Information and Analytics Leaders Suppose They’re AI-Prepared. They’re In all probability Not. 

How AI-ready is your group, actually? Perhaps not as prepared as you’d hope. This 12 months’s State of Information Integrity and AI Readiness report, revealed in partnership between Exactly and the Middle for Utilized AI and Enterprise Analytics at Drexel College’s LeBow School of Enterprise, surfaces an uncomfortable reality: There’s a big notion hole between the AI progress knowledge leaders report versus the challenges that have to be overcome.

This 12 months’s findings hit near house. In my years constructing knowledge and AI packages as Chief Information Officer at Exactly, I’ve seen first-hand how optimism about AI readiness can outpace actuality. Whereas the trade is buzzing with pleasure, the true work of aligning know-how, individuals, and governance is simply starting.

The analysis exhibits that this problem is pervasive. We surveyed over 500 senior knowledge and analytics leaders at main international enterprises about their AI preparedness, knowledge integrity, and the obstacles they’re going through. Right here’s what stands out:

Most respondents declare they’ve what AI requires:

• Information readiness (88%)
• Enterprise technique and monetary assist (88%)
• AI governance (87%)
• Infrastructure (87%)
• Expertise (86%)

And but, these very same parts high the record of greatest AI challenges, with many citing:

• Infrastructure (42%)
• Expertise (41%)
• Information readiness (43%)
• Enterprise technique and monetary assist (41%)
• AI governance (39%)

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

Right here’s what the information exhibits about AI readiness and what separates the organizations heading in the right direction from these headed for hassle:

The Confidence-Actuality Hole Threatens AI Success

Our research exhibits that AI dominates conversations about knowledge technique. Greater than half of organizations (52%) say it’s the first pressure shaping their knowledge packages. Corporations are going all-in on AI use circumstances throughout the board for safety and compliance (33-34%), provide chain optimization (33%), software program improvement (32%), customer support chatbots (31%), and extra.

However right here’s the place issues get attention-grabbing: forty‑p.c of respondents cite know-how infrastructure as a problem to aligning AI with enterprise targets, regardless of most saying their infrastructure is already AI‑prepared. This discovering highlights a deeper readiness difficulty: Organizations might really feel assured, however their technical foundations are falling brief.

The enterprise alignment numbers inform an analogous story. Seventy-one p.c say their AI efforts align with enterprise objectives. However solely 31% observe metrics resembling income development, price discount, or buyer satisfaction. That’s plenty of confidence, given the shortage of proof. In current conversations with fellow CDOs, all of us admitted we’re nice at measuring utility, however true ROI is way more durable to pin down.

The survey exhibits organizations could also be overly optimistic about ROI.  Thirty-two count on constructive ROI from AI within the coming six to 11 months, and 16% count on constructive ROI within the subsequent six months, regardless of many responses indicating that crucial shortfalls in governance, expertise, and knowledge high quality might impression their outcomes.

Clearly, organizations are enthusiastic about AI. Nevertheless, this will cause them to be overly optimistic in the event that they’re not actually ready for what’s required to graduate AI pilot initiatives to actual, cross-enterprise manufacturing environments.

Information Governance Emerges because the Make-or-Break Issue

Right here’s some excellent news: the report exhibits that knowledge governance has a measurable impression. Of organizations with knowledge governance packages, 71% report excessive belief of their knowledge. With out governance, belief drops to 50%.

This is sensible when you consider what governance does: handle knowledge high quality, lineage, utilization, and entry insurance policies for crucial knowledge. Organizations in extremely regulated industries usually have better knowledge governance maturity because of necessary compliance necessities.

What I discover most telling is how corporations deal with rising AI governance packages alongside their current knowledge governance efforts. The true winners are those that broaden their current knowledge governance to incorporate AI governance, somewhat than treating them as separate or one-off initiatives – or, worse, scaled again their concentrate on knowledge governance in favor of AI funding.

Information governance is the differentiator that delivers 10-20% enhancements within the outcomes executives care most about – primarily:

• Operational effectivity (19%)
• Income era (16%)
• Modernization (15%)
• Regulatory compliance (13%)

Past the enterprise outcomes, 42% of knowledge leaders say governance improves their AI readiness, and 39% report it straight enhances the standard of AI outcomes, proving that knowledge governance is way from only a compliance checkbox; it’s important.

From my perspective, treating knowledge and AI governance as a “mission completed” field to examine is dangerous. The organizations that hold evolving their governance, particularly as AI matures – are those that can win in the long term.

REPORT2026 State of Information Integrity and AI Readiness

Findings from a survey of worldwide knowledge and analytics leaders.

Learn the report

Information High quality Debt Undermines AI Ambitions

Information high quality tops the information integrity precedence record for 51% of knowledge leaders. It’s the highest difficulty throughout seven of eight questions in our survey associated to knowledge governance challenges, knowledge integration issues, third-party knowledge enrichment, and AI initiatives.

