AI is rapidly evolving, and engineering leaders face challenges in implementing solutions while justifying spend and avoiding risky investments. To position themselves strategically for AI adoption, teams must recognize that deploying generative AI should be driven by specific use cases rather than trend-following. Organizations must balance risk and reward by investing in a mix of low-risk improvements alongside more experimental applications. Effective adoption requires alignment between technical feasibility and strategic business value, with leaders building internal trust by championing successful AI projects while demonstrating a deep understanding of business needs and risks. Engineering teams need specialized tooling to track model performance and detect anomalies before they affect business outcomes. AI investments carry unique considerations beyond typical software projects, including ongoing requirements for data curation, model retraining, and specialized operational support. Leaders must assess not just implementation costs but also these ongoing expenses to ensure successful AI adoption.