Welcome to our recurring blog series, “The Breakdown.” Each post breaks down a complex marketing concept and explores its applications—helping you make smarter decisions that drive meaningful results.
Marketers face a pivotal choice in 2026: to frame their use of AI around driving business growth, or around cutting costs.
While AI can accelerate efficiency and reduce costs by speeding production and scaling content, its real power lies in the effectiveness it can deliver in the form of optimized creative, personalization, and predictive insights. Marketing leaders should guide their teams toward strategies that use AI to strengthen brand equity and long-term enterprise value, rather than focusing solely on its ability to drive efficiency gains and short-term margin improvements.
Data readiness is critical to achieving this goal. Without a strong foundation of unified first-party data and custom AI solutions trained on that data, organizations will struggle to create the differentiated outputs that drive brand and business growth.
How marketing leaders approach and position their use of AI in 2026 will define the role of marketing within their organizations for years to come.
While teams should absolutely use AI for efficiency-related gains like faster campaign execution and lower production costs, emphasizing its impact on efficiency alone can inadvertently play into the notion that marketing is merely a cost center that should be trimmed to the bone, rather than a strategic asset that can fuel effectiveness and multiply revenues. This, in turn, can reduce departmental resources and weaken marketing’s role in the larger organization.
Instead, marketers should embrace AI’s ability to drive sustainable growth through smarter targeting, better creative, and real-time optimization. This can look like implementing enterprise AI tools to augment or automate the media planning process, leveraging AI-powered software to improve and scale creative ideation and testing, exploring AI-powered optimization for real-time performance enhancement across campaigns, or creating AI synthetic audiences—AI-generated, data-driven models of real people—that simulate how specific customer groups might respond to creative variations.
This value-driven approach can have transformative impacts: When AI is used to enhance marketing effectiveness rather than just to improve efficiency, companies can more than double marketing-driven profitability.
Critically, this work goes beyond using AI to drive long-term growth. Marketing teams must also demonstrate how their teams’ use of AI impacts business outcomes, and earn executive buy-in for reinvesting productivity gains into long-term growth rather than settling for short-term wins.
Differentiated AI results require proprietary solutions fueled by large volumes of clean, unified first-party data. And with 58% of industry professionals citing data quality and accessibility issues as critical challenges to adopting AI, marketers must prioritize data readiness to realize these maximal business-level gains.
A lack of data readiness appears to be the norm among marketing teams: Just 17.9% of marketing and advertising professionals describe their first-party data as extensive and well-structured, and a whopping one-third of those professionals say that first-party data plays little-to-no role in their businesses’ current AI initiatives. Even more, nearly half of marketers and advertisers report that their organizations don’t currently use any custom AI solutions or have any imminent plans to develop them.
Personalized, company-specific instances of AI solutions are critical to driving the business-level outcomes that can position marketing as an indispensable revenue driver, but they require quality data for training and rigorous planning to ensure that any desired use cases align with specific, measurable outcomes. As such, organizations that prioritize data readiness—and use that infrastructure to power custom AI solutions—stand to substantially outperform their competitors.
Marketers who frame AI as a driver of business growth rather than solely operational efficiency will be better positioned to secure ongoing investment and strategic influence.
This starts with building business cases that connect specific AI capabilities directly to revenue and brand outcomes. Demonstrating marketing's contribution to enterprise value creates the foundation for budget expansion rather than contraction.
Realizing these outcomes requires prioritizing first-party data infrastructure. Quality, accessible data differentiates AI outputs—without it, even sophisticated tools produce generic results competitors can replicate. Once that data infrastructure is in place, it should fuel custom AI solutions built to amplify growth. Proprietary (and/or semi-custom) models trained on brand-specific data reflect unique market positioning and audience understanding, enabling organizations to establish competitive advantages that strengthen marketing's strategic position over time.
Ultimately, the choices marketing leaders make about AI positioning today will determine whether their teams are properly perceived as growth drivers—or unfairly maligned as cost centers—for years to come.
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Want more insights into how AI is reshaping marketing strategy? We surveyed marketing and advertising professionals from top agencies and brands to gather data on AI adoption trends, performance improvements, and implementation challenges. Check out AI and the Future of Marketing for a deeper dive into the opportunities and obstacles shaping marketing’s AI future.
