The Ravit Show

Ravit Jain

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side.

We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

Play

Open in

22 episodes
daily
Avg 13 min
English

Episodes 22

Newest Oldest Longest

All20 Unplayed20 In progress0 Played0

Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.

Wednesday · 21 min
The Future of Coding? Build Apps with AI No Code
Sat down with Mukund Jha, Founder and CEO of Emergent, on The Ravit Show at MongoDB.local Bangalore. Mukund is not new to building. He was part of the team that built Dunzo, founded startups before that, and has deep technical roots in ML and NLP. What he is doing now with Emergent is one of the most interesting vibe-coding stories happening right now. The company recently crossed $100 million in ARR, raised close to $200 million from Creaegis, Amazon, Ranjan Pai's Claypond, and others, and the numbers underneath are just as real as the funding. Here is what we got into. We started from the beginning. What Emergent actually is, the problem that made him want to start the company, and what it looks like in practice today. You describe what you want in plain English, and autonomous AI agents build, test, and deploy the full-stack app for you. Frontend, backend, database, hosting. All handled. The scale is hard to ignore. 10 million apps built across 190 countries. Deployment rates doubled in three months. Two thirds of power users are now taking complex apps live. This is not a demo product. This is a company that hit $100 million ARR eight months after public launch. We got into the database decision. Emergent tested PostgreSQL early on and ran into schema migration loops as agents tried to adapt apps while users kept changing requirements in real time. Mukund walked me through why MongoDB Atlas became the default for every app on the platform, and why the flexible document model maps naturally to how agents actually work. We talked about what is happening as more of these apps move from prototype to production. What MongoDB made easier that would have been much harder otherwise. And the patterns emerging in what people are building, which tell you something about where software is headed. Mukund gave the keynote at the event. I asked him what the one thing he wanted the room to walk away with was. His answer was clear and specific, and worth hearing from a founder who has already shipped at this scale. We closed on India. What the Indian builder community means to him, and why. A few things stayed with me. - The vibe-coding wave is not a toy. When your platform has 10 million apps live and the company just crossed $100 million ARR, the conversation shifts from whether this works to how it scales. - Schema flexibility is not a nice-to-have for AI-native products. It is the reason the agents can actually function when users change their minds every five minutes. - Some of the most interesting software being built right now is being built by people who do not call themselves developers. That changes things. #data #ai #mongodb #mongodblocal #theravitshow
Tuesday · 16 min
What Every AI Developer Should Know Before Building at Scale
What does it actually take to build AI products at scale in India? That was the focus of my conversation with Shrey Batra, Head of Engineering, Platforms at HROne and Founder of Cosmocloud, during MongoDB.local Bangalore. We started with the latest MongoDB announcements, including voyage-context-4, Hybrid Search, Native Reranking, and the expansion of Search and Vector Search. But the discussion quickly moved beyond product launches. Shrey shared what it looks like to build and run production systems in India, the infrastructure challenges that most teams underestimate, and why getting the data layer right matters long before AI agents enter the picture. We also talked about: -- Building Cosmocloud and the lessons from running it in production -- Why Indian AI founders have a unique opportunity right now -- What being a MongoDB Champion really means -- Why developers should join MongoDB User Groups -- How HROne and Cosmocloud use MongoDB today -- Where AI agents are headed -- One technology trend that's overhyped and another that's not getting enough attention It was a practical conversation with someone who is building every day, not just talking about AI. #data #ai #mongodb #mongodblocal #theravitshow
Monday · 15 min
India Needs 2 Million AI Builders. Here's MongoDB's Plan
Last week at MongoDB.local Bangalore, I sat down with Basavadarshan G N, or Darshan as most people know him, Senior Academia Partnership Manager for APAC at MongoDB, on The Ravit Show. Darshan sits at the intersection of two things I care a lot about. Developer education, and India's push to actually build for the AI era instead of just consuming it. His work with MongoDB for Academia is quietly one of the more important programs happening in Indian tech right now. Here is what we got into. - We started with the basics. What MongoDB for Academia actually is, who it reaches, and how it fits into the broader Indian developer landscape. If you have not looked at this program closely yet, this part is worth the time - We talked about the 650,000 students the program has already reached since 2023. That is not a small number. Darshan walked me through what has been driving the momentum, and why this moment felt right to double down and go bigger - We went into the big announcement from the event. MongoDB committing to upskill two million Indian builders by 2030. New curriculum in Kannada, Hindi, and Tamil. 