Shadow AI: The Frightening Return Of Shadow IT

Adrian Carr is CEO of global master data management provider Stibo Systems, which empowers companies through trustworthy intelligence.

​A decade or so ago, most large organizations had the same quiet, faintly humbling discovery. Someone in IT worked out how much of the business ran on siloed spreadsheets, databases only one person understood. What if that person left the company? We saw SaaS tools bought and never run through procurement, adopted department by department without anyone much noticing. Think vendor sprawl, fragmented data.

We gave this phenomenon a spooky name, shadow IT, and we spent several expensive years cleaning up after it.

Agentic AI is reading off the script but with a plot twist. I fear we are about to learn the same lesson, but more costly this time around.

Same Movie, Scarier Villain

The earlier waves of shadow IT gave way to shadow applications. This one creates something more dangerous: shadow AI.

An unmanaged spreadsheet has the decency to sit there until someone opens it. But an unmanaged agent can go fully rogue—interpret context, choose a tool, kick off a transaction, alter a record or fire off an email externally. McKinsey’s recent work on AI trust puts it well: The risk is that a system does the wrong thing and does it with total confidence.

Four things make shadow AI scarier than what we dealt with before:

• Creation is almost insultingly easy now: Anyone can take on a sophisticated workflow without understanding code or traditional software disciplines.

• Execution never really stops: An agent can keep running, calling other agents or tools, long after you forget it’s running.

• The blast radius is bigger: Permissions into email, CRM or ERP systems can turn mistakes into consequences that go live before you notice them.

• Behaviors change: Prompts, models and data all shift independently, which means your agent needs ongoing scrutiny.