If there’s one factor that’s clear from each dialog I’ve had not too long ago – whether or not with prospects, colleagues, or business friends – it’s this: AI ambition has by no means been larger.
However ambition alone doesn’t equal readiness.
In our latest Information Integrity & AI Discussion board, I had the chance to sit down down with Rabun Jones, CIO at C Spire; Andrew Brust, CEO of Blue Badge Insights; and Dave Shuman, Chief Information Officer at Exactly.
Collectively, we unpacked what it actually means to be “AI prepared” – and why so many organizations are struggling to show that ambition into measurable outcomes.
The dialogue was grounded in findings from information and analytics leaders within the 2026 Information Integrity & AI Readiness report, revealed by Exactly in partnership with the Heart for Utilized AI and Enterprise Analytics at Drexel College’s LeBow School of Enterprise.
One constant theme emerged: there’s a rising hole between how prepared organizations suppose they’re, and what it truly takes to succeed with AI at scale.
Let’s break down the most important takeaways.
The AI Readiness Hole Is Actual, and Rising
In keeping with the report, 87% of organizations say they’re prepared for AI. However on the identical time, 40–43% cite infrastructure, expertise, and information readiness as main blockers.
So, what’s the disconnect? As Andrew Brust put it:
“It’s laborious for individuals to say no as a result of that appears like they’re cynical about AI, and there’s a lot stress to be optimistic about it.” He went on to elucidate how there’s each exterior stress and real pleasure driving inflated confidence. However beneath that enthusiasm, many organizations haven’t absolutely accounted for the complexity of scaling AI.
Rabun Jones highlighted one other key issue:
“I do suppose that a few of it’s a definition drift … what you have been serious about a 12 months in the past with AI or what it might do could be very totally different than what you’re serious about right now.”
In different phrases, the goalposts are shifting. What counted as “AI prepared” a 12 months in the past – fundamental information entry, some experimentation – is not sufficient. In the present day, readiness means:
- Governance at scale
- Safe deployment
- Repeatable outcomes
- Operational integration
Dave Shuman summed it up with an idea that resonated throughout the panel: altitude confusion.
“Organizations are evaluating readiness on the platform degree: ‘Do we now have the infrastructure provision? Do we now have subscriptions to the suitable LLMs?’ However the true check of readiness lives one flooring down from that, on the working mannequin degree.”
Dave additionally explored what number of organizations are efficiently piloting AI, however far fewer are scaling it. As he put it, “AI readiness isn’t experimentation. It’s about repeatability.”
That distinction issues. Experimentation permits for:
- Remoted use instances
- Restricted danger
- Handbook oversight
However repeatability requires:
- Information high quality
- Governance
- Monitoring
- Cross-functional accountability
And most organizations aren’t there but. Much more importantly, there’s usually confusion between being able to experiment and being prepared for enterprise deployment. That is the place many AI initiatives stall.
Key takeaway: Merely having the appropriate instruments in place doesn’t equate to AI readiness. You want a repeatable, ruled working mannequin.
Governance Isn’t an AI Barrier. It’s an Accelerator.
Governance got here up repeatedly in our dialogue, and never in the way in which you may anticipate.
Too usually, governance is seen as slowing issues down. However the information tells a distinct story:
71% of organizations with governance applications report excessive belief of their information. With out governance, that quantity drops considerably.
Dave reframed governance in a manner that stood out: “Governance shouldn’t be seen as friction. It’s traction.”
That’s a important mindset shift. Sturdy governance:
- Builds belief
- Allows scale
- Reduces danger
- Accelerates adoption
Andrew added, “Governance doesn’t need to be the land of no … it ought to actually get rid of the belief obstacles which have blocked individuals from saying sure to AI.”
And importantly, probably the most profitable organizations aren’t creating totally new governance buildings – they’re extending current information governance into AI.
Why? As a result of splitting governance creates fragmentation:
- Conflicting definitions of belief
- Duplicate efforts
- Inconsistent controls
Key takeaway: The quickest path to trusted AI is constructing on what already works—your information governance basis.
WEBINARThe Information Integrity & AI Discussion board: AI Pleasure vs. Enterprise Actuality
Designed for senior information and analytics leaders, this roundtable is a chance to match notes, problem assumptions, and discover what it really takes to show AI ambition into sustainable, trusted outcomes.
Information High quality Debt Is Catching Up – Quick
One other main perception from the report: 51% of information leaders say information high quality is their high precedence.