1,500 plus institutions. 5,000 educators. I asked him what the actual roadmap to that number looks like, because two million is a promise that has to be earned, and he was clear about how they plan to get there - We spent real time on the language piece. Building curriculum in Kannada, Hindi, and Tamil is not a marketing move. It is an access move. Darshan's view on how big a barrier language has been for students outside the metros, and what opens up for them when that barrier drops, was one of the more grounded moments of the conversation. - We talked about foundational data skills too. The buzz right now is that AI is going to make technical skills more accessible. That may be true. But data skills are still the layer everything sits on, and Darshan made the case for why they are more important now, not less. - We got into the AICTE Virtual Internship Programme. What a student actually experiences, what they walk away with, and what they should be able to build after finishing it. A few things stayed with me. MongoDB is behind half of India's top 100 companies and 50 plus unicorns. Students trained on this stack are being prepared for the exact environment they will walk into on day one. The India AI story cannot be told without the education story. You cannot build two million careers on English-only curriculum. This is the real inclusion play. Two million by 2030 is not a slide. It is a plan with partners, institutions, languages, and a delivery model behind it. Worth watching. Full interview live now!!!! #data #ai #mongodb #mongodblocal #theravitshow
August 28 · 10 min
Everyone Is Building AI Agents Wrong. Here's What Actually Works
Last week at MongoDB.local Bangalore I sat down with Sejal Khanna, Senior Developer Advocate at MongoDB, on The Ravit Show. Loved hosting her!!!! Sejal spends her days helping developers move from AI curiosity to actually shipping. Workshops, hands-on sessions, product storytelling, community. She works at the layer where hype meets reality, which makes her one of the most useful voices to hear from right now. Here is what we got into. We started with what she is building and teaching right now. The volume of AI content out there is enormous. What she keeps finding herself filling in when she is working with builders directly is the gap between watching a tutorial and actually getting an agent to behave in production. We walked through the announcements from the day. voyage-context-4 GA. Hybrid Search GA. Native Reranking in public preview. Search and Vector Search shipping in Community Edition and Enterprise Advanced. Sejal broke down which of these she is most excited for builders to actually get their hands on, and why the Community Edition move might quietly be the biggest one for developers in India. We spent real time on her workshop, The A to Z of Building AI Agents. Reasoning, tools, memory, agent architectures, and then actually building one using MongoDB as memory, a Claude model, and LangGraph for orchestration. She walked me through what she hopes people walk away able to do, which is a lot more concrete than the average AI workshop pitch. #data #ai #mongodb #mongodblocal #theravitshow
August 27 · 19 min
AI Isn't the Problem. Your Data Architecture Is.
Last week at MongoDB.local Bangalore I sat down with Pete Johnson, Field CTO for AI at MongoDB, on The Ravit Show. Pete is a good friend and one of the most honest voices I know in this space. We covered a lot of ground. We walked through the announcements from the day. - voyage-context-4 going generally available. - Native Reranking hitting public preview. - Hybrid Search now GA. - Search and Vector Search shipping in both Community Edition and Enterprise Advanced. Pete broke down why retrieval quality has quietly become the most important AI conversation in enterprises right now, and what a real improvement in retrieval actually changes for teams trying to move from pilot to production. We spent real time on agentic AI. What is working, what is still slideware, and why the teams shipping agents in production are almost always the ones who solved the data layer first. We got into the message Pete brought to the general session. AI in production is a data problem, not a model problem. Simple line. Explains why so many enterprise AI programs stall. #data #ai #mongodb #mongodblocal #theravitshow
August 26 · 12 min
What It Actually Takes to Win at AI Transformation
