If there’s one factor that’s clear from each dialog I’ve had just lately – whether or not with clients, colleagues, or trade friends – it’s this: AI ambition has by no means been increased.
However ambition alone doesn’t equal readiness.
In our latest Knowledge Integrity & AI Discussion board, I had the chance to take a seat 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 Middle 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 assume they’re, and what it truly takes to succeed with AI at scale.
Let’s break down the largest takeaways.
The AI Readiness Hole Is Actual, and Rising
In accordance 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 clarify 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 assume that a few of it’s a definition drift … what you have been serious about a yr in the past with AI or what it may do may be very totally different than what you’re serious about immediately.”
In different phrases, the goalposts are transferring. What counted as “AI prepared” a yr in the past – fundamental information entry, some experimentation – is now not sufficient. Immediately, 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 stage: ‘Do we now have the infrastructure provision? Do we now have subscriptions to the suitable LLMs?’ However the true take a look at of readiness lives one flooring down from that, on the working mannequin stage.”
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
- 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 best 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 count on.
Too usually, governance is seen as slowing issues down. However the information tells a distinct story:
71% of organizations with governance packages report excessive belief of their information. With out governance, that quantity drops considerably.
Dave reframed governance in a means that stood out: “Governance shouldn’t be considered as friction. It’s traction.”
That’s a vital mindset shift. Robust governance:
- Builds belief
- Allows scale
- Reduces danger
- Accelerates adoption
Andrew added, “Governance doesn’t must 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, 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 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 adjustments the equation, and enhances the urgency round paying that invoice.
As Andrew described it, “AI is sort of a large magnifying glass and an enormous highlight.”
Up to now, human analysts may spot inconsistencies, apply context, and compensate for flaws. AI doesn’t work that means. 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 point out up in precise dangerous actions which can be taken in opposition to the fallacious information.”
To mitigate the rising danger round dangerous 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 process right into a steady working situation.
Proving AI Worth Requires Self-discipline, Not Magic
One of the hanging findings from the report was that:
- 71% say AI aligns with enterprise objectives …
- 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 assume it adjustments the principles round precision and the metrics that you just measured. And the facility of seeing that alleged magic form 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 “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 bolstered the significance of connecting all the things again to worth:
“It’s a maturity mannequin. If you happen to’re not already concerned in that mannequin of creating that worth chain connection of transferring up information, the inference, all of this stuff – you’ll want to be catching as much as that shortly,” 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 stage … however then you definitely take use instances the place you possibly can deploy up that full worth chain.”
Key takeaway: AI success can’t simply be measured in mannequin efficiency – you’ll want to outline and measure actual enterprise influence.
AI Success Begins – and Ends – with Knowledge Integrity
As we wrapped up the dialogue, one theme stood above the remaining: trusted AI begins with trusted information.
But it surely doesn’t cease there. To actually 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 in the long run, 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 potential to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the best 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 consequence of sturdy enthusiasm and stress to undertake AI. Nevertheless, gaps in information high quality, governance, infrastructure, and operational processes usually stop them from scaling past preliminary pilots into enterprise-wide deployment.
Why is information high quality essential for AI?
Knowledge high quality is vital for AI as a result of AI methods amplify each good and dangerous information. Excessive-quality information results in extra correct and dependable outcomes, whereas poor information high quality may end up in incorrect insights or actions – particularly in automated and agentic AI use instances.
How does information governance influence AI success?
Governance permits trusted AI by making certain accountability, consistency, and management over information and fashions. Organizations with sturdy governance packages 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 similar to income influence, value financial savings, or effectivity beneficial properties. Defining success metrics upfront and transferring past pilot phases into manufacturing are key to demonstrating actual ROI.
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