Luister naar The Enterprise GTM Podcast

In this must-listen episode of The Enterprise GTM Podcast, Pavi Ramamurthy, Global CIO and CSO of Blackhawk Network, shares a candid and deeply insightful look into the world of enterprise security, tech innovation, and risk management. From managing massive fraud prevention systems behind the gift cards you see at Target, to navigating startup pitches and the realities of AI adoption—this is a masterclass in modern IT and security leadership. 💡 What You’ll Learn: • How Pavi balances business priorities vs. security risks • Why startup founders need to drop the pitch and start a real conversation • The surprising cost of switching vendors—even if the tech is “10x better” • Why shadow AI isn’t so different from shadow IT—and how to manage both • The power of CISO communities and peer recommendations • Why “best-of-suite” often wins over “best-of-breed” in today’s enterprise deals • What makes a new security vendor stand out—or get silently passed over • The magic wand wish that Pavi has from startups 💬 Memorable Quote: “I ask one question before investing in any solution: What’s the cost of not doing this?” “Know your CISO!” 👀 Who Should Listen: • Founders and startups pitching to the enterprise market • Security and IT professionals navigating risk and innovation • Anyone curious about how real enterprise decisions get made This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit veenormous.substack.com [https://veenormous.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

In this episode, Diamond Bishop shares his fascinating journey through AI, machine learning, and enterprise software, from working at Microsoft (Cortana), Amazon (Alexa), AWS, and Meta (PyTorch) to founding Augmend, an AI-driven DevOps knowledge assistant, which Datadog later acquired. He dives deep into building AI-powered enterprise products, the challenges of selling into large organizations, and why AI-driven automation is the future of DevOps and security operations. Diamond also unveils Bits.ai, Datadog’s intelligent AI assistant designed to automate on-call incident resolution, debugging, and code error fixing. For founders, product leaders, and AI enthusiasts, this episode is packed with lessons on enterprise GTM (Go-To-Market) for Developer Tools, AI-driven automation, and the future of software engineering. Key Topics Discussed: 1️⃣ The Evolution of AI & Machine Learning 2️⃣ Founding Augmend: AI for DevOps Knowledge Sharing 3️⃣ The biggest challenges for Dev Tool startups 4️⃣ Bits.ai: The AI Assistant for DevOps & Security 5️⃣ Lessons in building with Gen AI & GTM Insights Who Should Listen? ✅ AI/ML Engineers & DevOps Professionals – Learn how AI-driven automation is transforming workflows ✅ Tech Founders & Enterprise GTM Leaders – Get real-world insights on selling AI to enterprises ✅ Investors & AI Enthusiasts – Understand the next wave of AI-powered enterprise automation Resources & Links: 🔗 Follow Diamond Bishop [https://www.linkedin.com/in/diamondbishop/] on LinkedIn 🔗 Datadog Bits.ai [https://www.datadoghq.com/product/platform/bits-ai/] (Private Beta) 📢 Enjoyed this episode? Don’t forget to subscribe, rate, and share! 🚀 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit veenormous.substack.com [https://veenormous.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Imagine walking into a digital store where the AI knows exactly what you want before you do. No more endless scrolling. No more generic recommendations. Just hyper-personalized shopping at lightning speed. In this electrifying episode of The Enterprise GTM Podcast, Vidya Raman sits down with two AI powerhouses: 🔹 Sameer Singh, CTO of Spiffy, a startup rethinking AI-powered shopping assistants 🔹 Anu Trivedi, Head of R&D at Flipkart, India’s largest e-commerce marketplace Together, they pull back the curtain on the AI revolution that’s already reshaping how we buy, sell, and engage with products online. What’s Inside? ❓ Why are today’s product recommendations so outdated—and how is AI fixing it? 🤯 Are personalized AI shopping assistants finally here? And are they actually good? 🚀 How is Flipkart handling AI at a mind-blowing scale of 600M+ customers? 🔥 Is AI-powered e-commerce too smart—or not smart enough? If you’re curious about how AI will change the way we shop forever, this is an episode you can’t afford to miss! Links mentioned in today’s episode: Sameer Singh [https://www.linkedin.com/in/sameersingh/] Spiffy AI [https://www.spiffy.ai/] Anu Trivedi [https://www.linkedin.com/in/anusua/] Flipkart [https://www.flipkart.com/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit veenormous.substack.com [https://veenormous.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

