If there’s one factor that’s clear from each dialog I’ve had 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 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 knowledge 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 suppose they’re, and what it really takes to succeed with AI at scale.
Let’s break down the largest 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 knowledge readiness as main blockers.
So, what’s the disconnect? As Andrew Brust put it:
“It’s onerous 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 had been interested by a yr in the past with AI or what it may do may be very totally different than what you’re interested by right now.”
In different phrases, the goalposts are transferring. What counted as “AI prepared” a yr in the past – primary knowledge entry, some experimentation – is now not sufficient. At the moment, 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 now we have the infrastructure provision? Do now we 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 circumstances
- Restricted threat
- Handbook 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 precise 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 knowledge tells a distinct story:
71% of organizations with governance packages report excessive belief of their knowledge. 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 crucial mindset shift. Sturdy governance:
- Builds belief
- Permits scale
- Reduces threat
- 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, probably the most profitable organizations aren’t creating fully new governance constructions – they’re extending current knowledge 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 knowledge governance basis.
WEBINARThe Knowledge Integrity & AI Discussion board: AI Pleasure vs. Enterprise Actuality
Designed for senior knowledge and analytics leaders, this roundtable is a chance to check 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 knowledge high quality is their prime precedence.
For years, organizations have carried “knowledge high quality debt” – points that had 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 a giant highlight.”
Prior to now, human analysts may spot inconsistencies, apply context, and compensate for flaws. AI doesn’t work that means. It scales each:
- Good knowledge → higher outcomes
- Unhealthy knowledge → 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 in opposition to the incorrect knowledge.”
To mitigate the rising threat round unhealthy knowledge high quality, main organizations are transferring from:
- Static high quality checks → Steady monitoring
- One-time fixes → Ongoing observability
- Handbook processes → Automated controls
Key takeaway: The invoice is now due for knowledge 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
Probably the most placing 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 adjustments the foundations round precision and the metrics that you simply measured. And the facility of seeing that alleged magic type of divorces us from … really 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 knowledge integrity working mannequin that brings collectively knowledge high quality, governance, context, observability, abilities, and enterprise alignment
Rabun strengthened the significance of connecting the whole lot again to worth:
“It’s a maturity mannequin. In case you’re not already concerned in that mannequin of constructing that worth chain connection of transferring up knowledge, the inference, all of these items – you should 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 circumstances 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 – you should outline and measure actual enterprise impression.
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 knowledge.
However it 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 knowledge 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 knowledge integrity makes all of the distinction. Learn our 2026 Knowledge Integrity & AI Readiness report for extra insights from knowledge 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 capacity to efficiently deploy, scale, and operationalize AI initiatives. It goes past having the precise instruments or infrastructure and consists of knowledge 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 because of robust enthusiasm and stress to undertake AI. Nonetheless, gaps in knowledge high quality, governance, infrastructure, and operational processes usually forestall them from scaling past preliminary pilots into enterprise-wide deployment.
Why is knowledge high quality essential for AI?
Knowledge high quality is crucial for AI as a result of AI programs amplify each good and unhealthy knowledge. Excessive-quality knowledge results in extra correct and dependable outcomes, whereas poor knowledge high quality may end up in incorrect insights or actions – particularly in automated and agentic AI use circumstances.
How does knowledge governance impression AI success?
Governance allows trusted AI by guaranteeing accountability, consistency, and management over knowledge and fashions. Organizations with robust governance packages report increased belief of their knowledge 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 impression, value financial savings, or effectivity good points. Defining success metrics upfront and transferring past pilot phases into manufacturing are key to demonstrating actual ROI.
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