.”

The challenge is to understand the technology well enough to ask the right questions, translate technical capabilities into business outcomes, and help the organization act on them.

In the end, Balazs believes the defining advantage will not be technical expertise. 

“Mindset is going to matter,” she says. The marketing organizations that thrive will be the ones filled with people who are eager to learn, comfortable with ambiguity, and energized by change. They are the people, she says, who “embrace the gray.”