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, revealed 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 really 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, abilities, 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 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 totally 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 enthusiastic about a yr in the past with AI or what it may do could be very completely different than what you’re enthusiastic about immediately.”
In different phrases, the goalposts are transferring. What counted as “AI prepared” a yr in the past – primary information entry, some experimentation – is not sufficient. As we speak, 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 have now the infrastructure provision? Do we have now subscriptions to the suitable LLMs?’ However the actual check 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 typically 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 fitting 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 typically, governance is seen as slowing issues down. However the information tells a unique 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 approach that stood out: “Governance shouldn’t be considered as friction. It’s traction.”
That’s a crucial mindset shift. Robust governance:
- Builds belief
- Permits scale
- Reduces danger
- Accelerates adoption
Andrew added, “Governance doesn’t should 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 fully 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 Knowledge Integrity & AI Discussion board: AI Pleasure vs. Enterprise Actuality
Designed for senior information and analytics leaders, this roundtable is a chance to check notes, problem assumptions, and discover what it really 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 prime 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 huge 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 approach. It scales each:
- Good information → higher outcomes
- Unhealthy information → amplified errors
Rabun made the stakes even clearer, saying that for the Agentic AI period particularly, “We’re going to maneuver from perception to motion … now it’s going to indicate up in precise unhealthy actions which are taken towards the mistaken information.”
To mitigate the rising danger 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 process right into a steady working situation.
Proving AI Worth Requires Self-discipline, Not Magic
Probably the most putting 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 enchantment of AI, that it’s so transformative that it makes us assume it modifications the principles round precision and the metrics that you just measured. And the facility of seeing that alleged magic type of divorces us from … really 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, abilities, and enterprise alignment
Rabun bolstered the significance of connecting the whole lot again to worth:
“It’s a maturity mannequin. For those who’re not already concerned in that mannequin of constructing that worth chain connection of transferring up information, the inference, all of this stuff – you must 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 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 must 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 remaining: trusted AI begins with trusted information.
However it 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
- Tackle 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 specialists 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 company’s means to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the fitting instruments or infrastructure and consists of information high quality, governance, abilities, and a repeatable working mannequin that delivers constant enterprise outcomes.
Why do many organizations battle with AI readiness?
Many organizations overestimate their AI readiness resulting from sturdy enthusiasm and stress to undertake AI. Nevertheless, gaps in information high quality, governance, infrastructure, and operational processes typically stop them from scaling past preliminary pilots into enterprise-wide deployment.
Why is information high quality essential for AI?
Knowledge high quality is crucial for AI as a result of AI methods 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 instances.
How does information governance affect AI success?
Governance permits trusted AI by making certain accountability, consistency, and management over information and fashions. Organizations with sturdy 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 reminiscent of income affect, value financial savings, or effectivity positive aspects. Defining success metrics upfront and transferring past pilot phases into manufacturing are key to demonstrating actual ROI.
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