A couple of days ago, I was scrolling through my LinkedIn feed and a post by my friend and fellow tech-watcher Dion Hinchcliffe caught my eye. For those who don’t know Dion, he’s a principal analyst at The Futurum Group and someone I’ve always found to be both thoughtful and ahead of the curve when it comes to enterprise tech trends. Dion was highlighting a recent piece in Fortune that summarized an MIT study making the rounds in the IT world
Now, the big headline from that MIT research — the one you’ve probably seen echoing through every tech newsletter and analyst webinar — is how the majority of GenAI projects aren’t really moving the needle on the bottom line. The implication: AI is the sizzle, but where’s the steak? But Dion zeroed in on a far less-hyped but arguably more important finding: The stealthy, widespread and surprisingly effective phenomenon known as “Shadow AI.”
Frankly, what Dion pointed out isn’t just a blip or some niche developer rebellion. It’s a force, and if you’re in enterprise IT or management, it’s coming for you — whether you’re ready or not.
What is Shadow AI?
Let’s start with some basics. Shadow AI refers to artificial intelligence applications, tools, or platforms that are built, adopted, or operated outside official IT oversight. This isn’t your sanctioned, board-approved, security-reviewed AI project. It’s the developer using ChatGPT to automate data labeling on the sly. It’s a marketing team spinning up a custom chatbot with an LLM on their own budget, blissfully ignoring the AI policy the CIO announced last quarter. It’s operations folks, crowdsourcing prompts from Reddit to squeeze out some process improvements without ever filing a ticket with IT.
According to that Fortune article, Shadow AI isn’t just a fringe activity. MIT’s study found that “nearly 62% of GenAI usage in large organizations is occurring outside formal channels”. Even more shocking: These “rogue” projects outperform officially sanctioned AI initiatives by 19% in terms of measurable productivity gains. Successful Shadow AI projects often spread virally within teams, with Fortune reporting that “up to 40% of departmental innovation attributed to AI is actually coming from these unsanctioned, unofficial efforts”.
So, while the boardrooms wring their hands about ROI, the real GenAI action is happening in the shadows — where business needs, not bureaucratic signoffs, drive adoption
Shadow IT: History Rhymes
If all of this sounds familiar, it should. Shadow IT is as old as the notion of a centralized IT department. I’ve seen this movie before — several times. Each time it’s the same plot, just with a different cast and technology.
I’m reminded of the early days of cloud computing. Back then, it wasn’t uncommon for a dev team to whip out a credit card and spin up AWS instances before IT could finish its risk assessment paperwork. Cloud was the new shadow. It was fast, cheap, and, best of all (for the users), flew under the radar. IT was left scrambling to “regain control,” but honestly, it was a losing battle from day one.
Then came open source. I remember when bringing open source libraries into the enterprise was tantamount to smuggling contraband. If IT security or procurement caught wind, you could expect a deluge of memos and mandatory review boards. But did that stop teams from sneaking in Apache, MySQL, or whatever the latest hot tool was at the time? Not a chance. They did it because open source worked for them, bureaucracy be damned.
And here’s a personal favorite: Early 2000s on U.S. Army bases. I recall an information assurance officer (IAO) assuring me that the Army had zero WiFi security concerns because, in his words, “We don’t use WiFi on this base.” All the while, right under our noses, troops and contractors were literally unplugging their access points and hiding them under desks as the IAO walked by — only to plug them back in once the coast was clear. You can’t make this stuff up.
The pattern here is predictable. If the “official” route is too slow, restrictive, or disconnected from daily reality, people will find a workaround. And as the Borg famously said, “Resistance is futile.” Try to crush Shadow IT or Shadow AI, and it slips through your fingers, popping up somewhere else, because it solves actual, real-world problems for real users.
Why Shadow AI Works (and What IT Should Do About It)
Here’s the uncomfortable truth for IT leaders and execs: People don’t do Shadow AI to be subversive or reckless. They do it because it works. The MIT/Fortune numbers bear this out, but so does history. Shadow cloud happened because it let people get things done. Shadow open source happened because it sped up delivery and improved quality. Shadow WiFi happened because, well, who wants to work in a Faraday cage?
What should you do if you’re in IT leadership? First, stop banging the table for more prohibitions, access controls, and “AI amnesty” forms. You can’t police your way out of Shadow AI. You never could with the cloud, open source, or WiFi, and you certainly won’t with generative AI. Instead, lean in. Encourage teams to share their Shadow AI wins — safely and openly. Learn what’s actually working on the ground. Bring the best practices and innovations out of the shadows. Make them part of the organizational DNA. Use Shadow AI as a “tribal knowledge base” — a proving ground for what’s possible when process meets passion.
Does this mean turn a blind eye to risk and governance? Of course not. But it does mean recognizing that the most valuable AI innovations might be the ones IT didn’t approve — at least not at first.
The Reality Check: Shadow AI’s Limitations
Let’s be clear: Shadow AI is not a panacea. The same thing that makes it so vibrant and useful — its grassroots, bottom-up adoption — can also be its Achilles’ heel. Shadow AI projects tend to thrive in the hands of savvy individuals and small, tight-knit teams. Scaling that up to hundreds or thousands of users, integrating with enterprise data and staying compliant with regulatory and security requirements? That’s an order of magnitude harder.
Just like those early AWS instances that worked perfectly for a handful of devs, but became a nightmare when Finance discovered thousands of dollars in untracked spending, Shadow AI can hit a wall when organizations try to “lift it out of the shadows and into the light.” That’s when issues of data privacy, maintainability, supportability and enterprise governance become real.
So yes, resistance is futile, but reckless adoption is, too. The trick — the eternal balancing act for IT — is to harness the energy, ingenuity and proof points from Shadow AI, and use them to inform, not undermine, your enterprise strategy.
In Conclusion: Don’t Fear the Shado
Shadow AI isn’t a threat to be eradicated. It’s a force to be understood. History tells us that the future of IT is built on yesterday’s shadow technologies — once feared, then embraced. The smart path is not to resist, but to listen, learn, and, when the time is right, turn that shadow into the next big thing for your enterprise
As for me? I’m off to check my desk for any unplugged access points. Some habits die hard.
