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Peter Gostev

Head of AI at Moonpig, an e-commerce company, and former AI strategy lead at NatWest.

Top 3 podcasts with Peter Gostev

Ranked by the Snipd community
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8 snips
Oct 16, 2024 • 40min

How to Create AI Strategy in Enterprises with Peter Gostev from Moonpig

Peter Gostev, Head of AI at Moonpig and former AI strategy lead at NatWest, shares valuable insights on creating effective AI strategies in large enterprises. He discusses the challenges of deploying LLMs, particularly in traditional banking and e-commerce. Peter highlights the importance of supportive leadership and the need for hands-on experience in AI implementation. The conversation also touches on underappreciated developments in AI, the automation of customer service, and how innovative projects can emerge from everyday workflows.
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Jul 24, 2025 • 1h 6min

#126 - AI Transformation at Scale: Practical Adoption Across 150+ Engineers with Peter Gostev // Head of AI @ Moonpig

Peter Gostev, Head of AI at Moonpig, is a trailblazer in AI transformation within large engineering organizations. He discusses the challenges of bridging the gap between AI hype and real-world implementation, especially in a 600-person company. The conversation covers managing AI adoption among 150 engineers while emphasizing the need for effective communication and training. Peter delves into the realities of AI tool usage, the importance of collaboration, and crafting a three-pillar AI strategy that combines tools, automation, and experimentation.
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Oct 1, 2024 • 53min

128 | The strategy and use cases used by a company to implement AI successfully with Peter Gostev

In this engaging discussion, Peter Gostev, the Director of Data at Moonpig, shares his expertise on AI implementation in large e-commerce environments. He outlines strategies for successful AI integration, including forming collaborative teams and selecting impactful use cases. Peter explains how Moonpig uses AI for automation, enhancing customer service efficiency while maintaining quality. He also dives into the role of large language models in data analysis and how to navigate the balance between automation and human involvement.

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