In 2012, the focus was on adopting software, with companies prioritizing the shift from on-premise solutions to cloud-based systems. Productivity gains were the primary goal. Today, with software embedded in every aspect of work, the focus has shifted to how these tools are used. The concept of "trajectory" describes not just having the right software, but how individuals navigate these tools to achieve their objectives. For example, a salesperson might follow a specific path when managing leads, while someone using AI for research will explore questions in a structured way. Understanding these trajectories is essential for automation and optimization. Companies used to rely on consultants to map processes, but AI can now observe and record workflows in real-time. By analyzing numerous user interactions, AI can identify effective patterns and areas needing improvement. This data not only enhances AI products but also creates a competitive advantage through increased vendor lock-in. The more detailed the trajectory data, the more specialized and effective the AI solutions become. Leadership within organizations can gain significant insights by analyzing employee trajectories. There’s often a gap between perceived collaboration and actual workflows. By truly understanding how work gets done, businesses can make informed decisions about their processes. As AI models evolve, they may rely on trajectories for reinforcement learning, leading to smaller, more efficient models that reduce costs and improve accuracy. This raises important questions about data ownership in enterprise software agreements, as companies will need to consider how to protect their trajectory data while negotiating for AI solutions.