Wow!!!! Loved hosting Erica Volini, Chief Customer Officer at MongoDB, here at MongoDB.local Bangalore on The Ravit Show. Erica has had a front-row seat to some of the biggest enterprise shifts of the last two decades. Deloitte. ServiceNow growing from 1.5 billion to over 10 billion in revenue. And now MongoDB at the center of the AI moment. So when she talks about how companies actually navigate change, you listen. Here is what we got into. I asked her how this AI moment compares to the transformations she has seen before. Her answer was honest. Faster, messier, and the gap between leaders who are experimenting and leaders who are deploying is wider than people realize. We talked about what she is actually hearing from enterprise leaders right now. Where they are excited, and where they are stuck. The stuck part was the more interesting half. We spent real time on India. MongoDB is behind half of India's top 100 companies and more than 50 unicorns. I asked her what that signals about where India is headed as an AI market. Her read on the speed of adoption here was sharper than I expected. Her background in human capital is rare for someone in her role, and that came through. She thinks about AI as much through the lens of people and skills as she does through the lens of platforms. That framing showed up strongly when we got to MongoDB's commitment to upskilling two million Indian builders by 2030. She made the case for why the developer pipeline matters as much as the product itself, and I agreed with most of it. A few things stayed with me from this conversation. The companies winning with AI right now are not the ones with the biggest budgets. They are the ones whose people are ready to use it. India is not just adopting AI. India is shaping how AI gets built for the rest of the world. And the next two years will separate the enterprises that treated AI as a project from the ones that treated it as a rewiring. More conversations coming soon stay tuned!!!! #data #ai #mongodb #lmongodbocal #theravitshow
August 25 · 9 min
SAP Transformation in 2026: Migration, AI, and the Road Ahead
For many SAP customers, the next couple of years will be critical. Between the upcoming ECC end-of-support deadline, evolving API strategies, and growing interest in AI, organizations have a lot of important decisions to make. At Boomi World Tour London, I had a chat with Donna Matthews to discuss what all of this means for SAP customers and how they can prepare for what's next. One of the biggest takeaways from our conversation was that modernization isn't just about completing a migration. It's about building a foundation that allows organizations to move faster, integrate more effectively, and take advantage of AI as their business evolves. During our discussion, we covered: * What SAP's recent API policy means for customers * How organizations should be thinking about the 2027 ECC end-of-support deadline * Why integration plays a key role in a successful S/4HANA journey * How SAP customers can start realizing value from AI today instead of waiting until migration is complete * Practical advice for organizations that are still planning or early in their transformation If your organization is navigating its SAP roadmap, this conversation offers valuable insights into the challenges and opportunities ahead. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld #BoomiAmbassador #theravitshow
August 24 · 13 min
How AI Companions and Agentic Workflows Are Reshaping the Enterprise
One of the biggest misconceptions in enterprise AI today is that better models automatically lead to better outcomes. During my conversation with Ann Maya at Boomi World Tour London on The Ravit Show, we discussed why many organizations are still struggling to move AI initiatives from experimentation into production despite significant investments in technology. What stood out to me was her perspective on AI readiness. Most companies focus heavily on models and tools, but the real differentiators are governance, context, trust, and the ability to connect knowledge across the organization. As AI agents become more common in enterprise environments, these foundational capabilities become even more important. We also explored: • Why bigger models don't always produce better business outcomes • The growing importance of agentic workflows • How organizations should think about enterprise governance in the AI era • Why context may become more valuable than the model itself • The gap between AI ambition and AI execution This was a thoughtful discussion on where enterprise AI is heading and what leaders should prioritize over the next few years. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld #BoomiAmbassador #theravitshow
August 19 · 11 min
Tech for Good: How Technology and AI Are Helping Charities Scale Their Impact
Technology is often measured by speed, efficiency, or innovation. But sometimes, its greatest impact is measured by the lives it helps improve. At Boomi World Tour London, I had the opportunity to speak with David Minahan from Young Lives vs Cancer about what "tech for good" really means and how technology can help charities deliver greater impact to the communities they serve. Our conversation went beyond technology itself. We discussed how charities are beginning to explore AI, the opportunities it creates, and the importance of ensuring technology always supports the people at the heart of their mission. We also talked about: * The mission and work of Young Lives Matter * What "tech for good" looks like in practice * How charities are adapting to AI and digital transformation * The role technology plays in helping nonprofits work more effectively * How Boomi supports Young Lives Matter in delivering better outcomes It was a refreshing reminder that behind every technology platform are people working to solve real-world problems. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld #BoomiAmbassador #theravitshow
