Companies often focus on avoiding fines by being overly cautious with data, a practice called "under-permissioning." This creates a huge opportunity cost by shrinking the marketable audience and leading to wasted ad spend on generalized campaigns.
Related Insights
To succeed, marketers must stop passively accepting the data they're given. Instead, they must proactively partner with IT and privacy teams to advocate for the specific data collection and governance required to power their growth and personalization initiatives.
A company can build a significant competitive advantage in healthcare by deliberately *not* touching or seeing Protected Health Information (PHI). Focusing exclusively on metadata reduces regulatory overhead and security risks, allowing the business to solve the critical problem of data orchestration and intelligence, a layer often neglected by data aggregators.
Cookie deprecation blinds ad platforms like Google and Meta to on-site conversion quality. Marketers can gain a significant performance edge by creating a feedback loop, pushing their attributed first-party data (like lifetime value and margins) back into the platforms' AI systems in near real-time.
As AI personalization grows, user consent will evolve beyond cookies. A key future control will be the "do not train" option, letting users opt out of their data being used to train AI models, presenting a new technical and ethical challenge for brands.
Contrary to the trend of tightening data privacy, the European Commission has proposed a package to soften GDPR and cookie rules. This could lead to fewer consent banners for "low risk" data collection, signaling a potential shift towards more practical and less burdensome privacy regulations for businesses.
While the industry chases complex AI, research shows less than half of marketers (42%) use basic preference data for personalization. This highlights a massive, untapped opportunity to improve customer experience with existing data before investing in advanced technology.
Digital trust with partners requires embedding privacy considerations into their entire lifecycle, from onboarding to system access. This proactive approach builds confidence and prevents data breaches within the extended enterprise, rather than treating privacy as a reactive compliance task.
An AI agent capable of operating across all SaaS platforms holds the keys to the entire company's data. If this "super agent" is hacked, every piece of data could be leaked. The solution is to merge the agent's permissions with the human user's permissions, creating a limited and secure operational scope.
The market reality is that consumers and businesses prioritize the best-performing AI models, regardless of whether their training data was ethically sourced. This dynamic incentivizes labs to use all available data, including copyrighted works, and treat potential fines as a cost of doing business.
To earn consumer data, brands must offer a clear value exchange beyond vague promises of "better experiences." The most compelling benefits are tangible utilities like time savings and seamless cross-device continuity, which are often undervalued by marketers.