On this episode we talk with Sarah Guo, founder of Conviction — a VC firm focused on early-stage AI investments. 

Sarah shares insights from her unique position at the intersection of AI research, startups, and enterprise adoption that are relevant to people building with AI. 

Topics like: How successful teams keep up with rapid changes in the AI landscape, how AI labs vs. enterprises approach problems differently, thoughts on why many AI initiatives struggle to move from prototype to production. She also shares some good advice for leaders on setting an ambitious roadmap and staying competitive in the current market. 

This conversation provides practical perspective on what's working with leading AI teams, and hopefully some advice others can learn from.

On this episode we talk with Tyler Phillips, AI Product Lead at Apollo.io, about what it really takes to build successful AI products in sales. Tyler shares surprisingly candid insights about the "unsexy" but critical work that great AI PMs do - from spending hours evaluating AI outputs to building systematic quality frameworks. Learn why it can help for domain experts should write prompts instead of engineers, how Apollo measures AI quality across their products, and the practical strategies they've developed after some early missteps. Whether you're an aspiring AI PM or an engineering leader trying to structure your team effectively, this conversation offers useful lessons on what actually drives AI product success. 

On this episode of Deployed: The AI Product Podcast, we sit down with Ben Kus, CTO of Box, to unpack how they built a secure, scalable AI platform within their enterprise content management system. Ben shares candid insights from Box's journey integrating AI capabilities while maintaining enterprise-grade security for sensitive customer data. Learn practical strategies for evaluating AI quality without accessing customer data, building internal platforms that engineering teams want to use, and designing architecture that can evolve with rapid AI advances. Whether you're integrating LLMs into an established product or building new AI features, this conversation offers valuable lessons on balancing innovation with enterprise requirements. Join Ian, Co-Founder and CEO of Freeplay, for this in-depth discussion on scaling AI responsibly in the enterprise.

In this episode, we sit down with Arya Asemanfar, Product and Engineering leader at Sierra, to explore the future of AI-powered enterprise agents. Arya breaks down how Sierra is transforming customer experiences by building agents that go beyond traditional chatbots—taking meaningful actions, embodying brand voice, and scaling reliably for unique business needs.

Learn how Sierra approaches agent development with their innovative “Agent OS,” solves challenges like tool hallucination, and co-designs solutions with customers. Arya also shares actionable insights for product and engineering leaders, from building better evaluation systems to designing intuitive feedback loops.

Topics covered in this episode:

What Are Enterprise Agents?

• The difference between traditional chatbots and AI-powered agents that take meaningful actions.
• Examples of how Sierra’s agents transform customer service interactions (e.g., returns, exchanges).

Building Scalable AI Agents

• The importance of robust architecture for reliability and performance.
• How Sierra’s “Agent OS” supports the full lifecycle of agent development—from design to monitoring.
• Addressing tool hallucination and ensuring agents only take actionable, reliable steps.
• Using evaluation frameworks to test and refine agent behavior at scale.

Collaborating with Customers

• Why each agent is treated as a unique product tailored to a customer’s brand, policies, and processes.
• The co-design process and how it ensures agents align with business needs.

Actionable Advice for Teams Building AI Agents

• Start with concrete customer examples to develop intuition and build better agents.
• Implement evaluation systems early to accelerate learning and iteration.
• Invest in tools and processes to streamline human feedback and testing.

The Future of AI and Product Design

• Emerging paradigms that will transform how users interact with software.
• Predictions for how AI will shape new user experiences beyond automation.

In this episode of Deployed: The AI Product Podcast, we talk with Nick Francis, CEO and Co-founder, and Luis Morales, VP of Engineering at Help Scout, about their journey integrating AI into their established (13 years in market!) customer support platform.

Nick and Luis share candid insights on:

• How they shifted from skepticism about AI to embracing it as a tool for delighting customers
• Their process for experimenting with AI features, from summarization to automated email drafts
• Challenges in prompt engineering and building effective RAG systems
• Creating a new role for an AI product quality expert with deep customer knowledge
• Transitioning to an experimentation-driven engineering culture
• Making the bold decision to completely overhaul their business model and pricing

The conversation covers both high-level strategy and tactical details of Help Scout's AI development process. Nick and Luis offer valuable perspectives for any company looking to thoughtfully integrate AI capabilities into existing products.For product and engineering leaders navigating the complexities of AI adoption, this episode provides practical advice on balancing innovation with customer needs. Tune in to learn how Help Scout is leveraging AI to enhance the customer support experience while staying true to their values.

Kyle Nesbit, a longtime Googler and AI expert, joins us on Deployed to share lessons from his 17+ years at the forefront of distributed systems, machine learning, and AI-driven product innovation. 

Kyle has helped build foundational technologies like BigQuery and worked on early large language model (LLM) development at Google, giving him a unique perspective on how teams can successfully transition from traditional engineering to modern AI-focused workflows.

In this episode, we explore:

• The challenges and opportunities of transitioning traditional engineering teams to LLM development
• Why starting with evaluation metrics is the foundation for success
• Practical strategies for iterative improvement and guardrail design
• How to balance product priorities and quality trade-offs when scaling AI systems
• The real story behind AI demos and how to communicate progress effectively
• Foundational issues in data discovery and access—and why solving them matters more than chasing trends

In this episode of Deployed, we sit down with Sara Beykpour, Co-founder and CEO of Particle, to discuss how they're using AI to transform how people consume news. Particle, which just launched last week, organizes news coverage across multiple sources into an easy-to-read, summarized, and personalized feed.What makes Particle particularly interesting is how seamlessly they've integrated AI into the core news reading experience.

Rather than building yet another AI chatbot, they've created an intuitive news app where the AI works behind the scenes to deliver better summaries and insights.In this conversation, Sara shares valuable lessons about building AI products that actually work for customers, including:• How they approach quality and trust when using AI to summarize news

• Their practical process for developing and improving AI features
• Key learnings about evaluation pipelines and prompt engineering
• Insights on working with publishers in the AI eraTune in to hear Sara's perspective on the future of AI in media and her advice for other founders building in this rapidly evolving space.

 Whether you're a product manager, engineer, or just curious about the intersection of AI and media, this episode offers actionable insights you can apply to your own work.

Check out Particle at particlenews.ai!

Join us for an insightful conversation with Eliza Cabrera (Principal Product Manager) and Beau Lyddon (Principal Engineer) of Workday as they share their journey implementing generative AI at enterprise scale. As one of the first major enterprise software companies to roll out GenAI features, Workday offers valuable lessons for product and engineering teams navigating this technology.

Learn how Workday approached everything from their first MVP features to scaling AI across their platform, including:

• How they identified the right initial use cases and measured success
• Their approach to prompt engineering, model selection, and RAG architectures  
• Managing enterprise customer expectations around data privacy and compliance
• Creating an AI playbook to help teams across the company adopt GenAI
• Balancing innovation with enterprise-grade reliability

This episode offers practical insights for anyone working to bring generative AI capabilities to enterprise software products. Eliza and Beau share candid perspectives on what worked, what didn't, and how to think about scaling AI responsibly in complex enterprise environments.

Whether you're just getting started with GenAI or working to scale it across your organization, this conversation provides valuable lessons from leaders who have successfully navigated these challenges at one of the world's largest enterprise software companies.

• Some of the limitations of LLMs, and what to do about them — especially when it comes to summarizing lots of content (tl;dr - long context windows don’t solve everything)
• How they’ve developed their eval suite through a practical iteration process among in-house finance experts, product, and engineering
• Thoughts on staffing AI engineering teams, including what he’s seen work to get strong software engineers up to speed working with LLMs