August 18 · 21 min
How GlobalLogic and Boomi Are Helping Organizations Become Enterprise AI-Ready
AI readiness has quickly become one of the most discussed topics in the industry. But what does it actually mean to be AI-ready? At Boomi World Tour London, I sat down with Maneesh Garg and Subash Chandra Bose M from GlobalLogic to explore how organizations are preparing their data foundations for the next wave of AI innovation. A key takeaway from our conversation was that AI readiness is not achieved by deploying a model. It is achieved by creating the right foundation of data, integration, governance, and operational processes that allow AI initiatives to scale successfully. During our discussion, we covered: • How enterprises are preparing data for AI initiatives • Common challenges organizations encounter on their AI journeys • Why integration plays a critical role in AI success • The importance of creating trusted and accessible data foundations • Opportunities they see emerging in the next 12 to 18 months The conversation reinforced something I've heard repeatedly from industry leaders this year: Organizations that invest in strong data foundations today will be best positioned to realize value from AI tomorrow. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow
August 17 · 12 min
How AI Is Redefining Enterprise Integration with Capgemini and Boomi
For years, integration was viewed as plumbing. Today, it is becoming one of the most important foundations for AI. At Boomi World Tour London, I had the opportunity to speak with Rahul Murudkar from Capgemini about how AI is reshaping the integration landscape and why enterprises are rethinking the way they connect applications, APIs, and data. The conversation came at an exciting time for Capgemini, which was recently recognized as Boomi FY26 EMEA Growth Partner of the Year, highlighting the momentum the company is seeing across integration, automation, and digital transformation initiatives. What I found particularly interesting was how the discussion shifted from technology to business impact. As organizations pursue AI initiatives, integration is no longer just about moving data from one system to another. It is about creating intelligent, connected environments where information can be discovered, accessed, and acted upon in real time. In our discussion, we covered: * How AI is changing the role of enterprise integration * The challenges organizations face with disconnected systems and integration debt * How modern integration platforms are helping teams work more efficiently * The impact of AI on API management and automation * What measurable outcomes customers are seeing from modern integration strategies * Why integration is becoming a critical enabler for enterprise AI A great conversation for anyone thinking about the future of enterprise architecture, integration, and AI. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow
August 14 · 12 min
How Cognizant and Boomi Are Helping Enterprises Modernize Data, AI, and Automation
One thing I've noticed across nearly every AI conversation this year is that organizations are becoming much more focused on outcomes than technology. At Boomi World Tour London, I sat down with Azin Nylander from Cognizant to discuss what customers are actually asking for when they begin their AI, automation, and modernization journeys. The answer is often simpler than many people think. They want faster decision-making, better access to data, reduced complexity, and the ability to scale innovation without constantly rebuilding their technology stack. We explored: • The business challenges driving enterprise modernization initiatives • How organizations are approaching AI and automation investments • What successful data modernization projects have in common • The role integration plays in creating AI-ready enterprises • Why customer outcomes remain the most important success metric I particularly enjoyed hearing Azin's perspective on where enterprise transformation efforts are headed and how customer expectations continue to evolve. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow
August 12 · 14 min
How AWS and Boomi Are Helping Enterprises Build AI Agents at Scale
Everyone is talking about AI agents. What fewer people are talking about is what happens behind the scenes to make those agents actually work. At Boomi World Tour London, I spoke to Andrea Bureca from AWS to discuss the growing intersection of AI, data management, and enterprise integration. One theme that emerged throughout our conversation was that successful AI initiatives are rarely just about AI. They depend on access to trusted data, reliable integrations, governance, and the ability to connect systems across the organization. We discussed: • The patterns AWS is seeing across enterprise AI deployments • How customers are approaching agentic AI initiatives • Why data management is becoming increasingly important for AI success • Common mistakes organizations make when scaling AI projects • The value of partnerships in helping customers move faster and reduce risk • What customers can expect from the AWS and Boomi relationship moving forward If your organization is thinking about AI agents, this conversation offers a practical perspective on what it actually takes to make them successful. The full interview is now live. #data #ai #boomi #BoomiWorldTour #london #api #BoomiWorld ##BoomiAmbassador #theravitshow
