Treating AI Like IT Is One of the Biggest Governance Mistakes Businesses Are Making

Article to be attributed to Blair Hasforth – Country Manager ANZ at OneTrust

Businesses are rapidly adding AI to customer service, marketing, fraud detection, and workplace productivity. However, many are trying to manage AI governance risk using controls designed for traditional IT.

That approach creates a problem. AI doesn’t behave like conventional software, and its risks can keep changing after deployment.

As organisations integrate AI into business-critical functions, governance frameworks are struggling to keep pace. Many of those frameworks were built for traditional technologies and manual decision-making.

The result is a widening gap between AI deployment and oversight. At the centre of that gap is a basic misunderstanding of how AI differs from conventional software.

AI Governance Risk Differs From Traditional Software Risk

One of the biggest misconceptions about AI is that organisations can govern it like traditional software.

Traditional systems follow defined rules and generally produce predictable outcomes. When something goes wrong, teams can often trace the problem to a coding error or system fault.

AI works differently. Training data, prompts, user behaviour, and evolving models can all shape its output. As a result, outcomes can be less predictable and harder to explain or control.

More importantly, some AI risks can emerge even when the technology works as designed.

Hallucinations, bias, and inaccurate recommendations are not necessarily conventional system failures. They can result from the way AI models generate responses.

That difference makes continuous oversight important. A simple “set and forget” approach to governance does not address how AI systems behave over time.

Recent cases demonstrate the distinction. A major airline was held accountable after its chatbot provided incorrect information about bereavement fares. Meanwhile, law firms have faced scrutiny for submitting AI-generated court filings containing fictitious case citations.

In those cases, the technology functioned as intended. The failure instead involved the governance and oversight surrounding its use.

These incidents highlight an important shift for organisations. The question is no longer simply whether AI works. Businesses also need to determine whether their governance frameworks can manage the risks when it does.

Too often, organisations describe these incidents as AI “not working”. However, the technology may be functioning as designed while exposing weaknesses in how people deploy, monitor and oversee it.

The Biggest AI Risks Can Emerge After Deployment

Many organisations still treat governance as a box-ticking exercise completed before launch. AI makes that approach increasingly difficult.

Models drift. User behaviour changes. Datasets evolve. Third-party providers also update the systems that businesses depend on.

Consequently, new risks can emerge months after implementation through ordinary use rather than during initial testing.

Rapid updates from major AI providers add another governance challenge. A new model release can change outputs, capabilities and risk profiles overnight.

For organisations using third-party models, those changes can create compliance, privacy and reputational risks. Businesses may also have limited visibility into how an underlying model change affects their own AI-enabled services.

Therefore, one-off assessments are not enough. Governance needs to cover the full lifecycle of an AI system, including deployment, monitoring and ongoing review.

AI Governance Risk Requires Continuous Oversight

Effective AI governance requires organisations to understand how their systems operate in practice.

That includes knowing how employees and customers use AI, what data systems can access, and who remains accountable for their outputs. Organisations also need to assess whether those outputs continue to meet legal, ethical and business expectations.

Clear ownership matters. Legal, risk, privacy, security and business teams need coordinated oversight rather than separate governance processes.

Organisations also need continuous performance monitoring, regular bias reviews and clear accountability throughout the AI lifecycle.

Regulators are increasingly looking beyond whether organisations use AI and focusing on how they govern it. Across jurisdictions, emerging AI regulations and guidance place greater emphasis on accountability, transparency and ongoing oversight.

As a result, AI governance is becoming both a risk and compliance issue.

Strong governance can also affect trust. Customers, regulators and investors increasingly expect organisations to explain how they use AI and who takes responsibility for its outcomes.

Businesses that can demonstrate clear oversight and accountability may therefore be better positioned to scale AI while managing its risks.

The central issue is straightforward: AI risk cannot be managed in the same way as traditional IT risk.

AI is not simply another piece of IT infrastructure. It is a dynamic technology that requires continuous governance rather than static controls.

As AI adoption accelerates, the gap between deployment and oversight is becoming a significant governance challenge. Organisations that recognise AI as an ongoing accountability issue, rather than a one-time technology implementation, will be better placed to manage that risk.