Shadow AI: the real issue is what you cannot see

AI adoption is already happening inside Belgian organizations, whether there is a formal AI strategy or not.

A 2026 Belgian survey found that 68% of managers and decision-makers already work in organizations using AI. At the same time, 47% said they use AI without colleagues or management necessarily being aware, while 18% use tools or software that have not been approved by their employer.

That gap between adoption and visibility is Shadow AI.

Why Shadow AI happens

Usually not because employees are trying to bypass IT.

They find a tool that helps them summarize a document, analyze data, prepare a presentation, write code or simply work faster.

The business benefit can be real.

The problem starts when the organization cannot answer basic questions:

• Which AI tools are being used?

• What company data is being shared with them?

• Who has access?

• Which business decisions rely on AI output?

• Which tools have actually been reviewed and approved?

Without that visibility, seemingly harmless experimentation can create unnecessary exposure.

Where the real risks are

The most common risks are practical rather than theoretical:

Data exposure
Customer information, employee data, contracts, financial data or intellectual property can end up in tools that were never approved to process them.

Uncontrolled access
AI assistants and embedded AI features can gain access to documents, mailboxes, code or business systems without clear ownership or appropriate controls.

Unverified output
Incorrect AI-generated information can quietly enter reports, customer communication, software or business decisions.

Compliance and accountability
Privacy, security and AI governance obligations do not disappear because a tool was introduced outside the normal procurement process.

Tool and cost sprawl
Different teams may adopt overlapping AI services while the organization already pays for similar enterprise capabilities elsewhere.

Blocking AI does not solve Shadow AI

An outright ban often addresses the symptom rather than the cause.

If employees see real productivity benefits, usage tends to move elsewhere.

A better starting point is visibility.

Create an inventory of the tools and use cases already present. Understand the data involved. Separate low-risk productivity use from applications that require stronger controls.

Then decide what should be enabled, governed, restricted or replaced.

The objective is not less AI.

It is controlled AI adoption.

How Galactus can help

Galactus helps organizations move from assumptions about AI usage to an evidence-based view of what is actually happening.

Depending on the level of maturity and exposure, we can help through:

AI GRIP Scan
A quick first indication of your current AI governance position.

AI Exposure Audit
A focused assessment of where AI is already being used and where the most material exposures are.

Shadow AI Assessment
A deeper engagement covering discovery, data and access risks, policy, ownership, governance, employee enablement and a prioritized implementation roadmap.

The result is not another theoretical AI policy.

It is clarity on:

what is being used, what actually matters, who should own it and what should happen next.

Because effective AI governance starts with knowing what is already happening.