As AI evolves from isolated tools into core operational infrastructure, enterprises inherently adopt what analyst Chris Marsh describes as a “cognitive system” embedded across workflows — whether they planned it or not. This shift means that control isn’t just about managing IT resources anymore; it’s about shaping how meaning, judgement, and decision logic are encoded and enforced across business processes. In this new reality, the next big risk for organizations isn’t simply a software bug or failed automation project, but cognitive instability — where AI-driven decisions affect outcomes unpredictably unless strong governance and frameworks are in place.
Traditional IT governance focused on clear, deterministic control points like servers, databases, and human-triggered workflows. By contrast, when AI systems become infrastructure, they continuously interpret data, make inferences, and influence outcomes across enterprise functions — from customer service to supply chain execution — in real time. This requires leaders to rethink control from manual checkpoints to systemic oversight of AI behaviour itself. Companies must now ensure consistency, accuracy, and alignment with business objectives for systems that act independently rather than only when humans initiate specific tasks.
A key part of maintaining control in an AI-driven enterprise is strong governance and operational discipline. This includes defining clear boundaries for autonomous AI actions, establishing standards for data quality and context, and building guardrails that align AI behaviour with organisational policies. It also involves continuous monitoring and feedback loops to spot when AI-generated outcomes drift from expected business rules or generate unintended effects. Without such frameworks, the very systems designed to boost productivity and innovation can introduce new vulnerabilities or amplify existing ones.
Ultimately, embracing AI as infrastructure demands a strategic approach to architecture and governance — treating AI systems as foundational to the business, rather than add-on tools. This means integrating AI planning into enterprise strategy, investing in scalable infrastructure, and aligning data, security, and compliance frameworks with how AI operates in production environments. Enterprises that succeed will be those that balance autonomy with accountability, ensuring AI not only executes tasks efficiently but does so in ways that are predictable, auditable, and aligned with broader organisational goals.
