Every pillar of AI governance ultimately depends on people — to set strategy, enforce accountability, ensure data quality, manage deployments, uphold ethics, and secure infrastructure. But in most organisations in Oman, the people dimension of AI governance receives the least structured attention.
AI talent strategies, where they exist, focus on hiring data scientists or sending staff to technical workshops. Risk management, where it exists, follows traditional enterprise risk frameworks that were not designed for the unique characteristics of AI systems. The result is an organisation that may have capable individuals but lacks the structured workforce planning, competency frameworks, and AI-specific risk practices that governance requires.
Enthusiasm Without Structure
The typical pattern looks like this: an organisation identifies a need for AI capability and responds by hiring a small technical team or engaging a vendor. The team builds and deploys models. But the broader organisation — the business units that use AI outputs, the compliance team that must govern them, the leadership that must make decisions about AI investments — remains untrained in AI fundamentals and unable to participate meaningfully in governance. Meanwhile, risk management treats AI systems the same way it treats any other IT project: a line item in the enterprise risk register, assessed annually, with generic controls.
This is what the 7-Pillar AI Governance Model calls a "Level 1 — Ad Hoc" state in Pillar 7 (Talent & Risk): AI capability exists in pockets, but governance competency and AI-specific risk management do not.
What Mature Talent and Risk Governance Looks Like
A mature approach to this pillar establishes four interconnected capabilities. First, an AI competency framework — a documented mapping of what roles across the organisation need to know about AI, from technical practitioners who need deep expertise to executives who need governance literacy to frontline staff who need to understand how AI affects their work. Training is role-specific, not one-size-fits-all. Second, workforce planning for AI — the organisation assesses its current AI talent against future needs, identifies gaps, and builds a pipeline through hiring, upskilling, partnerships with academic institutions, and knowledge transfer from vendors. This includes succession planning for critical AI governance roles. Third, an AI-specific risk framework — risks unique to AI systems (model drift, training data bias, adversarial attack, regulatory change, vendor lock-in, ethical harm) are identified, assessed, and managed through a dedicated framework that integrates with — but is not subsumed by — the enterprise risk register. Risk assessments are conducted per AI system, not per department. And fourth, a culture of governance awareness — AI governance is not the responsibility of a single team. It is embedded in organisational culture through regular training, clear communication of policies, visible leadership commitment, and feedback mechanisms that allow staff to raise concerns about AI systems without fear of reprisal.
The National Dimension
Oman's National AI Strategy 2025–2030 sets a target of 50,000 Omanis trained in AI by 2030, alongside 30,000 AI-related jobs. These are ambitious and necessary goals. But training volume alone does not produce governance capability. The organisations that will succeed are those that align internal talent development with the national strategy while also building the governance skills — risk assessment, ethical reasoning, regulatory interpretation, audit and assurance — that transform AI practitioners into AI-governed practitioners. The MTCIT 2025 General Policy reinforces this by requiring that organisations deploying AI in public services demonstrate not just technical capability but human oversight competency.
The Cost of Waiting
Organisations that neglect talent governance and AI-specific risk management face three compounding challenges. Talent flight and dependency: without career pathways, competency frameworks, and meaningful governance roles, skilled AI professionals leave for organisations that offer them. Those who stay become single points of failure. Unmanaged risk accumulation: every AI system deployed without a proper risk assessment adds to an invisible portfolio of unquantified exposure. When a risk materialises — a biased decision, a security breach, a regulatory finding — the organisation discovers the accumulated deficit all at once. And governance theatre: organisations that train staff in AI tools but not in AI governance create a workforce that can build and deploy AI but cannot govern it. The appearance of capability masks the absence of control.
The seventh pillar of the 7-Pillar AI Governance Model exists because governance is ultimately a human capability, not a technical one. Technology provides the tools. People provide the judgement. And risk management ensures that judgement is informed, structured, and continuously updated. This pillar closes the loop: strategy defines direction, accountability assigns ownership, intelligence ensures data quality, deployment governs operations, ethics protects people, infrastructure secures the foundation, and talent and risk ensure the organisation has the people and the discipline to sustain it all.
This article completes the foundational series on the 7-Pillar AI Governance Model™. For implementation guidance, see our Practitioner Guides.