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THE GUEST

Amri Abuseman is a powerhouse engineering leader known for building quality-first cultures across high-stakes industries—from healthcare and FinTech to telecom and HR tech. As Director of Engineering at Flatiron Health, she leads teams operating at the critical intersection of software delivery, system reliability, and regulatory rigor—where the margin for error is small and the impact of great engineering is enormous. With deep roots in quality engineering, Amri brings a powerful blend of technical depth and strategic leadership. Her toolkit spans everything from AI-driven test automation to enterprise-scale release management, helping organizations ship software that is not only fast, but reliable and resilient. But what truly sets her apart is her ability to align product and engineering through clear strategy, strong technical fluency, and genuine cross-functional empathy. Amri is the kind of leader who doesn’t just ship software—she builds the systems, cultures, and practices that make high-quality delivery sustainable at scale. She’s passionate about creating engineering environments where quality is embedded from the very beginning, not treated as an afterthought. And in this episode, we’re diving into what it really takes to build quality-first engineering organizations, how AI is reshaping test automation and software reliability, and why aligning product and engineering is the key to delivering software that truly matters.

THE SUMMARY

The fastest way to learn AI is to build, not study: Spending months watching tutorials or completing courses rarely leads to real capability. The only way to understand AI tools properly is to experiment, build rough prototypes, and learn through failure.

AI is removing the biggest barrier to building software: Modern AI coding tools are enabling people with little or no programming experience to create real products. This shift means the next wave of builders may come from non-traditional technical backgrounds.

AI tools only succeed if they actually fit developer workflows: Engineers quickly abandon tools that slow them down or misunderstand their code context. Real productivity gains only happen when AI tools integrate seamlessly with existing development habits.

Buying AI tools doesn’t guarantee productivity gains: Many organisations assume that simply adopting AI tools will instantly improve output. In reality, poor integration, unclear use cases, and workflow friction often lead teams to stop using them entirely.

Regulated industries are still pushing forward with AI innovation: Sectors like healthcare and finance face strict restrictions on using public AI systems. Instead of avoiding AI altogether, companies are building internal AI environments to stay competitive while maintaining compliance.

Product managers can’t afford to stay non-technical anymore: Leaders who avoid experimenting with AI tools risk losing visibility into what their teams can actually build. Getting hands-on with AI tools dramatically improves product intuition and decision-making.

The real AI skill isn’t prompting—it’s persistence: Success with AI often comes down to patience and experimentation. The builders who succeed are the ones willing to debug, refine prompts, and iterate repeatedly until something useful emerges.

THE SHOW

Weekly conversations with the AI’s top product leaders. Join Polly Allen as she discovers the paths to success in the world of AI.

THE LINKS

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Amri Abuseman

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