Artificial intelligence has moved faster into boardrooms than most previous technologies. In a matter of months, AI systems have been embedded into customer service, credit assessment, fraud detection, HR screening, software development and strategic analysis. For many organisations, the narrative is one of rapid adoption and visible productivity gains. Yet beneath this enthusiasm sits a widening governance gap: companies are using AI extensively without truly controlling it.
The distinction matters. Using AI is an operational choice. Controlling AI is a governance responsibility.
Across industries, AI deployment is increasingly decentralised. Business units experiment with models, vendors plug AI into platforms and employees rely on generative tools to accelerate everyday work. In many cases, these systems operate with limited visibility at the enterprise level. The result is not a single “AI risk”, but a portfolio of model risks, each with implications for accuracy, bias, explainability, data integrity and regulatory exposure.
Model risk is the most immediate concern. AI systems, particularly those based on machine learning and large language models, do not behave like traditional rule-based software. They learn from data, adapt over time and can drift in ways that are difficult to detect without structured monitoring. An AI model that performs well in testing may degrade quietly in production, producing biased outcomes, incorrect recommendations or misleading analysis. When such outputs inform credit decisions, pricing, hiring or compliance judgements, the risk becomes material.
Yet many organisations lack even a basic inventory of the AI models they are running. There is often no central register that answers simple questions: Where is AI being used? For what purpose? With what data? Under whose ownership? Without this visibility, meaningful oversight is impossible. You cannot govern what you cannot see.
Accountability is the second fault line. In traditional risk frameworks, ownership is clear: products have owners, controls have owners, decisions have accountable executives. AI blurs these lines. Is accountability with the business unit that uses the model, the technology team that deploys it, the vendor that built it, or the data scientists who tuned it? In practice, it is often shared and therefore diluted.
This diffusion of responsibility becomes problematic when something goes wrong. When an AI-driven decision triggers regulatory scrutiny or reputational damage, organisations frequently discover that escalation paths are unclear. The absence of defined accountability transforms technical issues into governance failures.
Auditability is the third pillar where gaps are most visible. Boards and regulators increasingly expect organisations to explain how automated decisions are made. Yet many AI systems operate as black boxes, with limited documentation of training data, assumptions, limitations or decision logic. Traditional audit trails: who approved what, based on which inputs, are difficult to reconstruct after the fact.
This is not a theoretical concern. As AI systems influence more high-stakes decisions, explainability becomes essential not only for regulators, but for internal confidence. Executives need to trust that AI-driven insights are robust. Risk teams need evidence that controls are operating effectively. Without traceable audit trails, trust erodes.
These challenges point to a broader issue: most boards have not yet articulated a minimum viable governance framework for AI. This does not mean creating heavyweight bureaucracy or slowing innovation. It means establishing a baseline set of principles and controls that scale with use.
At a minimum, AI governance at board level should rest on four foundations. First, visibility: a consolidated view of all material AI use cases, models and vendors across the enterprise. Second, ownership: clear executive accountability for AI risk, often anchored with a senior leader who can bridge business, technology and risk. Third, control: defined standards for model testing, monitoring, data quality and lifecycle management, proportionate to risk. Fourth, assurance: regular reporting to the board on AI performance, incidents, regulatory exposure and emerging risks.
Crucially, AI governance should be integrated into existing risk frameworks rather than treated as a standalone technical issue. Model risk, operational risk, conduct risk, cyber risk and third-party risk all intersect with AI. Organisations that silo AI governance within IT or innovation teams risk missing these interdependencies.
The most forward-looking boards are beginning to ask different questions. Not “where can we use AI?”, but “where should we not use AI without additional controls?” Not “does this model work?”, but “what happens when it fails?” Not “is this compliant today?”, but “how do we demonstrate compliance tomorrow?”
The governance gap will not close on its own. As regulators sharpen expectations and stakeholders demand greater accountability, the cost of uncontrolled AI will rise. The winners will not be those who deploy AI the fastest, but those who govern it the most deliberately.
In the AI era, competitive advantage will belong to organisations that understand a simple truth: using AI creates efficiency, but controlling AI creates trust.
