Data and Analytics Leaders Think They’re AI-Ready. They’re Probably Not. 

How AI-ready is your organization, really? Maybe not as ready as you would hope. This year’s State of Data Integrity and AI Readiness report, published in partnership between Precisely and the Center for Applied AI and Business Analytics at Drexel University’s LeBow College of Business, surfaces an uncomfortable truth: There’s a significant perception gap between the AI progress data leaders report versus the challenges that need to be overcome.

This year’s findings hit close to home. In my years building data and AI programs as Chief Data Officer at Precisely, I’ve seen first-hand how optimism about AI readiness can outpace reality. While the industry is buzzing with excitement, the real work of aligning technology, people, and governance is just beginning.

The research shows that this challenge is pervasive. We surveyed over 500 senior data and analytics leaders at major global enterprises about their AI preparedness, data integrity, and the obstacles they’re facing. Here’s what stands out:

Most respondents claim they have what AI requires:

• Data readiness (88%)
• Business strategy and financial support (88%)
• AI governance (87%)
• Infrastructure (87%)
• Skills (86%)

And yet, these exact same elements top the list of biggest AI challenges, with many citing:

• Infrastructure (42%)
• Skills (41%)
• Data readiness (43%)
• Business strategy and financial support (41%)
• AI governance (39%)

That’s not a minor discrepancy; that’s a fundamental disconnect.

Here’s what the data shows about AI readiness and what separates the organizations on the right track from those headed for trouble:

The Confidence-Reality Gap Threatens AI Success

Our study shows that AI dominates conversations about data strategy. More than half of organizations (52%) say it’s the primary force shaping their data programs. Companies are going all-in on AI use cases across the board for security and compliance (33-34%), supply chain optimization (33%), software development (32%), customer service chatbots (31%), and more.

But here’s where things get interesting: forty‑percent of respondents cite technology infrastructure as a challenge to aligning AI with business objectives, despite most saying their infrastructure is already AI‑ready. This finding highlights a deeper readiness issue: Organizations may feel confident, but their technical foundations are falling short.

The business alignment numbers tell a similar story. Seventy-one percent say their AI efforts align with business goals. But only 31% track metrics such as revenue growth, cost reduction, or customer satisfaction. That’s a lot of confidence, given the lack of proof. In recent conversations with fellow CDOs, we all admitted we’re great at measuring utility, but true ROI is much harder to pin down.

The survey shows organizations may be overly optimistic about ROI.  Thirty-two expect positive ROI from AI in the coming six to 11 months, and 16% expect positive ROI in the next six months, despite many responses indicating that critical shortfalls in governance, skills, and data quality may impact their results.

Clearly, organizations are excited about AI. However, this may lead them to be overly optimistic if they’re not truly prepared for what’s required to graduate AI pilot projects to real, cross-enterprise production environments.

Data Governance Emerges as the Make-or-Break Factor

Here’s some good news: the report shows that data governance has a measurable impact. Of organizations with data governance programs, 71% report high trust in their data. Without governance, trust drops to 50%.

This makes sense when you think about what governance does: manage data quality, lineage, usage, and access policies for critical data. Organizations in highly regulated industries often have greater data governance maturity due to mandatory compliance requirements.

What I find most telling is how companies handle emerging AI governance programs alongside their existing data governance efforts. The real winners are those who expand their existing data governance to include AI governance, rather than treating them as separate or one-off projects – or, worse, scaled back their focus on data governance in favor of AI investment.

Data governance is the differentiator that delivers 10-20% improvements in the outcomes executives care most about – primarily:

• Operational efficiency (19%)
• Revenue generation (16%)
• Modernization (15%)
• Regulatory compliance (13%)

Beyond the business outcomes, 42% of data leaders say governance improves their AI readiness, and 39% report it directly enhances the quality of AI outcomes, proving that data governance is far from just a compliance checkbox; it’s essential.

From my perspective, treating data and AI governance as a “mission accomplished” box to check is risky. The organizations that keep evolving their governance, especially as AI matures – are the ones that will win in the long run.

REPORT2026 State of Data Integrity and AI Readiness

Findings from a survey of global data and analytics leaders.

Read the report

Data Quality Debt Undermines AI Ambitions

Data quality tops the data integrity priority list for 51% of data leaders. It’s the top issue across seven of eight questions in our survey related to data governance challenges, data integration problems, third-party data enrichment, and AI initiatives.

