By Justin Rosen, SVP Data & Insights, Ampersand
In 2025, AI became the buzzword of every marketing meeting. In 2026, it will become the backbone of how marketing actually gets done. Marketers are no longer asking if they should use AI and machine learning. They are asking how to make these tools smarter, faster, and more efficient for their goals.
Across industries, AI approaches and agents are quietly transforming how we work. From content creation to campaign optimization, marketers now use AI to automate workflows, generate insights from massive datasets, and iterate faster than ever before. AI enables experimentation at a speed that was unthinkable just a few years ago. The result is that marketers spend less time managing manual processes and more time innovating.
But with this acceleration comes a fundamental question: what fuels these AI engines? The answer is data. Lots and lots and lots of data. But as marketers rely more on automation and predictive intelligence, they are risking poor decision making and business outcomes if the data that feeds these models is below standard. Even worse than generating meaningless insights, it even threatens to create a feedback loop of flawed learnings that can multiply over time. When a model is trained on incomplete or inaccurate information, every decision that follows becomes less accurate. At scale, that degradation can be significant.
As AI becomes embedded in marketing infrastructure, the focus will shift from how much data a company can access to how good that data is. Clean, consented, accurate first-party data will drive more meaningful insights than broad but questionable datasets collected from multiple third parties. This shift puts companies with scaled, high-quality first-party data in a strong position. Whether they are marketers, publishers, or technology providers, those who hold verified audience relationships at scale have an advantage.
For years, marketers have emphasized the importance of first-party data. Now it is no longer just an advantage. It is becoming the defining factor in competitive differentiation. As privacy regulations tighten, cookies deprecate, and consumers demand greater transparency, the value of proprietary audience data has skyrocketed. What is different now is how first-party data intersects with AI development. Machine learning models improve continuously through exposure to data. The more they learn, the smarter they become. However, when that data is shared broadly, such as when it is used to train open AI systems, the competitive advantage begins to erode.
Marketers that sit on high-quality first-party datasets will likely not feel incentivized to let their data fuel systems others can benefit from. Who can blame them? Instead, they are looking for ways to leverage it internally. They are training AI models designed to understand their own customers better, optimize campaigns more precisely, and predict outcomes unique to their brand. In the coming year, we will see a stronger emphasis on data stewardship. Companies will focus not just on collecting and activating first-party data but on refining it to ensure it remains their most valuable and defensible asset.
The marketing industry is entering a period where data connectivity matters more than ever. As large platforms reinforce walled gardens, marketers are increasingly limited in how they can access and combine data, creating more fragmentation at the exact moment AI models need broader and more connected inputs to perform at their best. Interoperability is becoming essential, and marketers need partners who can link data across sources while preserving privacy, control, and accuracy. Privacy-safe collaboration through clean rooms and secure identity matching allows AI systems to learn more effectively and generate stronger insights. As companies guard their first-party assets more closely, responsible data connection will become a key competitive advantage, enabling brands, agencies, and media sellers to unlock smarter models, better predictions, and more effective marketing.
Marketers do not need to overhaul their technology stack overnight to prepare for this shift. Instead, they should focus on three priorities. First, prioritize data quality by investing in processes that ensure first-party data is clean, complete, and continuously validated. A small but accurate dataset will have lower risk of the “bad decision doom loop” vs a larger but messier one when it comes to powering AI insights. Second, develop a data strategy that identifies which assets truly differentiate the business. What do you know about your customers that no one else does? Build AI initiatives around that knowledge base. Third, build privacy and trust into every step. Consumers are more aware than ever of how their data is used. Being transparent, compliant, and ethical in how data is collected and applied will not only protect a brand but also strengthen the long-term sustainability of AI investments.
The next phase of AI in marketing will not be defined by who has the flashiest tool or the biggest model. It will be defined by who has the best data. First-party data is becoming the lifeblood of AI-driven marketing. It determines the accuracy, creativity, and performance of every automated decision. Marketers who understand this will treat their data not just as a resource but as intellectual property, a strategic asset to be nurtured, protected, and used responsibly.
As we enter 2026, one thing is clear. AI may be the engine of modern marketing, but data is the driver’s seat. Those who own it, protect it, and understand its value will lead the way.
