Accelerating Scientific Breakthroughs at an AI-Biotech Scale-up

Dyno Therapeutics, a pioneer in AI-driven gene therapy, faced a critical scaling challenge that threatened to slow its innovation. As the company grew, its brilliant teams—spanning machine learning, data science, and biology—were trapped in functional silos. Handoffs were slow, coordination was complex, and rigid plans were out-of-sync with the reality of R&D.

We worked with Dyno’s leadership to replace this “mechanical agility” with a lightweight, outcome-driven operating system. By reorganizing into cross-functional teams focused on clear product goals, we eliminated friction and aligned the entire company—from the lab to the executive team—around a shared mission.

The result? Faster innovation loops, reduced coordination drag, and a resilient structure that scales with complexity, not against it.

The Challenge: When Silos and Handoffs Slow Down Science

Dyno Therapeutics operates at the cutting edge of biotech, using AI to design new gene therapies. As the company scaled, it hit a common, expensive problem: its organizational structure couldn’t keep up with its mission.

• Functional Silos: Brilliant teams in the lab (Biology), in data science, and in machine learning (ML) worked separately. This created painful handoffs, misaligned incentives, and slowed down the innovation cycle.

• Rigid Plans vs. R&D Reality: Traditional project management (like Gantt charts) and “mechanical” agile ceremonies were failing. Plans were obsolete almost immediately in the face of rapid scientific discovery and uncertainty.

• “Process Theater”: The teams were doing agile ceremonies, but they weren’t being agile. The system lacked true feedback loops and wasn’t creating strategic adaptability. This coordination drag was a direct threat to the company’s ability to innovate.

The Solution: A Pragmatic Operating System for Hybrid Teams

Working directly with Dyno’s founders and leadership, we designed and implemented a lightweight operating system focused on outcomes, not just process. This went far beyond textbook Scrum or OKR implementation.

1. Reorganized Around Outcomes, Not Functions: We dissolved the rigid functional silos. We redesigned the organization into small, cross-functional teams that combined lab scientists, ML engineers, and data scientists, all aimed at a single, shared “Product Goal.”

2. Aligned Strategy with Execution: We helped the leadership team level up its OKR cycle, moving it from a “to-do list” to a true strategic tool. We created a clear connection between the company’s highest-level objectives and the daily work of the agile teams, ensuring (and proving) that every experiment was driving the mission forward.

3. Installed an “Empirical” Engine: We replaced the “process theater” with a simple, powerful engine for learning. By focusing on real feedback loops, teams could inspect and adapt based on what they were discovering in the lab and in the data, not just based on a pre-defined plan.

4. Scaled Agility to the Entire Business: We applied this same outcome-oriented, cross-functional logic to the entire company, including G&A and the executive team, treating the organization itself as a product that could be continuously improved.

The Results: Faster Innovation and a Structure That Scales

By fixing the underlying operating system, Dyno Therapeutics unlocked its teams’ full potential and created a structure that could handle massive complexity.

A Resilient, Scalable Organization: Dyno is now able to scale its complexity and its headcount without collapsing under coordination overhead. The company has a proven, lightweight operating system that allows it to adapt and pivot as fast as its science discovers new opportunities.

Faster, More Effective Innovation: By removing handoffs and aligning teams around a single goal, innovation loops accelerated. Teams could move from hypothesis to experiment to learning in a fraction of the time.

Dramatically Reduced Coordination Drag: Transparency increased across the organization. The new structure eliminated the friction, alignment issues, and rework that stemmed from the old silos.

Empowered, “T-Shaped” Teams: The new model fostered a culture of shared ownership. Scientists, engineers, and data experts began to learn from each other and broaden their skills, feeling more engagement and a clearer sense of purpose.