In this must-listen episode of the Enterprise GTM Podcast, I dive deep with two powerhouse AI experts: 🎙 Manasi Vartak (Chief AI Architect at Cloudera) 🎙 Shreya Shankar (PhD Candidate, UC Berkeley) We discuss the current landscape of GenAI in enterprises. We explore the challenges of productionizing AI applications, the importance of data privacy, and the evolving use cases within large organizations. Manasi shares insights from her experience at Cloudera, highlighting the slow adoption of GenAI in production and the prevalent use of internal applications. Shreya elaborates on her research at UC Berkeley, including how her work could transform data engineering. The conversation emphasizes the need for new tools and strategies to navigate the complexities of AI deployment and the significance of benchmarking in this evolving field. This conversation explores the evolving landscape of data engineering and the role of Generative AI (Gen.AI) in automating processes, simplifying data management, and enhancing user experience. The speakers discuss the importance of coding assistants, the need for new benchmarks in AI tools, and the critical role of user experience in designing AI applications. They emphasize the necessity of education and training to help users effectively leverage AI technologies in their workflows. Takeaways * The excitement about GenAI remains strong in enterprises. * Adoption of GenAI in production is still under 10%. * Internal use cases dominate the current landscape of GenAI applications. * The RAG pattern is a leading use case for enterprises. * Data privacy and security are major concerns for enterprises. * AI is becoming more democratized, allowing non-experts to build applications. * Productionizing AI requires significant effort and infrastructure. * Shreya's research focuses on optimizing database interactions with GenAI. * Accuracy in prompting is a complex and fuzzy concept. * Benchmarking in GenAI is challenging due to the lack of ground truth. The future of data engineering will involve more automation and less manual toil. * Gen.AI can simplify data management tasks significantly. * Natural language processing will make data access easier for non-technical users. * User experience is crucial for the success of AI applications. * AI tools should be designed for seamless human-AI collaboration. * Benchmarks for AI tools need to be redefined beyond traditional accuracy measures. * Education is key to helping users effectively utilize AI technologies. * Domain experts will benefit from tailored coding assistants in their specific environments. * The transition from creation to review in AI applications is essential for user engagement. * Accessibility of Gen.AI presents new opportunities for education and innovation. Chapters 00:00 Introduction to AI Experts 02:05 Current State of GenAI in Enterprises 05:58 Use Cases and Adoption Challenges 11:59 Parallels in AI Evolution 16:02 Shreya's Research on GenAI and Databases 22:41 The Future of Data Engineering and Automation 24:11 Simplifying Data Management with Gen.AI 27:20 The Role of Coding Assistants in Data Tasks 30:47 Rethinking Benchmarks for AI Tools 35:33 User Experience in AI Applications 39:20 Designing for Human-AI Collaboration 44:18 Education and Training for Effective AI Use Links mentioned in today’s episode: Shreya Shankar [https://www.sh-reya.com/] Shreya’s work DocETL [https://www.docetl.org/] Manasi Vartak on Linkedin [https://www.linkedin.com/in/manasi-vartak/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit veenormous.substack.com [https://veenormous.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

In today's episode, we sit down with Jerry Ting to delve into his experience of founding and scaling a startup in the world of AI-powered contract management. Jerry is the Founder and CEO of Evisort, a cutting-edge AI-powered contract management platform transforming how organizations manage, analyze, and automate their legal documents. Jerry's vision has been instrumental in establishing Evisort as an innovator at the intersection of law and technology. In our conversation, we discuss Jerry’s transition from sales to law, the early days of Evisort, and how he overcame skepticism to secure the company’s first clients. We also unpack the steps to identifying the right time to build sales and marketing teams, determining pricing, and the importance of a customer-first approach. Explore Evisort’s mission-driven innovation approach, the strategy of “land and expand,” and the advantages startups have over larger competitors in delivering user-focused solutions. Join us for a detailed look at the challenges and successes of building a transformative legal tech company from the ground up with Jerry Ting. Key Points From This Episode:  * Introducing Jerry and his background in law and journey into entrepreneurship. * Jerry explains Evisort's product-led approach to its enterprise selling motion. * Why working with large enterprises is sometimes a curse for smaller companies. * Making the first Sales hire for Evisort and what he would do differently. * Discover how he determined Evisort's initial offerings and associated pricing. * How Evisort's mission to make peoples' lives easier catalyzed its growth.  * How do you go from departmental sales to enterprise sales. * Jerry shares advice for budding founders of AI-native companies. Quotes: “[Evisort] really early on took a product-led approach in our enterprise selling motion.” — Jerry Ting [0:06:45] “My advice to the founders listening out there is that sometimes it is a curse to actually get big enterprises [interested] in the beginning.” — Jerry Ting [0:07:55] “If you really focus around the customer from a 360 [degree] company perspective, how you sell, how you support, how do you build your product, over time they become a great sounding board for the company strategy.” — Jerry Ting [0:18:18] Links Mentioned in Today’s Episode: Jerry Ting on LinkedIn [https://www.linkedin.com/in/jerryting/] Evisort [https://www.evisort.com/] Tim Zonca on LinkedIn [https://www.linkedin.com/in/timzonca/] Tim Zonca on X [https://twitter.com/timzonca] Vidya Raman on LinkedIn [https://www.linkedin.com/in/vidyaraman/] Vidya Raman on X [https://twitter.com/veenormous] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit veenormous.substack.com [https://veenormous.substack.com?utm_medium=podcast&utm_campaign=CTA_1]