For years, organizations have carried “information high quality debt” – points that have been manageable in conventional analytics environments. However AI modifications the equation, and enhances the urgency round paying that invoice.
As Andrew described it, “AI is sort of a massive magnifying glass and a giant highlight.”
Prior to now, human analysts might spot inconsistencies, apply context, and compensate for flaws. AI doesn’t work that manner. It scales each:
- Good information → higher outcomes
- Dangerous information → amplified errors
Rabun made the stakes even clearer, saying that for the Agentic AI period specifically, “We’re going to maneuver from perception to motion … now it’s going to indicate up in precise dangerous actions which can be taken in opposition to the improper information.”
To mitigate the rising danger round dangerous information high quality, main organizations are shifting from:
- Static high quality checks → Steady monitoring
- One-time fixes → Ongoing observability
- Handbook processes → Automated controls
Key takeaway: The invoice is now due for information high quality debt. Information high quality must be repositioned from a cleanup process right into a steady working situation.
Proving AI Worth Requires Self-discipline, Not Magic
One of the crucial placing findings from the report was that:
- 71% say AI aligns with enterprise targets …
- However solely 31% have metrics tied to KPIs
There’s a transparent disconnect, and Andrew defined why:
“There’s an enchantment of AI, that it’s so transformative that it makes us suppose it modifications the principles round precision and the metrics that you just measured. And the ability of seeing that alleged magic sort of divorces us from … truly managing what you measure.”
AI definitely is transformative, however that doesn’t take away the necessity for clear success metrics, monetary accountability, and outcome-based measurement.
Dave outlined three issues that separate profitable organizations. They:
- Outline success – in enterprise outcomes – earlier than they begin
- Resist temptations to maintain issues “protected” in pilot – and transfer into manufacturing, the place worth is created
- Construct an built-in information integrity working mannequin that brings collectively information high quality, governance, context, observability, expertise, and enterprise alignment
Rabun strengthened the significance of connecting every part again to worth:
“It’s a maturity mannequin. If you happen to’re not already concerned in that mannequin of constructing that worth chain connection of shifting up information, the inference, all of these items – it’s essential be catching as much as that rapidly,” he says. “As a result of that’s the way you make it work, and that’s the way you get to the worth. You make investments on the on the foundational degree … however you then take use instances the place you’ll be able to deploy up that full worth chain.”
Key takeaway: AI success can’t simply be measured in mannequin efficiency – it’s essential outline and measure actual enterprise affect.
AI Success Begins – and Ends – with Information Integrity
As we wrapped up the dialogue, one theme stood above the remaining: trusted AI begins with trusted information.
Nevertheless it doesn’t cease there. To really shut the hole between AI ambition and execution, organizations have to:
- Transfer from experimentation to repeatability
- Deal with governance as an accelerator, not a blocker
- Deal with information high quality as an ongoing self-discipline
- Measure success in enterprise phrases
As a result of ultimately, AI must be dependable, scalable, and actionable. And that’s the place information integrity makes all of the distinction. Learn our 2026 Information Integrity & AI Readiness report for extra insights from information and analytics leaders worldwide, and listen to extra from our panel of consultants within the full webinar, The Information Integrity & AI Discussion board: AI Pleasure vs. Enterprise Actuality.
FAQs: AI Readiness and Information Integrity
What’s AI readiness?
AI readiness refers to a company’s skill to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the appropriate instruments or infrastructure and contains information high quality, governance, expertise, and a repeatable working mannequin that delivers constant enterprise outcomes.
Why do many organizations wrestle with AI readiness?
Many organizations overestimate their AI readiness as a result of sturdy enthusiasm and stress to undertake AI. Nevertheless, gaps in information high quality, governance, infrastructure, and operational processes usually forestall them from scaling past preliminary pilots into enterprise-wide deployment.
Why is information high quality essential for AI?
Information high quality is important for AI as a result of AI techniques amplify each good and dangerous information. Excessive-quality information results in extra correct and dependable outcomes, whereas poor information high quality can lead to incorrect insights or actions – particularly in automated and agentic AI use instances.
How does information governance affect AI success?
Governance permits trusted AI by guaranteeing accountability, consistency, and management over information and fashions. Organizations with sturdy governance applications report larger belief of their information and are higher positioned to scale AI initiatives with confidence.
How can organizations measure AI success?
Organizations can measure AI success by tying initiatives to enterprise outcomes resembling income affect, price financial savings, or effectivity features. Defining success metrics upfront and shifting past pilot phases into manufacturing are key to demonstrating actual ROI.
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