Six of TDWI's expert fellows offer up their top 2 strategic insights for what to expect in the upcoming year.
As organizations continue to operationalize AI, modernize data platforms, and respond to mounting regulatory and economic pressures, 2026 represents a pivotal year for data and analytics leaders. Experimentation is giving way to execution, and new practices are becoming strategic imperatives.
Below, we've asked six TDWI experts to share their unique predictions on the technologies, practices, and organizational shifts that will shape enterprise data, analytics, and AI over the coming year. These perspectives are grounded in TDWI research, real-world client engagements, and deep experience working with organizations navigating rapid change.
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According to new research from TDWI, AI has moved from the experimental to everyday workflow, but governance and user guardrails lag behind.
Three highlights from the report, called "The Impact of Generative AI on Business," include:
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Here are five items you don’t want to miss this week, including a survey showing enterprises racing into AI without readiness, draft U.S. model-security rules poised to reshape AI governance, and new research on the real business impact of generative AI.
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Here are five podcast episodes that dig into what it takes to be AI-ready: From data foundations and governance to enterprise adoption and skills. (All podcasts available through the major distributors unless otherwise indicated.)
TDWI Speaking of Data — Episode 65: AI Readiness with Fern Halper
TDWI’s Fern Halper breaks down organizational focus areas and the TDWI AI Readiness Assessment, covering data, operations, skills, and governance as practical levers for getting from pilot to production. Listen
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Model audits are important, but without data governance, AI risk is already baked in. TDWI explains why effective AI governance must begin with the data that powers your systems.
When people talk about AI governance, they usually talk about models—how to audit them, how to explain them, how to control them. But here’s the reality: by the time you get to the model, the risk is already baked in. If your data isn’t governed, the rest doesn’t matter.
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From poor data quality to missing context, many AI challenges stem from unprepared data. Learn how AI-ready data can address the top five causes of AI project obstacles.
Many AI projects encounter challenges not because of the models—but because of the data. Without the right structure, labeling, and controls, AI systems can produce variable or unusable results.
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It may seem that BI-ready data is enough for AI. Learn what else AI needs from your data in order to succeed.
BI-ready data is typically aggregated, structured for human interpretation, and optimized for visualization and reporting. It's clean, consistent, and governed—representing significant organizational investment in data quality, integration, and discipline. AI-ready data builds on this foundation but often requires additional elements: greater granularity, labeled training examples, contextual metadata, and real-time accessibility.
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Many teams assume their existing data is ready for AI, but even well-managed data often needs additional preparation to power successful machine learning initiatives.
As organizations race to implement artificial intelligence, one term keeps popping up: AI-ready data. It's more than a buzzword: It's the foundation for building successful, scalable, and responsible AI systems. But what exactly does it mean? And more importantly, how do you know if your data is AI-ready?
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