Will Data Scales Balance Out?
Let’s look at the divide between AI and insights-driven marketing. No one doubts the speed and efficiency of AI, but some might argue that it’s leading to poor data quality and it accelerates mistakes.
Gillian MacPherson, Senior Vice President of Product Management at Epsilon, in a piece for Marketing Dive, notes that, “For years, the marketing and ad-tech ecosystem has been locked in competition around data scale. Vendors proudly tout the number of households they reach, the devices they recognize or the trillions of signals flowing through their platforms. Bigger numbers have become shorthand for marketing sophistication, with scale still treated as a primary competitive differentiator across the industry. But in the industry’s relentless race to amass more data—often across increasingly interconnected partners and platforms—one critical question is often overlooked: How accurate is the data behind those numbers?”
The accuracy question can no longer be ignored, argues MacPherson in, “Why data accuracy matters more than data scale amid the rise of AI.” To be sure, AI, automation, and algorithms are part of modern marketing operations making decisions at scale regarding targeting, personalization, and measurement. But the principle of “garbage in, garbage out” has never been more relevant, she notes: “It is a dangerous, double-edged problem that compounds risk at the very moment speed and precision matter most… Because automation operates at speed and scale, these issues spread fast. The problem with inaccurate data is not simply that it is wrong, but that it is wrong at scale, magnifying inefficiency and eroding trust in results.”
The insights capability is one function that can still be a guardrail against bad data quality. Data standards and governance have never been more important in the age of AI.
MacPherson notes that, “The real competitive advantage in modern marketing is not data volume, but data accuracy, validation, and usability. Data drives performance when it is verified and continuously refreshed, resolved to real people and connected across partners and platforms in ways that allow organizations to collaborate and create new, high-value data assets. It must also be structured in a way that AI and automation systems can rely on with confidence.”
Ultimately, MacPherson believes the data scales will balance out, and that marketers will signal a broader industry shift from prioritizing data quantity to prioritizing data trustworthiness, usability, and the ability to support more collaborative, interoperable approaches to data-driven marketing.
TMRE Session Spotlight: Reinforcing Accuracy
During TMRE 2026, which will be held October 5-7, the session, “Fast, Wrong, or Both? Reclaiming Accuracy in the Age of AI Marketing,” will be presented by Thomas Zoëga Ramsøy, Founder & CEO, Neurons.
Marketing has never moved faster and that is exactly the problem. As generative AI accelerates content production and decision-making, many organizations are trading accuracy for speed. Insights teams, traditionally responsible for rigor and validation, are increasingly left out of the process entirely. But a different model is emerging.
In this session, we will show how combining predictive, generative, and suggestive AI can shift insights upstream, embedding evidence directly into creative development rather than evaluating it after the fact. Built on decades of neuroscience research into attention and memory, these systems provide early indicators of performance and enable marketers to iterate with confidence.
We will walk through the Creative AI Loop in action, including how unified KPIs such as single-score metrics can align teams and drive better decisions across organizations. A live or recent client example will illustrate how this approach delivers both speed and accuracy at scale. The takeaway: insights leaders that are using good AI solutions will not become obsolete. They are becoming more critical than ever as the architects and stewards of AI-powered decision systems.
Click here for more TMRE 2026 registration information and the show agenda.
Building Trust in the AI Age
In Forbes’ “What Product Marketers Need to Succeed in the Age of AI,” a similar case is made in that generating content for marketers has become easier… yet there is a danger of losing or diminishing the value of authenticity.
“Product marketers possess something that AI cannot replicate: the ability to connect deep product understanding with genuine customer pain points,” observes Forbes. “The most successful product marketers are using AI as a sophisticated research and execution engine that amplifies their expertise. This approach treats AI as an intelligent assistant rather than an autonomous content creator.”
Quality control, Forbes notes, is one key tactic that is becoming ever more relevant, as this will multiply the benefits of AI. Forbes writes, “Developing approval workflows, fact-checking protocols, and performance measurement systems ensures AI-generated content maintains accuracy, brand consistency, and strategic alignment. This becomes especially critical when content volume increases dramatically through AI automation.”
Forbes further asserts that product marketers who can synthesize AI-generated market intelligence with customer empathy create unique value that no tool can replicate.
Currently, insights and marketing professionals are awash in data and AI-powered marketing. As both Forbes and MacPherson note, this makes accuracy and authenticity a valued commodity in a sea of sameness. Making sure standards are set to high-quality data, and focusing on accurate, actionable, and trusted understanding of the consumer remains extremely relevant, says MacPherson: “Because when data drives decisions at scale, accuracy is not a nice-to-have. It is the difference between momentum and misdirection.”
Video: “AI Changed the Rules of Marketing: The Shift No One Could Ignore,” courtesy of Solve It Like A Marketer.
