While the benefits of AI may dominate strategic discussions within your business, conversations around AI risk management may be too narrow. Your board may prioritize data privacy and algorithmic bias concerns, which is entirely valid, but may not see the broader risks at stake, such as operational disruption, reputational damage or regulatory non-compliance.
Many leaders may simply equate AI with chatbots or assume that truly disruptive AI capabilities are years away. Generative AI has many dimensions, including audio, image, video, and reasoning models, already embedded in business-critical systems and delivering advanced decision-making and automation today. From fraud detection in financial services, to predictive maintenance in manufacturing, to personalized customer experiences in retail, AI is already reshaping how organizations operate.
If your governance framework doesn’t account for how AI behaves under stress or uncertainty, you may be missing key vulnerabilities.
Our experience suggests three AI risk management areas your board may need to carefully consider alongside the more well-known risks:
01
AI systems excel at pattern recognition, but they can be brittle. They perform well within trained parameters but may fail unpredictably when exposed to new conditions or when the real-world environment no longer aligns with training data. This concept is called “Model drift”.
In practice, it may work like this: your team deploys an AI model that performs flawlessly at launch. Confidence grows. But when market conditions shift, the model’s accuracy drops and no one notices. Human oversight erodes through automation complacency and errors go unchecked.
That’s why fragility needs to be treated as a governance issue. AI systems that pass initial testing can still fail silently. Without disciplined oversight, those failures can escalate into operational crises.
To mitigate this, your business must introduce robust AI risk management protocols every time a new AI application is deployed and ensure these protocols cover the entire AI model lifecycle.
02
Diversification is a cornerstone of risk management. It means that if one area fails, another may still thrive, helping to smooth volatility. But in AI, diversification can be misleading when the underlying technology is the same. What looks like variety on the surface may still carry the same systemic risks underneath. You might use multiple platforms, vendors or tools, yet many rely on the same few foundational models from dominant tech providers. That creates hidden dependencies. If one core model is compromised, unavailable or flawed, the impact could be systemic.
Ask yourself: Is your AI truly diversified, or just layered on the same foundation? Understanding the provenance of your AI tools and incorporating relevant questions into your supplier due diligence process is essential to managing concentration risk.
03
Generative AI can produce polished, authoritative-sounding content on essentially any topic. Whilst this content can often be high-quality and accurate, it can also be partially or entirely incorrect while being presented confidently as verified truth. AI “hallucinations” are now a well-known risk. However, organizations should also consider the risk of phantom expertise: outputs produced largely by an AI but then incorporated into materials shared by a human. Without transparency around the use of AI, such materials can appear credible but may be subtly flawed or even entirely inaccurate.
This can undermine traditional assurance methods, such as peer review, subject matter validation or relying on senior oversight. How do you verify a market analysis written by a junior employee using AI? How do you ensure AI-generated code doesn’t contain critical errors?
Layered oversight, governance and control is critical. This means effective AI policy, tailored training and robust output assurance controls combined with strong governance. Your organization needs to define clear policies on AI usage, assign accountability for AI-enhanced outputs and implement robust quality monitoring. Your goal here isn’t to restrict innovation, it’s to manage it with discipline and transparency.
AI governance is a proactive discipline that challenges organizations to build resilience into their AI strategies at board level.
The first steps to support your board in strengthening its AI risk governance are to:
To explore how your organization can strengthen its AI governance and risk oversight, speak to our enterprise risk specialists.
