In this episode of Between Product and Partnerships, Cristina Flaschen sits down with Stephanie Neill, Head of Product at Stripe, to explore what’s actually changing in product development in the age of AI, and what isn’t.

Stephanie draws on experience across government, Twitch, and Stripe to explain why the pressure to “just use AI” often leads teams in the wrong direction. Instead, she makes the case for grounding every decision in the problem to be solved, not the technology being used. The conversation dives into where AI is genuinely useful today, where it still falls short, and how teams can use it to move faster without compromising quality or trust.

They also explore the operational realities behind the hype, from messy, unreliable data to the risks of deploying AI in high-stakes environments like payments and tax. Throughout, Stephanie emphasizes a consistent theme: AI can accelerate good product thinking, but it cannot replace it.

Who we sat down with

Stephanie Neill is Head of Product at Stripe, where she leads teams focused on payments infrastructure and tax-related products. Her career spans e-commerce, public sector innovation with the United States Digital Service, and creator monetization at Twitch.

Stephanie brings expertise in:

•  Applying product thinking in high-stakes environments like government and financial systems 
•  Building platforms that enable businesses and creators to earn revenue 
•  Scaling product teams across complex, data-dependent domains 
•  Navigating emerging technologies like AI within regulated, trust-sensitive systems 

Key topics

• Why “use AI” is the wrong starting point

Teams are often pushed to adopt AI without a clear problem in mind. The right approach is unchanged: start with the user problem, then evaluate whether AI meaningfully improves the outcome.

• Where AI actually works today

AI is most effective in accelerating discovery and iteration, helping teams research faster, test ideas, and explore solution spaces without heavy upfront investment.

• AI as a tool for reducing product risk

Product development is fundamentally about reducing risk. AI increases the number of iterations teams can run, allowing them to be wrong more often and converge on better solutions faster.

• The reality of data as a limiting factor

Even the most advanced models are constrained by messy, incomplete, or externally controlled data. In domains like tax, poor data quality becomes a major blocker to reliable AI systems.

• Why human judgment still defines production readiness

Despite rapid progress, AI outputs still require careful review. Especially in financial systems, the cost of being wrong is too high to remove humans from the loop.

Episode highlights

 11:00 — The industry-wide pressure to “just use AI”
 13:30 — AI’s role in product discovery and rapid iteration
 16:20 — Automating repetitive work with internal tools at Stripe
 19:00 — What happens to entry-level roles in an AI-driven world
 22:30 — Why data quality is the biggest limiter for AI systems
 25:10 — The gap between AI hype and production reality
 32:00 — How Stripe evaluates risk before shipping AI-powered features
 35:10 — Staying grounded by continuously redefining the problem

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