This doesn’t shock me; corporations have been battling knowledge high quality because the early days of knowledge warehouses, straight via the large knowledge hype, and into the cloud knowledge lake.

We’ve watched the information entry panorama shift dramatically – from the times of keypunch operators to as we speak’s decentralized, everybody’s-a-data-engineer actuality. The impression of that is seen daily: extra entry factors, extra apps, and extra alternatives for poor knowledge to creep in. Incentives and requirements matter, and with out them, knowledge high quality debt simply retains rising.

However AI has modified the sport and elevated the potential danger of poor-quality knowledge.  While you prepare AI fashions on untrustworthy knowledge, it is going to propagate that knowledge into inaccurate AI outputs. And, if your corporation desires to learn from autonomous AI brokers, you can not safely grant decision-making skill if these brokers are liable to working on dangerous knowledge.

The worst half? Twenty-nine p.c say their most important impediment to getting high-quality knowledge is definitely measuring knowledge high quality within the first place. And sadly, you’ll be able to’t repair what you’ll be able to’t measure.

There may be excellent news revealed within the analysis, although. When corporations spend money on knowledge governance and knowledge integration, high quality will get higher:

• 44% say improved high quality is governance’s high profit
• 45% level to knowledge high quality as integration’s greatest win

Context Gives the Aggressive Edge for AI

The info you accumulate from your individual operations is simply the place to begin. To make good selections, you should perceive what’s occurring in the true world impacting your prospects, suppliers, supply routes, properties, and networks.

Location intelligence and knowledge enrichment present that context, and so they remodel uncooked knowledge into one thing actionable. Ninety-six p.c of organizations are already doing this, which exhibits simply how normal this observe has turn into.

Corporations use location intelligence throughout the board to be used circumstances like:

• Focused advertising and marketing primarily based on buyer demographics (41%)
• Validating and cleansing up deal with knowledge (41%)
• Optimizing deliveries and repair (40%)
• Assessing danger and processing claims (39%)

On the information enrichment facet, 44% use buyer segmentation and viewers knowledge, 38% use shopper demographics, and 39% use administrative boundaries for geographic context.

Nevertheless, knowledge enrichment requires focus to keep away from widespread points. When leveraging location intelligence insights, knowledge and analytics leaders report considerations about privateness and safety (46%) and integration complexity (44%). And when incorporating third-party datasets, extra challenges embrace:

• high quality points (37%)
• privateness and ethics questions (33%)
• regulatory compliance (32%)
• programs that don’t simply combine (31%)

If that sounds acquainted, these are similar to the governance and compliance challenges that hold popping up when corporations attempt to align AI with enterprise objectives.

At Exactly, we’ve seen how including context via knowledge enrichment is usually a game-changer – however provided that you’re vigilant about high quality, privateness, and integration.

Expertise Scarcity Recognized as High Barrier

Corporations have constructed out AI platforms, gathered knowledge, and launched knowledge integrity initiatives. However the survey exhibits the true bottleneck isn’t know-how, it’s individuals. Greater than half of knowledge leaders surveyed (51%) say expertise are their high want for AI readiness, whereas solely 38% really feel assured they’ve the precise workers expertise and coaching.

What’s attention-grabbing is how evenly the talents gaps are unfold out. Information leaders report ability gaps for each competency measured, clustering between 25% and 30% per competency. The reply just isn’t so simple as hiring extra knowledge scientists or enterprise analysts. Organizations want individuals who supply a breadth of expertise to assist the dimensions and complexity of AI.

Right here’s how this breaks down:

• 30% can’t deploy AI at scale in a enterprise setting
• 29% lack experience in accountable AI and compliance
• 28% wrestle to translate enterprise wants into AI options
• 27% need assistance with AI mannequin improvement and fundamental AI literacy
• 26% have hassle bridging technical and enterprise groups, turning AI findings into motion, and understanding enterprise processes

In constructing groups all through my profession, I’ve discovered that generalists – those that can bridge technical and enterprise worlds – are simply as crucial as specialists. Translating AI findings into actionable enterprise methods is commonly the toughest half, and it’s the place the correct mix of expertise makes all of the distinction.

Construct Your 2026 Information Integrity Technique

Reflecting on this 12 months’s findings, I’m struck by how a lot they reinforce what I’ve seen all through my profession: the basics of knowledge technique, governance, and expertise are extra crucial than ever. The challenges and alternatives highlighted on this report are the identical realities I’ve confronted personally, and I do know a lot of my friends are navigating the identical terrain.

What excites me most is how these insights might help different knowledge leaders reduce via the noise and concentrate on what actually issues. Whether or not you’re simply beginning your AI journey or scaling mature packages, the teachings right here – about bridging the disconnect by investing in knowledge integrity and constructing the precise groups – are important for long-term success.

For deeper evaluation and sensible steerage in your group, I encourage you to dig into the total  2026 State of Information Integrity and AI Readiness report. These findings will allow you to outline a knowledge technique that’s not simply AI-ready, however future-ready.

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