Where AI products go next: voice, agents, and self-driving software | Tara Sesha and Nan Yu (OpenAI)

Building successful AI products requires shifting from a perfectionist mindset to one that prioritizes rapid iteration and empirical user evidence. Shipping imperfect solutions, such as early agentic harnesses, allows teams to observe real-world usage and refine functionality before competitors leapfrog them. Effective product leadership in this era demands a synthesis of deep user empathy and robust systems thinking to manage the inherent unpredictability of non-deterministic models. Maintaining direct, accessible relationships with users is essential for capturing subtle feedback on agent behavior. Looking toward 2027, the product landscape will likely shift toward voice-first interfaces and "self-driving" experiences that automate complex workflows, effectively reducing the cognitive load on users and solving the "empty input box" problem. Success hinges on planning in short, two-to-three-month horizons rather than attempting long-term predictions in a rapidly evolving technological environment.