Today's software teams face increased release risks despite faster shipping, as a Harvard Business Review Analytic Services survey sponsored by LaunchDarkly reveals that over half of organizations encounter software release issues monthly, leading to potentially severe consequences like lost revenue and damaged reputations. Many teams rely on outdated risk management practices such as manual rollbacks and big bang deployments, which are particularly hazardous in the AI-driven market, where only 6% can detect release issues in real time. The survey indicates a shift away from big bang deployments due to their magnified risks, with AI introducing new challenges like model drift and unpredictable behavior that increase release frequency and risk. To counter these challenges, leading teams are adopting strategies like decoupling deployments from releases using feature flags, employing progressive rollouts with real-time monitoring to manage risk effectively, and automating rollbacks to ensure rapid recovery, all contributing to a more resilient release process.