Information and Analytics Leaders Assume 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, printed in partnership between Exactly and the Heart for Utilized AI and Enterprise Analytics at Drexel College’s LeBow Faculty of Enterprise, surfaces an uncomfortable fact: There’s a major notion hole between the AI progress knowledge leaders report versus the challenges that must be overcome.

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

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

Most respondents declare they’ve what AI requires:

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

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

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

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

Right here’s what the info reveals about AI readiness and what separates the organizations heading in the right direction from these headed for bother:

The Confidence-Actuality Hole Threatens AI Success

Our examine reveals that AI dominates conversations about knowledge technique. Greater than half of organizations (52%) say it’s the first power shaping their knowledge applications. Corporations are going all-in on AI use instances 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‑% of respondents cite expertise 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 situation: Organizations could really feel assured, however their technical foundations are falling brief.

The enterprise alignment numbers inform an identical story. Seventy-one % say their AI efforts align with enterprise targets. However solely 31% monitor metrics similar to income development, price discount, or buyer satisfaction. That’s a whole lot 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 tougher to pin down.

The survey reveals 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, abilities, and knowledge high quality could impression their outcomes.

Clearly, organizations are enthusiastic about AI. Nonetheless, this may occasionally make them 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 reveals that knowledge governance has a measurable impression. Of organizations with knowledge governance applications, 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 larger knowledge governance maturity attributable to obligatory compliance necessities.

What I discover most telling is how corporations deal with rising AI governance applications alongside their present knowledge governance efforts. The actual winners are those that broaden their present knowledge governance to incorporate AI governance, moderately than treating them as separate or one-off initiatives – or, worse, scaled again their deal with 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 information leaders say governance improves their AI readiness, and 39% report it instantly enhances the standard of AI outcomes, proving that knowledge governance is much from only a compliance checkbox; it’s important.

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

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 info integrity precedence listing for 51% of information leaders. It’s the highest situation 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 scuffling with knowledge high quality because the early days of information warehouses, straight by means of the large knowledge hype, and into the cloud knowledge lake.

We’ve watched the info entry panorama shift dramatically – from the times of keypunch operators to at this time’s decentralized, everybody’s-a-data-engineer actuality. The impression of that is seen day-after-day: 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.  If you prepare AI fashions on untrustworthy knowledge, it is going to propagate that knowledge into inaccurate AI outputs. And, if what you are promoting desires to learn from autonomous AI brokers, you can not safely grant decision-making capacity if these brokers are vulnerable to working on unhealthy knowledge.

The worst half? Twenty-nine % say their most vital 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’s excellent news revealed within the analysis, although. When corporations put money into knowledge governance and knowledge integration, high quality will get higher:

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

Context Gives the Aggressive Edge for AI

The information you gather from your personal operations is simply the place to begin. To make good choices, you have to perceive what’s taking place in the actual world impacting your clients, suppliers, supply routes, properties, and networks.

Location intelligence and knowledge enrichment present that context, they usually rework uncooked knowledge into one thing actionable. Ninety-six % of organizations are already doing this, which reveals simply how customary this follow has turn into.

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

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

On the info enrichment aspect, 44% use buyer segmentation and viewers knowledge, 38% use client demographics, and 39% use administrative boundaries for geographic context.

Nonetheless, 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, further challenges embody:

• 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 maintain popping up when corporations attempt to align AI with enterprise targets.

At Exactly, we’ve seen how including context by means of knowledge enrichment could be a game-changer – however provided that you’re vigilant about high quality, privateness, and integration.

Abilities Scarcity Recognized as Prime Barrier

Corporations have constructed out AI platforms, gathered knowledge, and launched knowledge integrity initiatives. However the survey reveals the actual bottleneck isn’t expertise, it’s folks. Greater than half of information leaders surveyed (51%) say abilities are their prime want for AI readiness, whereas solely 38% really feel assured they’ve the precise workers abilities 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 shouldn’t be so simple as hiring extra knowledge scientists or enterprise analysts. Organizations want individuals who supply a breadth of abilities to help the size 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 primary AI literacy
• 26% have bother 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 usually the toughest half, and it’s the place the right combination of abilities 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 information technique, governance, and abilities 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 lots of my friends are navigating the identical terrain.

What excites me most is how these insights might help different knowledge leaders reduce by means of the noise and deal with what actually issues. Whether or not you’re simply beginning your AI journey or scaling mature applications, 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 complete  2026 State of Information Integrity and AI Readiness report. These findings will provide help to outline a knowledge technique that’s not simply AI-ready, however future-ready.