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"Do more with less" is being misread as a mandate to use AI to shrink. The CMOs winning right now are reading it as a mandate to use AI to expand.
Every CMO operating today knows the mandate. "Do more with less" has been a fixture of the annual planning conversation for years. But AI has given the phrase new urgency. Boards want to see cost savings. CEOs want headcount reduction. Shareholders want proof that AI investments translate to margin.
The pressure is real, and the efficiency case for AI is not wrong. The tools can absolutely help marketing organizations run leaner. Campaigns that used to take 14 weeks can launch in days. Teams that previously needed to choose which products got hero creative can now cover the full catalog. The efficiency wins are documented and significant.
But here is what gets lost in the efficiency framing: it accepts the current definition of what marketing can be.
Efficiency asks: how do we do the same things faster and cheaper? Capability asks: what becomes possible that wasn't possible before?
The CMOs who are building durable advantages right now are not primarily asking how AI makes their current marketing cheaper. They are asking how AI changes the ceiling on what their marketing organization can achieve. That is a fundamentally different question, and it leads to fundamentally different investments.
It is not hard to see how we got here. AI adoption in enterprise marketing happened during a period of intense cost scrutiny. Marketing budgets were already under pressure heading into 2024. The promise of doing the same work with fewer people, faster, at lower cost was an easy story to tell a CFO.
That story also tends to win in the short term. Efficiency gains are measurable quickly. A 40% reduction in creative production cost shows up in the next quarter. A 6x improvement in time-to-market is a metric that fits cleanly into a board presentation. The value is legible.
Capability expansion is harder to quantify upfront, and harder to sell internally. What is the ROI of doing something you have never done before? How do you budget for a new category of marketing motion that did not exist last year? These questions require a CMO who is willing to make a bet on possibility rather than prove an incremental case.
That is exactly the bet the best ones are making.
The clearest way to understand capability expansion is to look at what leading consumer brands are actually doing with AI in their marketing organizations right now. Not the announcements and the pilot programs, but the things that represent genuine new ground.
Nike has built one of the most sophisticated AI-driven marketing stacks in consumer retail. Their VP of Marketing Data, Linda Cereda, put it plainly at a recent industry event: Nike is building toward tools "that can make decisions, not just predictions, and lead to true 1:1 personalization." She specifically called out the goal of moving "beyond delivering short-term efficiency gains" toward creating "sustainable competitive advantage."
One of the more striking capabilities Nike is developing is the use of synthetic consumer personas to test go-to-market strategies without waiting months for real-world consumer feedback. This is not an efficiency play. It is a capability that did not exist before, enabling Nike's marketing teams to iterate on positioning and creative strategy at a pace that was structurally impossible in a world of traditional consumer research.
Nike's SNKRS app, driven by AI, is another example. It does not just sell sneakers more efficiently. It creates an entirely new kind of relationship between a brand and its most engaged customers, one built on personalized drops, community connections, and experiences that turn a transaction into a lifestyle touchpoint.
Fragrance is the one beauty category where digital has always had a hard ceiling. You cannot transmit scent through a screen. In December 2025, The Estée Lauder Companies and Jo Malone London launched the AI-powered Scent Advisor, a conversational tool on JoMalone.com that interprets natural language prompts ("a crisp and refreshing winter scent" or "a birthday surprise for a friend") and maps them against the brand's proprietary olfactory data to generate bespoke fragrance recommendations. It was built on Google's Gemini, and it works the way a skilled in-store consultant works: through conversation, context, and deep product knowledge.
Before this, online fragrance shopping relied on static dropdown quizzes with multiple-choice inputs. The experience was a poor proxy for the discovery that happens in a Jo Malone London boutique, where a consultant learns what a customer is drawn to through dialogue and builds a recommendation from genuine expertise. AI made it possible to replicate that conversation at scale, online, across every customer who visits the site. Jo Dancey, Global Brand President, described it plainly: the tool opens "a new era of digital scent discovery where AI can bridge the gap between curiosity and confidence."
Fragrance has historically been underdeveloped in digital channels precisely because the discovery experience was so hard to translate. Removing that constraint doesn't just improve conversion on existing traffic. It opens a category to a wider audience of first-time buyers who found in-store consultations intimidating and online shopping too generic to be useful.
In early 2026, Wyndham Hotels connected its hotel inventory directly to Google's AI Mode search product, to Anthropic's Claude, and to ChatGPT, building what CEO Geoff Ballotti called "AI-native distribution". Travelers can now discover and book Wyndham properties through a natural-language conversation without visiting a traditional search interface, a booking behavior that simply did not exist eighteen months ago.
The investment to get there was less than 00,000. No OTA commission structures. No dependence on a third party controlling the discovery experience. Ballotti described it as "an early glimpse of how AI-native distribution will reshape the way guests find and book our hotels," and pointed to a 200 basis-point increase in direct bookings driven by AI voice agents as an early signal of what the channel can do.
This is not a faster version of how hotel booking used to work. It is a structurally different model. Wyndham's marketing organization is now capable of owning the guest relationship at the moment of discovery in a way that no hospitality brand could before AI-native search existed. That is the definition of capability expansion.
Each of these examples shares something important: the CMO who championed the investment had to make a case for possibility, not just efficiency. That requires a different kind of leadership conversation with the rest of the executive team.
Here is what makes this moment unusual. The efficiency narrative around AI is so dominant that any CMO who surfaces a genuine capability argument stands out. The board has heard dozens of pitches about cost reduction and headcount leverage. It has heard far fewer about what the marketing organization can do in 2025 that was genuinely not possible in 2023.
"Do more with less" is being misread as a mandate to shrink. The CMOs winning right now are reading it as a mandate to expand.
The strongest version of this argument is not about AI tools at all. It is about what kind of marketing organization a brand wants to have in three years. The efficiency-first path produces a leaner version of today's marketing team. The capability-first path produces an organization that can do things no competitor has done yet.
One of these paths compounds. The other does not.
Before the next annual planning conversation focuses entirely on productivity gains and cost savings, it is worth pausing on a different question: which capabilities does this marketing organization not yet have that it should?
True 1:1 personalization at the scale of your customer base. Creative coverage across every product and every platform, not just the top performers. The ability to move at the speed of culture instead of the speed of your production calendar. The capacity to test ten ideas in the time it used to take to launch one.
These are not efficiency improvements. They are capability additions. And the brands building them now are building the moat that will define the next decade of consumer marketing.
The "do more" in "do more with less" has always been the more interesting half. Most marketing organizations, under pressure to prove AI ROI quickly, have been spending all their time on the "with less" half. The CMOs who figure out how to pursue both simultaneously are the ones worth watching.
Adora is an AI performance marketing engine purpose-built for the world’s best consumer brands. Clients like Brooks Running, goop, and Servco Pacific have used Adora to expand campaign reach, accelerate time-to-market, and improve ROAS, not by replacing their marketing teams, but by giving them capabilities they didn't have before.
