AI gets the spotlight. Data does the work.
Across industries, organizations are racing to adopt large language models (LLMs), build AI assistants and explore autonomous agents. This ambition is understandable. AI promises faster decisions, new efficiencies and entirely new ways of working. But amid the excitement, many leaders are overlooking a fundamental truth. AI success is not determined by the sophistication of the model alone. It depends on the strength of the underlying data foundation.
That foundation determines whether raw data can become trusted, governed and usable for AI. It determines whether models can operate with context, whether outputs can be explained and whether organizations can scale AI in ways that are responsible, compliant and valuable.
I recently listened to a few episodes of the Pondering AI podcast, and the message about data management came through clearly. In the episode "AI Is As Data Does," featuring Gretchen Stewart, Principal Data Scientist for the Public Sector at Intel, she challenges the idea that AI innovation begins and ends with models and algorithms. Instead, she points to the reality that data leaders have understood for years. Organizations cannot separate AI performance from data quality, governance and accessibility.
The technology has changed. The data challenges remain.
In the Pondering AI episode, Chief Data Concerns, featuring Heidi Lanford, Global Chief Data and Analytics Officer, Lanford reflected on decades of experience helping organizations unlock value from data and noted that many of today's challenges are no different.
Extracting value out of data has advanced a lot in technology. But we're also still seeing the same kinds of struggles in terms of adoption, momentum and areas of focus that we saw 20 or 30 years ago. It's still hard to do. Heidi Lanford
Despite advances in cloud architectures, data fabrics, automation and AI-driven analytics, organizations continue to wrestle with data silos, inconsistent governance practices and cultural barriers to adoption. These are not technology problems. They are organizational challenges requiring strategy, leadership and commitment.
Data intelligence: The next evolution of data management
Reflecting on the past decades, data management was often viewed as essential, but largely invisible. AI has changed that equation and exposed a new reality. In fact, AI has given rise to a new term that needs to be on every leader’s radar: data intelligence.
Every successful AI initiative depends on data that is accurate, trusted, explainable and governed. Without those characteristics, organizations risk creating AI systems that generate unreliable insights, introduce compliance concerns or erode stakeholder trust.
IDC research points to the same conclusion. Consistent data access, governance, lineage, quality and business context are becoming essential to turning AI experimentation into trusted, measurable value.
As AI becomes embedded in decision-making, data management shifts from the operational layer to the enterprise's strategic core. But first, it’s important to clarify what data intelligence is and how it’s the next evolution of data management.
Data intelligence adds value and context to data for AI within a single, governed platform. Data management is the foundation. Think of it as how data is accessed, integrated, prepared, governed and made reliable. Data intelligence builds on that foundation by making data trusted, contextual and operationally ready for real business decisions.
That distinction matters with AI because organizations rarely struggle because of data access and storage alone. They struggle because data is fragmented across platforms, duplicated across pipelines, weakly governed at runtime or disconnected from the business context needed to support trustworthy decisions.
In that sense, data intelligence reframes data management from a back-office discipline into a strategic capability. It’s the layer that makes modern data environments usable, explainable and valuable for AI.
Customer examples show why this distinction matters. Posten Bring, a Nordic postal and logistics group, modernized its data environment to support 24/7 data access, real-time insights and dramatically faster performance. The customer story illustrates how data management is not simply about storing or moving data. It is about creating a stronger foundation for smarter operations, better decisions and future AI growth.
See data intelligence in action.
Learn how Posten Bring modernized its analytics platform to deliver real-time insights, improve operational agility and support 24/7 logistics with SAS® Viya® and SAS SpeedyStore.
A question worth asking
Perhaps the most important question facing organizations today is not, "How quickly can we deploy AI?" Instead, it may be, “Are we investing in the data foundation that is governed and trusted with the same urgency and enthusiasm that we're investing in AI?”
The answer may determine which organizations create sustainable organizational value and which become trapped in a cycle of AI experimentation without a return on investment (ROI). Data intelligence gives leaders a more practical lens for that question. It asks not simply whether data exists, but whether data remains governed and ready to move from preparation through deployment to trusted decisions.
The commentary from both Gretchen Stewart and Heidi Lanford is thought-provoking. AI may be transforming business, but data management remains core to making transformation possible. Data intelligence is the next evolution: connecting data access, preparation, governance, acceleration, analytics and AI into a coherent system that produces trusted outcomes. The winners in this AI race will not simply be the organizations chasing the most advanced models. They will be the ones who recognize that AI success is, ultimately, as data does.