This doesn’t surprise me; companies have been struggling with data quality since the early days of data warehouses, straight through the big data hype, and into the cloud data lake.

We’ve watched the data entry landscape shift dramatically – from the days of keypunch operators to today’s decentralized, everyone’s-a-data-engineer reality. The impact of this is visible every day: more entry points, more apps, and more opportunities for poor data to creep in. Incentives and standards matter, and without them, data quality debt just keeps growing.

But AI has changed the game and elevated the potential risk of poor-quality data.  When you train AI models on untrustworthy data, it will propagate that data into inaccurate AI outputs. And, if your business wants to benefit from autonomous AI agents, you cannot safely grant decision-making ability if those agents are at risk of operating on bad data.

The worst part? Twenty-nine percent say their most significant obstacle to getting high-quality data is actually measuring data quality in the first place. And unfortunately, you can’t fix what you can’t measure.

There is good news revealed in the research, though. When companies invest in data governance and data integration, quality gets better:

• 44% say improved quality is governance’s top benefit
• 45% point to data quality as integration’s biggest win

Context Provides the Competitive Edge for AI

The data you collect from your own operations is just the starting point. To make smart decisions, you need to understand what’s happening in the real world impacting your customers, suppliers, delivery routes, properties, and networks.

Location intelligence and data enrichment provide that context, and they transform raw data into something actionable. Ninety-six percent of organizations are already doing this, which shows just how standard this practice has become.

Companies use location intelligence across the board for use cases like:

• Targeted marketing based on customer demographics (41%)
• Validating and cleaning up address data (41%)
• Optimizing deliveries and service (40%)
• Assessing risk and processing claims (39%)

On the data enrichment side, 44% use customer segmentation and audience data, 38% use consumer demographics, and 39% use administrative boundaries for geographic context.

However, data enrichment requires focus to avoid common issues. When leveraging location intelligence insights, data and analytics leaders report concerns about privacy and security (46%) and integration complexity (44%). And when incorporating third-party datasets, additional challenges include:

• quality issues (37%)
• privacy and ethics questions (33%)
• regulatory compliance (32%)
• systems that don’t easily integrate (31%)

If that sounds familiar, these are very similar to the governance and compliance challenges that keep popping up when companies try to align AI with business goals.

At Precisely, we’ve seen how adding context through data enrichment can be a game-changer – but only if you’re vigilant about quality, privacy, and integration.

Skills Shortage Identified as Top Barrier

Companies have built out AI platforms, gathered data, and launched data integrity initiatives. But the survey shows the real bottleneck isn’t technology, it’s people. More than half of data leaders surveyed (51%) say skills are their top need for AI readiness, while only 38% feel confident they have the right staff skills and training.

What’s interesting is how evenly the skills gaps are spread out. Data leaders report skill gaps for every competency measured, clustering between 25% and 30% per competency. The answer is not as simple as hiring more data scientists or business analysts. Organizations need people who offer a breadth of skills to support the scale and complexity of AI.

Here’s how this breaks down:

• 30% can’t deploy AI at scale in a business environment
• 29% lack expertise in responsible AI and compliance
• 28% struggle to translate business needs into AI solutions
• 27% need help with AI model development and basic AI literacy
• 26% have trouble bridging technical and business teams, turning AI findings into action, and understanding business processes

In building teams throughout my career, I’ve learned that generalists – those who can bridge technical and business worlds – are just as critical as specialists. Translating AI findings into actionable business strategies is often the hardest part, and it’s where the right mix of skills makes all the difference.

Build Your 2026 Data Integrity Strategy

Reflecting on this year’s findings, I’m struck by how much they reinforce what I’ve seen throughout my career: the fundamentals of data strategy, governance, and skills are more critical than ever. The challenges and opportunities highlighted in this report are the same realities I’ve faced personally, and I know many of my peers are navigating the same terrain.

What excites me most is how these insights can help other data leaders cut through the noise and focus on what truly matters. Whether you’re just starting your AI journey or scaling mature programs, the lessons here – about bridging the disconnect by investing in data integrity and building the right teams – are essential for long-term success.

For deeper analysis and practical guidance for your organization, I encourage you to dig into the full  2026 State of Data Integrity and AI Readiness report. These findings will help you define a data strategy that’s not just AI-ready, but future-ready.

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