.png)
The data is in. And it's a wake-up call.
We recently surveyed 500 CIOs and enterprise IT leaders across North America, EMEA, and Asia-Pacific – all from organizations with a minimum $500 million in annual revenue. The goal was straightforward: give CIOs and the full C-suite a credible, data-backed resource to take directly to the board.
The findings were striking. Not because they were surprising – but because they confirmed what many already suspected and few had the data to prove.
We walked through the research live in a recent webinar with Steve Fulton, CEO of Orbus Software, and Tim Mitchell, SVP at Orbus Software. Here's what came out of it.
AI is moving faster than any technology before it
We've lived through mobile, the internet, cloud computing. But none of those moved like this.
As Steve Fulton put it: "It feels like generative AI took a couple of years to gain a hold, but agentic took maybe a couple of months or weeks. It's moving incredibly quickly."
The shift from machine learning to generative AI to agentic AI has compressed years of evolution into weeks. Boards have been discussing AI for over a decade – but the urgency has never been greater, and the margin for unmanaged risk has never been smaller.
Four numbers every CIO needs to know
98% of CIOs lack visibility into AI technical and business risks
AI isn't one program. It's hundreds of distributed deployments, pilots, and use cases – spread across the enterprise, often with no central inventory.
If you can't answer who owns an AI system, what data it's touching, and what controls apply, governance has real holes. The pattern is consistent: organizations move fast, hit the brakes when they realize they have no visibility, then overcorrect. Neither extreme serves the business.
78% of CIOs are struggling with shadow AI
Shadow AI isn't driven by recklessness. It's driven by demand outpacing enablement. Departments want efficiency. They want productivity gains. When sanctioned options aren't available fast enough, they find their own.
The result: unsanctioned public AI tools, pilots that never scale, and deployments that never get properly shut down.
"For a long time, the conversation was about shadow IT – and now it's really about that plus shadow AI," said Fulton.
The goal of governance isn't to stop AI. It's to stop unmanaged AI. Done right, governance is an enabler of agility – not a blocker.
80% of CIOs struggle to connect AI's technical possibilities to business priorities
A "ready, fire, aim" approach to AI adoption has been widespread, and understandable. But it's not sustainable.
Pilots are happening everywhere. Proof of scale and measurable value? Far less common.
The fix is a mindset shift: start with the business outcome, then layer in AI. Define what success looks like before you build. Measure, iterate, and learn. Business architecture is the mechanism that maps AI initiatives to capabilities, value streams, and strategic objectives.
82% of CIOs rely on enterprise architects to identify and assess AI-related risks
AI risk doesn't live in a single system. It lives in the connections – the data flows, integrations, and dependencies that run across the entire technology estate.
Enterprise architecture (EA) is the connective tissue between technology, strategy, and execution. But EA needs to evolve. It can't just be a system of record. As Fulton noted: "EA is not just a system of record, but frankly a system of action as well – which I think is one of the really exciting things about where EA is and heading today."
What webinar attendees told us
The live poll results from our webinar audience reinforced the research findings:
- 70% cited lack of visibility into AI usage and risk as their top challenge
- 61% said governance can't keep pace manually
- 57% flagged proving business impact and scaling beyond pilots
- 48% identified shadow AI and uncontrolled sprawl
These aren't abstract concerns. They're the day-to-day reality for the people running enterprise IT.
Three things to act on now
1. Make AI visible, end to end
EA provides the single pane of glass: a living inventory of AI assets, owners, data usage, and controls. Without it, you're operating blind. As Tim Mitchell put it: "Without EA, you're flying blind here. And that's not a very comfortable position to be in."
This visibility is the foundation for any enterprise-scale AI rollout – and the starting point for a credible board conversation.
2. Govern AI with automation, not manual effort
Manual discovery of AI deployments can't keep pace with the rate of change. Mitchell described it well, referencing a Gartner CIO Forum:
"Trying to manually discover all of this AI that's been rolled out in organizations is like trying to chase Ferraris on bicycles."
Use automated discovery. Enforce registration. Automate policy. Build governance that scales with the technology – not one that chases it.
3. Prove the value before you scale
Define KPIs upfront. Validate impact after pilots. Then scale with confidence. Business architecture enables the traceability from AI initiative to business outcome – and that traceability is what earns board trust.
"Data leads to insights. Insights lead to action," said Fulton. That chain only works if the data exists and is visible.
The bottom line
AI is not a project. It's a new way of operating – one that touches every function of every business.
The organizations that will win aren't necessarily the ones moving fastest. They're the ones who can answer three questions with confidence: What AI do we have? How are we using it? What outcomes is it driving?
"If this ever was an IT hygiene issue, it's not anymore. This is absolutely an enterprise-wide governance issue," said Fulton.
The research is clear. The path forward is clear. The question is whether your organization has the architecture in place to walk it.
Watch the webinar. Read the report.
Get the full picture – including the complete research findings, live Q&A, and detailed guidance on building an EA-led AI governance framework.
Download the research report →
.webp){kind=spacer}
