Asha Sharma: Why org charts give way to agent work charts

Modern AI products should be designed as organisms that continuously ingest data, update reward models, and improve outcomes over time—making the learning loop itself the core intellectual property of the company.

As large models reach scale (e.g., ~30B parameters), it becomes more economical and powerful to adapt them via fine-tuning, RL, and high-quality feedback data—often using proprietary, expert-labeled, or synthetic data—rather than training from scratch.

Different tasks need different models (latency, quality, domain), and future interfaces will favor code-native, composable primitives and text streams that agents can orchestrate, rather than fixed, hand-crafted GUIs.

Successful companies get everyone using AI in their workflows, start with existing processes (e.g., support, fraud) to prove value, then scale to growth use cases—always with clear blueprints, evals, observability, and P&L-level measurement.

As embedded and embodied agents take on more tasks, organizations can have fewer layers and more dynamic, task-based routing of work—humans decide where AI is applied while agents execute and continuously improve.