Knowledge 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 Knowledge Integrity and AI Readiness report, revealed in partnership between Exactly and the Heart for Utilized AI and Enterprise Analytics at Drexel College’s LeBow School of Enterprise, surfaces an uncomfortable fact: There’s a big 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 packages as Chief Knowledge 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 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:

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

And but, these very same components high the listing of largest AI challenges, with many citing:

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

That’s not a minor discrepancy; that’s a basic 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 hassle:

The Confidence-Actuality Hole Threatens AI Success

Our research reveals 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 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 goals, regardless of most saying their infrastructure is already AI‑prepared. This discovering highlights a deeper readiness problem: Organizations might 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% observe metrics equivalent to income progress, value discount, or buyer satisfaction. That’s numerous confidence, given the dearth 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 reveals organizations could also be overly optimistic about ROI.  Thirty-two anticipate constructive ROI from AI within the coming six to 11 months, and 16% anticipate constructive ROI within the subsequent six months, regardless of many responses indicating that essential shortfalls in governance, expertise, and knowledge high quality might influence their outcomes.

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

Knowledge Governance Emerges because the Make-or-Break Issue

Right here’s some excellent news: the report reveals that knowledge governance has a measurable influence. 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 concentrate on what governance does: handle knowledge high quality, lineage, utilization, and entry insurance policies for essential knowledge. Organizations in extremely regulated industries usually have higher knowledge governance maturity as a result of obligatory compliance necessities.

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

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

• Operational effectivity (19%)
• Income technology (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 way from only a compliance checkbox; it’s important.

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

REPORT2026 State of Knowledge Integrity and AI Readiness

Findings from a survey of world knowledge and analytics leaders.

Learn the report

Knowledge High quality Debt Undermines AI Ambitions

Knowledge high quality tops the info integrity precedence listing for 51% of information leaders. It’s the highest problem 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 fighting knowledge high quality for the reason that early days of information warehouses, straight by 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 as we speak’s decentralized, everybody’s-a-data-engineer actuality. The influence 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.  Once you practice AI fashions on untrustworthy knowledge, it’s going to propagate that knowledge into inaccurate AI outputs. And, if your small business needs to learn from autonomous AI brokers, you can not safely grant decision-making means if these brokers are vulnerable to working on dangerous knowledge.

The worst half? Twenty-nine % say their most important impediment to getting high-quality knowledge is definitely measuring knowledge high quality within the first place. And sadly, you possibly can’t repair what you possibly can’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 largest win

Context Supplies the Aggressive Edge for AI

The info you accumulate from your personal operations is simply the place to begin. To make sensible choices, you must perceive what’s occurring in the true 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 commonplace this follow has turn into.

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

• Focused advertising and marketing based mostly 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 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 issues about privateness and safety (46%) and integration complexity (44%). And when incorporating third-party datasets, further challenges embrace:

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

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

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

Expertise Scarcity Recognized as Prime Barrier

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

What’s attention-grabbing is how evenly the talents gaps are unfold out. Knowledge leaders report talent gaps for each competency measured, clustering between 25% and 30% per competency. The reply isn’t so simple as hiring extra knowledge scientists or enterprise analysts. Organizations want individuals who supply a breadth of expertise 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 atmosphere
• 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 hassle bridging technical and enterprise groups, turning AI findings into motion, and understanding enterprise processes

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

Construct Your 2026 Knowledge 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 expertise are extra essential 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 may also help different knowledge leaders minimize by the noise and concentrate on what really 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 suitable groups – are important for long-term success.

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

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