Post by AMEC Measurement and Evaluation

The AI conversation is largely focused on models, prompts, agents, and the latest capabilities. But many organizations still can't answer a much simpler question: Can we trust the data feeding those systems? As communicators, researchers, and measurement professionals, we spend a lot of time talking about AI visibility, GEO, decision intelligence, and trustworthy AI. Yet all of those depend on something far less glamorous: data quality. The reality is that AI systems are generating insights, summaries, recommendations, and citations based on massive amounts of unstructured data. If that data is inaccurate, poorly structured, lacking context, or disconnected from real-world outcomes, AI will simply amplify those issues at scale. One of the most interesting concepts in @AMEC's new Data Quality Principles white paper is the rise of Context Engineering - the idea that AI effectiveness depends not just on the model, but on how organizations organize, govern, and provide context to the information those systems rely on. For communicators, this creates a new responsibility. We are no longer just consumers of data. We are increasingly: • Consumers of AI outputs • Creators of content that influences and grounds AI systems • Curators of trusted information • Correctors of misinformation The future of AI visibility, GEO, communications intelligence, and decision-making may depend less on who has the best model and more on who has the best data and context. We explore these ideas and key takeaways from AMEC's new white paper, Why Data Quality Is the Missing Foundation of AI Visibility, GEO, and Communications Intelligence, here: https://lnkd.in/eyYNKheG What role do you think communicators should play in improving data quality and AI governance? Author: Nicole Moreo #AI #DataQuality #GEO #GenerativeEngineOptimization #CommunicationsMeasurement #DecisionIntelligence #AIVisibility #ContextEngineering #AMEC