If there’s one factor that’s clear from each dialog I’ve had lately – whether or not with prospects, colleagues, or business friends – it’s this: AI ambition has by no means been greater.
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 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 largest 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 individuals 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 clarify how there’s each exterior strain 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 suppose that a few of it’s a definition drift … what you had been fascinated about a 12 months in the past with AI or what it may do could be very totally different than what you’re fascinated about as we speak.”
In different phrases, the goalposts are transferring. What counted as “AI prepared” a 12 months in the past – primary information entry, some experimentation – is now not sufficient. Right now, 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’ve got the infrastructure provision? Do we’ve got subscriptions to the suitable LLMs?’ However the actual take a look at 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
- 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 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 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 means that stood out: “Governance shouldn’t be considered as friction. It’s traction.”
That’s a essential mindset shift. Robust 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 fully new governance constructions – 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 information 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 large magnifying glass and a giant 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
- 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 point out up in precise dangerous actions which are 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 job right into a steady working situation.
Proving AI Worth Requires Self-discipline, Not Magic
One of the crucial 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 enchantment of AI, that it’s so transformative that it makes us suppose it modifications the foundations round precision and the metrics that you just measured. And the facility 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 all the things again to worth:
“It’s a maturity mannequin. For those who’re not already concerned in that mannequin of creating that worth chain connection of transferring up information, the inference, all of this stuff – it’s worthwhile 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 degree … however then you definitely take use instances the place you may deploy up that full worth chain.”
Key takeaway: AI success can’t simply be measured in mannequin efficiency – it’s worthwhile 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 remaining: trusted AI begins with trusted information.
Nevertheless it doesn’t cease there. To actually shut the hole between AI ambition and execution, organizations must:
- Transfer from experimentation to repeatability
- Deal with governance as an accelerator, not a blocker
- Handle 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 company’s skill to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the best instruments or infrastructure and consists of 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 on account of robust enthusiasm and strain 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 vital for AI?
Knowledge high quality is essential 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 may end up in incorrect insights or actions – particularly in automated and agentic AI use instances.
How does information governance affect AI success?
Governance allows trusted AI by making certain accountability, consistency, and management over information and fashions. Organizations with robust governance applications report greater 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 affect, price 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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