INFOGRAPHIC2026 State of Data Integrity and AI Readiness
Key statistics and findings for data and analytics leaders on the AI readiness gap, data governance, data quality, and skills challenges shaping enterprise strategy in 2026.
The AI readiness gap is growing
AI is the defining force shaping enterprise data strategy, but many organizations are moving faster than their foundations can support. This year’s research reveals a growing gap between perceived AI readiness and the operational realities of data quality, governance, business alignment, and skills.
say AI is the primary influence on their data programs
41-43%
say infrastructure, skills, and data readiness are still major obstacles to achieving their AI goals
expect positive ROI from AI in the next 6–11 months
AI enthusiasm is high.
Readiness is not.
AI is now the primary influence on data strategy at most organizations, but the findings show a clear confidence-reality gap between how prepared leaders feel and the challenges they still face.
say AI is the primary influence on their data programs
What leaders say they have
- 87% say they have the infrastructure to support AI
- 86% say they have the skills required
- 88% say they have the data readiness required
What leaders say is still a barrier
- 42% cite infrastructure as a top AI challenge
- 41% cite skills gaps as a barrier
- 43% cite data readiness as a major obstacle
At the same time, leaders report difficulty measuring AI value in business terms
say AI aligns with business goals
have metrics tied to business KPIs
struggle to connect AI performance to business outcomes
While many leaders claim a high degree of preparedness for AI, they also recognize barriers to success in core aspects of AI-readiness. Organizations may struggle to scale the baseline capabilities that support today’s proofs of concept to true enterprise AI deployments.
Infrastructure exists – but data remains the challenge.
Most data leaders believe their organizations have the infrastructure required for AI. But having the right systems in place doesn’t guarantee that the data within them is ready for AI.
87% say they have the infrastructure needed for AI, yet data-related challenges persist across their programs.
The gap isn’t about where data lives – but rather, how well it’s managed.
say they have the infrastructure needed for AI
Top data program challenges:
- 35% lack effective data management tools
- 34% cite gaps in data literacy
- 33% struggle with the complexity of data ecosystems
From a data integrity perspective, leaders report persistent issues with:
- 39% data privacy and security
- 38% data quality
- 32% data integration
Organizations have invested in modern infrastructure, but data integrity remains the limiting factor for AI success.
Data governance is a pivotal factor in AI success
The strongest AI outcomes come from organizations that treat data governance as a strategic enabler rather than a compliance exercise. Governance improves trust in data, strengthens AI readiness, and helps organizations create better AI results.
- 71% of organizations with governance programs report high trust in their data
- Only 50% report high data trust without governance
have an ongoing data governance program
Leaders see data governance as essential for AI initiatives.
say governance improves AI readiness
say governance improves AI outcomes
have expanded data governance to include AI governance
Data governance boosts business outcomes like operational efficiency, revenue generation, modernization, and compliance. The most successful companies are expanding existing data governance programs to include AI governance, rather than treating them as separate tracks.
The data quality debt is due.
Data quality remains the most common data integrity priority, and AI is raising the cost of getting it wrong. As organizations push toward more advanced and autonomous AI use cases, long-standing data quality issues are becoming immediate business risks.
- 51% say data quality is their top data integrity priority
- 43% cite data readiness as the biggest barrier to AI alignment
cite data readiness as the biggest barrier to AI alignment
Long-neglected investment in data quality improvements are gaining momentum.
have at least started discovery, planning, or approval processes to improve data quality for AI
are already executing those initiatives
The old “fix it later” approach to data quality issues no longer works. AI makes poor data more visible, more costly, and harder to work around.
Real-world context is a competitive edge.
Third-party datasets and location intelligence are increasingly used to add real-world context that improves decision-making and strengthens AI results – transforming raw data into accurate, actionable insights.
invest in location intelligence and third-party data enrichment
Top location intelligence use cases
- 41% – Targeted marketing
- 41% – Address data validation and standardization
- 40% – Product & service delivery optimization
What types of third-party data are being used?
- 44% – Customer segmentation and audiences
- 39% – Admin, community, and industry boundaries
- 38% – Consumer demographics
Top challenges faced with location intelligence and third-party data
- 46% cite privacy and security concerns for location intelligence
- 44% struggle with the complexity of integrating location intelligence into their data
- 37% face data quality issues with third-party data
The takeaway:
Context improves outcomes, but only when organizations mitigate issues like privacy and ease of integration.
Skill shortages show that the AI gap is human, not just technical.
Skills are now one of the biggest barriers to enterprise AI success, and the gaps span technical, business, and governance capabilities. Closing the gap requires broad, balanced talent.
- 51% cite skills as a top need for AI readiness
- But only 38% feel very prepared with the right staff skills and training
say skills and resource shortages limit data program success
No single skill dominates. Leaders cite the top missing skills as:
Ability to deploy AI at scale
Responsible AI and compliance expertise
Ability to translate business needs into AI solutions
AI model development and basic AI literacy
The takeaway:
Successful organizations will build AI-focused teams that bring together business, technical, and data skillsets.
AI success depends on data integrity. That starts with fixing the foundations.
The 2026 findings point to a consistent pattern: organizations that succeed with AI are the ones investing in fundamentals.
Strong governance, measurable business alignment, better data quality, richer context, and broader skills create the conditions for scalable AI success.
The competitive advantage will go to the companies building on trusted, well-governed, AI-ready data.
2026 State of Data Integrity and AI Readiness
Get the full report and benchmark your own AI readiness against industry leaders.
Frequently Asked Questions
Are most enterprises ready for AI?
Not as ready as they think. While 87% of data and analytics leaders surveyed claim they have the infrastructure, skills, and data readiness required for AI, the same survey found that 41–43% of those leaders also cite those exact areas as their biggest obstacles. This confidence-reality gap suggests many organizations are operating on optimism rather than verified preparedness.
What is the biggest barrier to AI alignment with business goals?
Data readiness is the most commonly cited obstacle — named by 43% of data and analytics leaders as their top barrier to aligning AI with business objectives. Data quality, infrastructure limitations, skills gaps, and governance challenges follow closely.
How does data governance affect AI readiness and outcomes?
Significantly. Organizations with formal data governance programs are 21 percentage points more likely to report high trust in their data (71% vs. 50% without governance). Leaders also directly credit governance with improving AI readiness (42%) and improving the quality of AI outcomes (39%). The most successful organizations are expanding existing data governance programs to include AI governance, rather than building separate AI governance tracks.
What percentage of organizations have addressed their data quality issues for AI?
Progress is happening but unevenly. While 94% of organizations have at least initiated discovery, planning, or approval processes to improve data quality for AI training and inference, only 55% are actively executing those initiatives. Data quality remains the top data integrity priority for 51% of data and analytics leaders — and the biggest challenge across governance, integration, third-party data, and AI initiatives alike.
What AI skills are enterprises most lacking?
The skills gap is broad rather than concentrated in one area. The most commonly cited missing capabilities include: the ability to deploy AI at scale in a business environment (30%), expertise in responsible AI and compliance (29%), skills in translating business needs into AI solutions (28%), and AI model development and basic AI literacy (27%). This tight clustering across competencies suggests organizations need balanced, cross-functional AI talent rather than narrow specialists.
How are organizations using location intelligence and third-party data to improve AI outcomes?
Nearly all — 96% — of organizations surveyed invest in location intelligence and third-party data enrichment. The most common applications are targeted marketing via customer demographics and segmentation (41%), address data validation and standardization (41%), and product and service delivery optimization (40%). Organizations using this contextual data report stronger AI outcomes, though challenges around privacy and security concerns (46%) and integration complexity (44%) remain.