August 11 · 14 min
Why AI Fails at Scale: The Rise of Context Engineering
Most enterprise AI projects don't fail on the model. They fail on context. That's the line that stuck with me from my conversation with Geetesh Iyer at Data + AI by Databricks Summit on The Ravit Show, right after his talk on the rise of the AI Context Engineer!!!! The pattern he laid out is one a lot of data teams will recognize. Accuracy looks great in the pilot. Then you scale, the inputs get messy, and the answers start breaking down. People blame the model or the data. Geetesh makes the case that both are usually fine. What's missing is the context, which definition of revenue to trust, why a metric changed last quarter, how leaders actually read the numbers. That knowledge lives in people's heads, not in the system. His answer is a new role built from the analyst seat: the AI Context Engineer. The person who encodes that business context so AI can be trusted at scale. We got into: - What an AI Context Engineer actually is, and why the role is showing up now - Why accuracy holds in pilots but falls apart at scale - Why the model and the data usually aren't the problem - The four layers of enterprise context, and the one most companies miss - Whether the harder part is the skills or the organizational buy-in - The one thing a leader should do tomorrow if this hits home His framing for all of it: AI is the engine, context is the fuel. And the people best positioned to provide that fuel are already on your payroll. Full interview below. Worth a watch if you're trying to get AI analytics past the pilot stage. #data #ai #databricks #wisdom #theravitshow
August 10 · 10 min
Why the Way You're Giving AI Agents Data Access Is Probably Wrong
Data + AI Summit by Databricks is in full swing!!!! Just finished talking with Steven Touw, CTO at Immuta, on The Ravit Show, about one of the problems nobody is talking about yet but everybody will be talking about in six months. The problem: an AI agent needs access to data inside your Databricks lakehouse. What do most enterprises do right now? They plug in the agent with a user’s OAuth token. The agent inherits everything that user can access. Simple. Done. Here is what actually happens next: the agent now has a user’s full permissions. If the agent gets compromised, your data does too. If the agent runs a query you did not intend, it looks like that user ran it. If you need to revoke access, you have to revoke the whole user. The audit trail tells you a person did the work when a machine did it. Steve calls this the authentication-authorization gap for agents. Everyone is solving for “can the agent prove who it is” and ignoring “can we control what it actually does.” The alternative is what he calls “on behalf of” access. The agent can act on behalf of a user but does not inherit their full permissions. It gets a scoped token. It can only touch the specific tables and columns it needs. It can only do the operations it was designed to do. If it breaks, the damage is bounded. The audit log is honest. Revocation is surgical. This is not an Immuta problem. This is a security architecture problem that every company building production agents needs to solve right now. Watch the full conversation in the video below. This is the kind of problem that separates the companies shipping agents safely from the ones that are going to have a very bad incident next year. #data #ai #access #security #databricks #api #immuta #theravitshow
August 6 · 15 min
The Enterprise AI Bottleneck Nobody's Talking About
Everyone wants AI in production. Very few are talking about the biggest thing holding it back. I had a great conversation with Matthew Carroll, CEO and Co-Founder of Immuta, at the Databricks Data + AI Summit, and one message came through loud and clear: AI doesn’t scale if people can’t securely access the data they need!!!! We discussed why Immuta has evolved from being known as a data security company to focusing on data provisioning in the AI era. Some of the topics we covered: * Why many AI projects don’t fail because of the model, but because teams can’t get access to the right data at the right time * What it really means for an enterprise to become agent-ready * Why manual data access requests are becoming one of the biggest bottlenecks for AI adoption * And the practical steps data leaders can take today to move from slow approval processes to policy-driven access As more organizations move from AI pilots to production, conversations like these are becoming increasingly important. The full interview is now live. #data #ai #databricks #immuta #theravitshow
August 4 · 16 min
Everyone Talks About AI Models. It's Time to Turn our Attention to Agent Memory and State
