The Rise of Operational Risks without AI Governance in 2026

Most organisations are still talking about artificial intelligence (AI) risks in day-today operations as though it is something coming in the future. The reality? It is blatantly here!

For many businesses, AI is omnipresent, whether leadership teams fully realise it or not. That is why the recent direction coming out of the UAE is so important.The headlines focused on AI adoption and digital transformation. But the bigger story underneath is operational dependency. Because once AI becomes part of operational delivery, governance stops being optional. And that pressure does not stay within government. It moves directly into:

• suppliers
• software providers
• consultants;
• outsourced service providers
• technology teams
• SMEs supporting those environments.

This is the part many organisations are still underestimating.

AI governance is no longer just about having an “AI policy”. It is becoming part of operational maturity in the same way cybersecurity, privacy and financial controls became business-critical over time.

The most common AI tools already running inside businesses today

Most organisations already have AI somewhere in the business today:

• AI meeting note tools
• document summarisation
• workflow automation
• AI-supported recruitment
• customer service tools
• marketing content generation
• reporting tools
• AI quietly embedded inside existing software platforms.

The problem is that many organisations adopted these tools informally. This is what is now commonly referred to as Shadow AI. According to IBM, it simply means employees using AI tools or applications without the knowledge, approval, or supervision of their organisation’s IT department.

Usually, it starts with:
“We are just improving efficiency.” “We are saving time.” “Everyone else is using it.”

While these tools can improve efficiency and streamline tasks, their unauthorised use can create serious risks related to data security, regulatory compliance, and reputational damage for the business.

AI use case registry: six questions to ask your team

• What AI tools are actually being used?
• What data is being uploaded?
• Has anybody assessed the risks?
• Is anyone checking the outputs properly?
• Could confidential information be exposed?
• Who approved the tool?
• What happens if the AI gets it wrong?

This a simple exercise to gain clarity where operational risk starts creeping in quietly and as data protection officer I have often noticed these real life examples.

What recent AI incidents show us

A recently discussed incident involved an AI-supported coding tool carrying out a destructive action inside a live production environment after attempting to resolve a technical issue autonomously. The issue was recoverable. But the wider lesson was important.

Organisations are increasingly connecting AI-enabled systems into environments capable of making real operational changes to systems, infrastructure and data. That changes the risk conversation significantly.

Why AI governance fails: the five most common operational gaps

The issue is often not the AI itself. It is the lack of safeguards and employees lack of education around it:

• excessive permissions;
• weak approval controls;
• poor separation between testing and live systems;
• lack of human confirmation for irreversible actions; and
• organisations deploying AI faster than governance frameworks are evolving around them.

And this is exactly why regulators globally are moving so quickly on AI governance. The law is no longer theoretical.

AI compliance obligations in the UK and UAE

The EU AI Act entered into force on August 1st, 2024. Certain prohibited AI practices and AI literacy obligations became applicable from February 2nd, 2025. Further transparency and high-risk AI obligations begin applying from August 2nd, 2026, with some sector-specific implementation periods extending into 2027 and 2028.

At the same time:

• UAE PDPL obligations already apply where AI systems process personal data;
• UK GDPR and the Data Protection Act 2018 already apply to AI-driven processing and decision-making; and
• organisations operating internationally are increasingly being expected to demonstrate accountability, oversight, transparency and governance around AI use.

A surprising number of businesses still think AI regulation is years away. Organisations are already subject to privacy, cybersecurity and governance obligations the moment AI starts processing personal data, influencing decisions, monitoring individuals or generating outputs relied upon operationally. This is not limited to large technology companies.

Identifying risks in daily AI-Uses across different departments

I am currently helping several organisations work through both current and planned AI use cases from a practical operational and compliance perspective. Interestingly, most of the conversations are not about “advanced AI”. They are about very normal operational situations organisations will recognise immediately:

• HR teams wanting to use AI to screen CVs or summarise interviews
• marketing teams using AI-generated content tools
• customer service teams implementing AI chat functionality
• employees uploading meeting notes or client documents into public AI tools
• operational teams automating reporting workflows
• leadership teams wanting AI insights from business data
• organisations exploring AI productivity tools without clear governance around them yet.

Most organisations are not intentionally “doing AI badly”. They are simply moving faster than their governance structures were originally designed for. One of the biggest misconceptions I still see is organisations treating AI as purely a technology issue.

When really, it touches almost every operational area:

• HR
• legal
• compliance
• procurement
• cybersecurity
• marketing
• investigations
• finance
• leadership decision-making.

Which means governance cannot sit with IT alone. The good news is that practical AI governance usually starts much simpler than people expect.

Building an AI governance framework

The organisations handling this best are generally doing a few key things well:

1. Identifying what AI is already being used internally.
2. Defining approved and prohibited use cases.
3. Setting rules around what data can and cannot be entered into AI tools.
4. Introducing human review requirements.
5. Assessing higher-risk use cases properly before deployment.
6. Training employees practically, rather than assuming “common sense” is enough.
7. Building accountability into procurement and supplier management.

And importantly, this does not need to become heavy bureaucracy.

The most effective governance models are usually the practical ones:

• guidance employees can actually follow
• proportionate controls
• visibility over operational risk; and
• governance that supports innovation rather than blocking it entirely.

There is also a wider human point that sometimes gets lost in the AI discussion. The organisations gaining the most value from AI are usually not the ones trying to replace human thinking completely. They are the ones using AI to support human capability. That difference matters. Whilst AI can accelerate tasks and improve efficiency, it still cannot replace accountability, judgement, context or professional experience. Regulators are making that expectation increasingly clear globally.

Human oversight is becoming one of the most important operational controls organisations can demonstrate.

A simple operational AI governance checklist

• Do we know which AI tools employees are already using?
• Have we defined approved and prohibited AI use cases?
• Are staff uploading confidential or personal data into public AI tools?
• Have we completed privacy or risk assessments where required?
• Is there meaningful human review of AI-generated outputs?
• Have employees received practical AI awareness training?
• Do procurement teams assess AI vendors properly?
• Can decisions made using AI be explained or challenged?
• Are we monitoring for inaccurate, biased or inappropriate outputs?
• Do we have leadership oversight of AI governance and risk?

TenIntelligence Thoughts

The organisations are most likely to struggle over the next few years are probably not the businesses “behind” on AI. They are more likely to be the organisations that adopted AI quickly without understanding:

• where it sits operationally;
• what risks it introduces; or
• how to govern it properly once it becomes business-critical.

The conversation has moved beyond: “Should we use AI?”

The real question now is: Can your organisation govern AI properly before AI becomes embedded into the way your organisation operates?