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Key Takeaways

1. Hire for business fluency, not just tech chops.Even in deep technical roles, prioritize candidates who ask sharp product questions, understand customer needs, and challenge assumptions. At Abridge, the best hires can move between code and conversation without missing a beat.

2. Design your org around product bets, not functions.Skip rigid structures. Instead, build flexible pods aligned to specific initiatives—each one mixing traditional roles with AI-native talent like prompt engineers, domain experts, and model builders.

3. Invest in hybrid athletes who span domains.Clinician-technologists—MDs who can code, prototype, or guide ML work—are force multipliers in healthcare AI. Seek out and empower these unicorns with the tools and team support they need to ship.

4. Treat applied research as the frontend of product.Don't silo your scientists. Much of what looks like “research” is really pre-product work—surfacing use cases, prototyping capabilities, and testing limits. Encourage tight loops between research, engineering, and users.

5. Build tooling that proves your AI’s trustworthiness.If you’re in a high-stakes domain, interpretability isn’t optional. Let users trace outputs back to raw inputs. Abridge’s “linked evidence” lets doctors audit every sentence of an AI-generated note—no trust fall required.

6. Separate how fast you build from how fast you ship.To serve enterprises, decouple product development from release schedules. Move fast internally, but deploy predictably—using feature flags, beta groups, and clear comms to manage rollout complexity.

7. Put partner success on par with product.Supporting complex customers isn’t a reactive function—it’s a strategic differentiator. Abridge’s partner success team maintains trust across 130+ hospital systems, translating product progress into long-term alignment.

8. Build data flywheels into your product DNA.Design products that generate the data you need to improve them. In AI systems, usage and improvement are tightly coupled—make sure your loop gets stronger with every interaction.

9. Staff pods based on ownership, not headcount ratios.There’s no universal playbook for how many AI engineers vs. PMs vs. domain experts you need. Start with the product objective, then layer in the right skill sets to get there.

10. Share your internal rigor externally.Enterprise buyers don’t just want performance—they want proof. Publish eval results. Open up your process. Abridge’s transparency with models, QA tools, and whitepapers became a competitive advantage.