I've done 750+ interviews on The Ravit Show. Everyone asks about models. Frameworks. Platforms. Almost nobody asks the question that actually decides whether an AI agent works: where does the memory live? So I sat down with Ed Huang, Co-Founder and CTO of TiDB, powered by PingCAP in Mountain View, and we went deep on the layer everyone is ignoring. A few things from this conversation that stuck with me: → "Memory is the surface, state is the system." Your agent remembering your name is memory. Your agent forgetting what it already tried three steps ago? That's a state failure — and it looks like a dumb agent, even on a frontier model. → Teams stitch together relational + vector + cache + sync pipelines. It works in the demo. It dies at scale. Ed breaks down why collapsing it into one distributed SQL engine matters beyond just "fewer parts." → The laptop-return story: an agent confidently answering from a 2023 policy doc. Better embeddings can't fix it. Ed explains why the retrieval accuracy gap is an architecture problem, not a model problem. → Manus runs 1.2M database clusters — and 99% were created by agents, not engineers. What breaks when your database's "user" is an agent instead of a DBA? Almost everything you assumed. → And the big one: three years out, when everyone has access to the same models, what do AI products actually compete on? Ed's answer — the most reliable memory wins, not the biggest model. If you're building agents, this is the conversation about the layer underneath everything else. Thank you, Ed, for the depth and honesty in this one. #data #ai #agenticai #TiDB #PingCAP #theravitshow
August 3 · 12 min
How AtScale Extends Snowflake Semantic Views to Power BI and Excel
AtScale's latest announcement with Snowflake highlights a reality many organizations are just beginning to realize: AI is only as smart as the business context behind it. That's where the Semantic Layer comes in. What do you think? That was one of the key takeaways from my conversation with Luis Maldonado, Chief Product Officer at AtScale, during Snowflake Summit on The Ravit Show. For years, organizations have struggled with a simple problem: different teams looking at the same data but arriving at different answers. Finance has one definition of revenue, sales has another, and operations has a third. The result is confusion, duplicated effort, and a lack of trust in analytics. The Semantic Layer changes that. It creates a common business language that sits between data and the people, applications, dashboards, and AI systems consuming it. Instead of every team building its own logic and calculations, everyone works from the same trusted definitions. What makes this particularly interesting is the collaboration between AtScale and Snowflake. As enterprises move beyond dashboards and into AI-powered decision making, trusted business context becomes critical. AI systems need more than data. They need to understand what that data actually means. The message from AtScale was clear: the future is not just about storing and processing data. It's about ensuring consistent business definitions across Power BI, Excel, analytics platforms, and AI applications. As AI adoption accelerates, I believe we'll hear a lot more about Semantic Layers. They may very well become the foundation that helps organizations move from AI experiments to trusted AI outcomes. #Data #AI #SnowflakeSummit #Snowflake #AtScale#DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow
July 30 · 10 min
How Qlik and Snowflake Are Powering the Next Generation of Enterprise AI
AI doesn't need more data. It needs more context!!!! That was one of the key themes from my conversation with Josh Good from Qlik at Snowflake Summit on The Ravit Show. As organizations move beyond AI experimentation, the focus is shifting toward governance, trust, and ensuring AI understands the business context behind the data. We also discussed how partnerships between Qlik, Snowflake, and platforms like ServiceNow are helping customers connect data, analytics, and AI into a more unified ecosystem. The future of enterprise AI won't be built by a single platform. It will be built through connected ecosystems working together. #Data #AI #SnowflakeSummit #Snowflake #Qlik #DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow
July 29 · 11 min
How Qlik and Snowflake Are Helping Enterprises Become AI-Ready
Spent a day at Snowflake Summit in San Francisco this week, and one theme came up in almost every conversation: AI is only as good as the data behind it. I had the opportunity to sit down with Andy Iyengar from Qlik on The Ravit Show, and our discussion went beyond AI hype. A few key takeaways: * Modernizing the data estate is no longer optional. Organizations need trusted, governed, and accessible data before they can scale AI initiatives * Moving data into Snowflake is only part of the journey. Data quality, integration, governance, and readiness remain some of the biggest challenges for enterprises * Agentic AI is pushing organizations toward connected ecosystems where data, analytics, and AI work together instead of operating in silos * The expanded collaboration between Qlik and Snowflake reflects where the industry is heading: helping customers accelerate AI adoption by making data easier to trust, manage, and activate What stood out to me most was the focus on outcomes rather than technology. The conversation wasn't about building AI for the sake of AI. It was about creating a foundation that allows organizations to confidently move from experimentation to real business value. #Data #AI #SnowflakeSummit #Snowflake #Qlik #DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow

Showing 1–20 of 22 episodes