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 increased.
However ambition alone doesn’t equal readiness.
In our current Knowledge 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 Knowledge 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 Knowledge Integrity & AI Readiness report, printed by Exactly in partnership with the Heart for Utilized AI and Enterprise Analytics at Drexel College’s LeBow Faculty 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 line with the report, 87% of organizations say they’re prepared for AI. However on the similar 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 folks to say no as a result of that appears like they’re cynical about AI, and there’s a lot strain to be optimistic about it.” He went on to elucidate how there’s each exterior strain 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 had been occupied with a 12 months in the past with AI or what it might do could be very completely different than what you’re occupied with right now.”
In different phrases, the goalposts are transferring. What counted as “AI prepared” a 12 months in the past – fundamental information entry, some experimentation – is now 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 actual take a look at of readiness lives one ground 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 circumstances
- Restricted threat
- Guide oversight
However repeatability requires:
- Knowledge 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 proper 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 best way you may anticipate.
Too usually, governance is seen as slowing issues down. However the information tells a special 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 considered as friction. It’s traction.”
That’s a vital mindset shift. Sturdy governance:
- Builds belief
- Permits scale
- Reduces threat
- Accelerates adoption
Andrew added, “Governance doesn’t need to be the land of no … it ought to actually get rid of the belief boundaries which have blocked folks from saying sure to AI.”
And importantly, essentially the most profitable organizations aren’t creating totally new governance constructions – they’re extending present 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 Knowledge 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 actually takes to show AI ambition into sustainable, trusted outcomes.
Knowledge High quality Debt Is Catching Up – Quick
One other main perception from the report: 51% of knowledge leaders say information high quality is their high precedence.
For years, organizations have carried “information high quality debt” – points that had 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 an enormous highlight.”
Previously, human analysts might spot inconsistencies, apply context, and compensate for flaws. AI doesn’t work that manner. It scales each:
- Good information → higher outcomes
- Unhealthy 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 unhealthy actions which might be taken towards the improper information.”
To mitigate the rising threat round unhealthy information high quality, main organizations are transferring from:
- Static high quality checks → Steady monitoring
- One-time fixes → Ongoing observability
- Guide processes → Automated controls
Key takeaway: The invoice is now due for information high quality debt. Knowledge high quality must be repositioned from a cleanup job right into a steady working situation.
Proving AI Worth Requires Self-discipline, Not Magic
Some of the hanging 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 attraction of AI, that it’s so transformative that it makes us suppose it modifications the foundations round precision and the metrics that you simply measured. And the ability of seeing that alleged magic sort of divorces us from … truly managing what you measure.”
AI actually 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 “secure” 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 bolstered the significance of connecting all the things again to worth:
“It’s a maturity mannequin. In case you’re not already concerned in that mannequin of creating that worth chain connection of transferring up information, the inference, all of these items – it is advisable to 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 then you definately take use circumstances the place you may deploy up that full worth chain.”
Key takeaway: AI success can’t simply be measured in mannequin efficiency – it is advisable to outline and measure actual enterprise affect.
AI Success Begins – and Ends – with Knowledge Integrity
As we wrapped up the dialogue, one theme stood above the remainder: trusted AI begins with trusted information.
But it surely doesn’t cease there. To really shut the hole between AI ambition and execution, organizations must:
- Transfer from experimentation to repeatability
- Deal with governance as an accelerator, not a blocker
- Tackle 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 Knowledge 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 Knowledge Integrity & AI Discussion board: AI Pleasure vs. Enterprise Actuality.
FAQs: AI Readiness and Knowledge Integrity
What’s AI readiness?
AI readiness refers to a corporation’s skill to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the proper 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 resulting from robust enthusiasm and strain to undertake AI. Nonetheless, 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 necessary for AI?
Knowledge high quality is vital for AI as a result of AI techniques amplify each good and unhealthy 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 circumstances.
How does information governance affect AI success?
Governance permits trusted AI by guaranteeing accountability, consistency, and management over information and fashions. Organizations with robust governance applications report increased 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 akin to income affect, value financial savings, or effectivity features. Defining success metrics upfront and transferring past pilot phases into manufacturing are key to demonstrating actual ROI.
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