Expert: Enterprise AI Lacks Strategy and Risk Frameworks
AI ethics expert Rose Genele warns that most companies are stuck in early AI experimentation with basic policies. She argues they haven't connected AI efforts to business strategy or updated their risk frameworks, creating a significant governance gap as adoption scales.
The disconnect between AI experimentation and enterprise-wide strategy is widening. While 78% of organizations now use AI in at least one business function, fewer than 25% have successfully deployed it at scale to achieve measurable business outcomes. This gap highlights a tendency to fund isolated pilot projects rather than developing a comprehensive vision and governance framework. For product leaders, particularly in sensitive domains like HR and compensation, this lack of strategic alignment creates significant risk. In the HR sector, AI tools are increasingly used for everything from recruitment to performance management, yet they carry the risk of amplifying historical biases in data related to hiring, pay, and promotions. An AI hiring tool at one major tech company, for instance, was found to systematically favor male candidates because it was trained on biased historical hiring data. In total rewards, AI is being deployed to analyze pay equity and benchmark salaries against real-time market data, supporting a move towards greater pay transparency. However, this also introduces the risk of "surveillance pay," where opaque algorithms set wages based on the lowest amounts workers will accept, potentially uncoupling hard work from fair compensation. This has prompted new regulatory scrutiny, with some jurisdictions considering laws that would require employers to disclose when AI is used in pay and hiring decisions. This evolving landscape is redefining the role of the Chief Product Officer, who must now possess not only business acumen but also technical fluency in areas like model selection and training pipelines. Strong CPOs are moving beyond simply adding AI features, instead focusing on where AI genuinely improves user outcomes and establishing clear guardrails around data privacy, ethics, and compliance. Effective AI governance requires a cross-functional approach, bringing together leaders from HR, IT, legal, and security to form dedicated councils. These groups are tasked with defining clear ethical standards and policies *before* deploying new tools. A key practice is maintaining human oversight for all critical HR decisions, ensuring that AI serves as a co-pilot rather than a fully autonomous decision-maker. The next wave of enterprise AI involves agentic workflows, where autonomous AI systems can reason, plan, and execute complex, multi-step tasks across different platforms. In HR and finance, this could mean AI agents that handle everything from payroll adjustments after a compensation cycle to validating expense reports against company policy, promising significant efficiency gains but also demanding more sophisticated risk management frameworks.
