Feb 20, 2025
Liz Elfman
Content Marketing Director
Organizations are racing to leverage artificial intelligence for competitive advantage. But how many are truly ready? At data.world, we've been conducting an "AI-Readiness Survey" to help organizations benchmark their preparedness for AI adoption. Here's what we're seeing emerge emerge about the state of enterprise data readiness. (PS - haven't taken the survey yet? Go for it here.)
The journey from survey initiation to completion tells its own story. With a 31% completion rate, we're seeing that organizations are eager to start assessing their AI readiness, but many haven't yet completed the full evaluation.
While interest in AI readiness is high, many organizations may encounter challenging questions that require deeper organizational reflection. It's not just about having AI ambitions—it's about having the data foundation to support them.
Our preliminary results reveal an average overall readiness score of 54.2% across completed surveys. This middle-of-the-road score tells us that most organizations have begun their journey, but still have significant room for growth.
Looking at the category breakdown:
Data Ops & Infrastructure (60.9%): This emerges as the strongest area, suggesting that many organizations have made progress in building technical foundations.
Data Culture (58.9%): Organizations are making strides in having data-driven mindsets, though there's still room for improvement.
AI Strategy & Change Management (52.6%) and Data Quality & Semantics (52.5%): While strategies exist, they may lack depth or organizational alignment.
Data Management & Enablement (49.8%): This lower score suggests that many organizations struggle with making data discoverable, accessible, and usable.
One insight that emerges from our survey is the critical role of knowledge graphs in AI readiness. Organizations without a knowledge graph foundation often report challenges like:
Lack of context for terminology and table relationships
Excessive spreadsheets for ad hoc analysis
"Lift and shift" approaches that fail to address underlying data issues
Data lakes that quickly become data swamps
Organizations who leverage knowledge graph architecture report better data accuracy, stronger relationship modeling, and more intuitive data exploration. These are all critical factors for successful AI implementation.
Our survey has reached organizations worldwide, with the majority hailing from the United States, and several from other countries. We're particularly interested in expanding participation in international markets to gain a more comprehensive global view of AI readiness.
If you haven't completed the AI Readiness Survey yet, we encourage you to do so. The assessment is designed to identify specific areas where your organization can improve its data foundation for AI success.
For organizations that have completed the survey and want deeper insights, our team is available for personalized review calls to discuss your results and develop targeted improvement strategies.
Stay tuned for more detailed insights as we continue to analyze the results and identify patterns that can help all organizations advance their AI readiness journey.
Organizations are racing to leverage artificial intelligence for competitive advantage. But how many are truly ready? At data.world, we've been conducting an "AI-Readiness Survey" to help organizations benchmark their preparedness for AI adoption. Here's what we're seeing emerge emerge about the state of enterprise data readiness. (PS - haven't taken the survey yet? Go for it here.)
The journey from survey initiation to completion tells its own story. With a 31% completion rate, we're seeing that organizations are eager to start assessing their AI readiness, but many haven't yet completed the full evaluation.
While interest in AI readiness is high, many organizations may encounter challenging questions that require deeper organizational reflection. It's not just about having AI ambitions—it's about having the data foundation to support them.
Our preliminary results reveal an average overall readiness score of 54.2% across completed surveys. This middle-of-the-road score tells us that most organizations have begun their journey, but still have significant room for growth.
Looking at the category breakdown:
Data Ops & Infrastructure (60.9%): This emerges as the strongest area, suggesting that many organizations have made progress in building technical foundations.
Data Culture (58.9%): Organizations are making strides in having data-driven mindsets, though there's still room for improvement.
AI Strategy & Change Management (52.6%) and Data Quality & Semantics (52.5%): While strategies exist, they may lack depth or organizational alignment.
Data Management & Enablement (49.8%): This lower score suggests that many organizations struggle with making data discoverable, accessible, and usable.
One insight that emerges from our survey is the critical role of knowledge graphs in AI readiness. Organizations without a knowledge graph foundation often report challenges like:
Lack of context for terminology and table relationships
Excessive spreadsheets for ad hoc analysis
"Lift and shift" approaches that fail to address underlying data issues
Data lakes that quickly become data swamps
Organizations who leverage knowledge graph architecture report better data accuracy, stronger relationship modeling, and more intuitive data exploration. These are all critical factors for successful AI implementation.
Our survey has reached organizations worldwide, with the majority hailing from the United States, and several from other countries. We're particularly interested in expanding participation in international markets to gain a more comprehensive global view of AI readiness.
If you haven't completed the AI Readiness Survey yet, we encourage you to do so. The assessment is designed to identify specific areas where your organization can improve its data foundation for AI success.
For organizations that have completed the survey and want deeper insights, our team is available for personalized review calls to discuss your results and develop targeted improvement strategies.
Stay tuned for more detailed insights as we continue to analyze the results and identify patterns that can help all organizations advance their AI readiness journey.
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