The Problem — AI Without Guardrails
Most organizations are rushing to deploy AI without a strategy that aligns with their business goals, risk tolerance, or operational realities. The result is fragmented adoption, shadow AI, inconsistent quality, rising costs, and avoidable security and compliance exposure.
AI deployed without a strategic framework can introduce bias, leak sensitive data, and drift into unsafe or unreliable behavior. These issues are not just technical failures, they are business risks that can undermine customer trust and long-term competitiveness.
Regulatory momentum continues to build. The EU AI Act’s 2025 enforcement deadlines bring fines up to 7 percent of global turnover. In the United States, the federal order on Safe, Secure, and Trustworthy AI establishes national expectations for model oversight, transparency, and testing. NYDFS Part 500 amendments introduce stricter governance requirements for firms handling sensitive financial data.
Yet most enterprises still rely on one off checklists managed by siloed teams. This approach does not address model specific threats, lifecycle management challenges, or strategic decisions around deployment, procurement, and prioritization. Modern AI systems require a coordinated, cross functional strategy supported by strong governance.