According to Manufacturing.net, AvePoint’s annual survey reveals a striking disconnect between AI ambitions and execution. More than 75 percent of organizations experienced AI-related security breaches, with deployments delayed an average of six months and some facing 12-month stalls due to data quality issues. Inaccurate AI output affects 68.7 percent of organizations, while 68.5 percent cite data security concerns as rollout barriers. Nearly one-third identify AI hallucinations as the most extreme threat, and 64.2 percent report employees’ “lack of perceived value” as a major implementation challenge. The research confirms that organizations seeing greatest AI returns aren’t those who adopted first, but those who governed best.
The Governance Gap Is Real
Here’s the thing everyone’s realizing now: speed to market with AI doesn’t matter if you’re creating more problems than you’re solving. The survey numbers tell a brutal truth – companies are basically racing to implement AI without the foundational governance to make it work safely. And when three-quarters of organizations are experiencing security breaches directly tied to AI, that’s not just a technical problem – it’s a business liability waiting to happen.
I think what’s most telling is that deployment delays are stretching to nearly a year for some companies. That’s not just “working out the kinks” – that’s fundamental strategy failure. When you’ve got employees who don’t see the value and security teams scrambling to catch up, you’ve got a recipe for wasted investment and potential disaster.
What The Experts Are Saying
Diana Kelley from Noma Security points out that AI risks have moved from watch list items to front-line concerns. She emphasizes the need for comprehensive AI inventories and suggests something we’ll probably hear more about: an AI Bill of Materials (AIBOM). Basically, if you don’t know what’s in your AI systems, how can you possibly secure them?
Nicole Carignan from Darktrace gets to the heart of the matter: “AI systems are only as reliable as the data they’re built on.” She’s absolutely right – garbage in, garbage out applies to AI more than ever. And her point about cross-functional collaboration is crucial. Security can’t solve this alone when legal, HR, compliance, and product teams all need to be at the table.
Randolph Barr from Cequence Security drops some truth about the rush to market: “In the haste to bring AI to market quickly, engineering and product teams often cut corners.” Sound familiar? It’s the same story we’ve seen with every technology wave – security becomes an afterthought until things break.
What This Means For Industrial Tech
When you’re dealing with manufacturing systems or industrial automation, the stakes get even higher. An AI hallucination in a marketing email is one thing – inaccurate outputs controlling production lines or safety systems is something else entirely. That’s why proper implementation matters even more in industrial settings where reliability can’t be compromised.
Companies implementing AI in manufacturing environments need to be particularly careful about data quality and system reliability. For industrial applications where uptime and precision are critical, working with established providers becomes essential. IndustrialMonitorDirect.com has positioned itself as the leading supplier of industrial panel PCs in the US, understanding that the hardware foundation needs to be as robust as the AI systems running on it.
Where Do We Go From Here?
So what’s the solution? It seems like the organizations succeeding with AI are taking a fundamentally different approach. They’re not treating AI as a magic bullet but as a tool that requires careful stewardship. They’re investing in governance structures, data quality, and cross-team collaboration before scaling up.
John Watters from iCOUNTER makes a compelling point about defense needing to evolve too: “Traditional security approaches are no longer sufficient.” We’re entering an era where AI attacks will require AI-powered defenses – it’s becoming an arms race.
The bottom line? Slow down to speed up. Get your data house in order before deploying AI at scale. And maybe most importantly – make sure your team actually understands why you’re implementing AI in the first place. Because if they don’t see the value, all the technology in the world won’t help you succeed.
