Director Internal AI Governance

Company Description

NIQ is seeking a Director of Internal AI Governance to establish and lead the enterprise governance framework for the responsible, secure, and value-driven use of artificial intelligence across NIQ's internal operations. Working with other departments, this role will be responsible for defining how AI is evaluated, approved, deployed, monitored, and governed across internal business functions. The Director will create the policies, decision rights, operating mechanisms, and controls required to enable AI adoption at scale while managing risk, protecting NIQ data, meeting regulatory and contractual obligations, and ensuring investments deliver measurable business value.

The role will operate at the intersection of Technology, Security, Architecture, Privacy, Legal, Risk, Finance, Procurement, HR, and business leadership. It requires a leader who can balance innovation with disciplined governance and translate rapidly evolving AI capabilities and risks into practical enterprise standards.

• Define and maintain NIQ's enterprise framework for the governance of internally used AI, including generative AI, machine learning, autonomous agents, and third-party AI-enabled products.
• Establish clear policies, standards, decision rights, approval thresholds, and accountability for AI use across the enterprise.
• Define risk-based governance requirements based on factors such as data sensitivity, business criticality, autonomy, customer or employee impact, regulatory exposure, and financial risk.
• Ensure governance remains proportionate to risk and enables responsible experimentation without creating unnecessary barriers to adoption.
• Maintain an enterprise inventory of material internal AI solutions, use cases, models, vendors, and associated owners and risks.

• Establish governance processes for evaluating proposed AI investments and internal AI opportunities.
• Partner with Finance and business leaders to ensure material AI initiatives have clearly defined business cases, measurable outcomes, full lifecycle costs, and accountable benefit owners.
• Define criteria for progressing, scaling, pausing, or stopping AI initiatives based on business value, adoption, performance, risk, and total cost of ownership.
• Help ensure NIQ prioritizes meaningful business opportunities rather than deploying AI solely because the technology is available.
• Establish governance mechanisms to identify overlapping tools, duplicative investments, and opportunities for enterprise reuse.

• Develop and operationalize NIQ's responsible AI principles and control framework for internal AI.
• Partner with Privacy, Legal, Security, Risk, Compliance, and HR to address risks including data privacy, intellectual property, cybersecurity, bias, explainability, human oversight, records retention, regulatory requirements, and inappropriate use.
• Define requirements for human review, testing, validation, monitoring, auditability, and escalation based on AI risk classification.
• Establish processes for identifying, reporting, investigating, and remediating AI-related incidents.
• Monitor emerging AI regulation, standards, and industry practices and translate them into appropriate NIQ governance requirements.

• Partner with Technology and Security leaders to establish approved AI technology patterns, platforms, models, and architectural guardrails.
• Define requirements for the use of public, private, proprietary, and open-source AI models.
• Establish governance for AI data access, model access, identity and permissions, integrations, agents, APIs, logging, monitoring, and production deployment.
• Partner with technology teams to define controls for AI development environments, experimentation, productionization, and ongoing operations.
• Support enterprise decisions regarding build-versus-buy approaches and strategic AI platforms.

• Partner with Procurement, Technology, Security, Privacy, and Legal to establish standards for evaluating AI capabilities embedded within third-party products and services.
• Define due-diligence requirements for AI vendors, including data usage, model training practices, security, intellectual property, contractual protections, service continuity, and regulatory risk.
• Help ensure the enterprise understands the full financial and operational impact of AI technology commitments, including licensing, consumption, infrastructure, integration, support, and ongoing operating costs.

• Chair or lead appropriate enterprise AI governance forums and provide clear recommendations to senior leadership.
• Establish transparent escalation paths and decision-making processes for high-risk or high-impact AI initiatives.
• Define governance metrics and reporting that provide leadership with visibility into AI adoption, value realization, risk exposure, policy compliance, and portfolio…