The Ravit Show

What happens when the AI SOC becomes more expensive, less private, and harder to control? We talk a lot about AI transforming cybersecurity. But there are some questions that don't get enough attention. Where does AI genuinely add value in the SOC? Does the cybersecurity industry actually have a talent shortage, or a productivity problem? What happens when security teams start paying for every alert and every token? And perhaps most importantly, should sensitive security telemetry really be sent to third-party AI models and cloud providers?I sat down again with Monzy Merza, CEO and Co-Founder of Crogl on The Ravit Show, to unpack these questions and discuss what he saw at Black Hat.We also went deeper into the future of the AI SOC and Crogl's new Sovereign AI SOC, including the trade-offs between AI capabilities, cost, telemetry, and data sovereignty.This is not just a conversation about AI in cybersecurity.It's about what the next generation of the SOC actually looks like.#data #ai #aisoc #soc #cybersecurity #api #crogl #theravitshow

Can AI actually solve alert fatigue, or are we expecting too much from it?Every security team wants faster investigations, fewer false positives, and less manual work.AI promises all of that.But the reality inside the SOC is far more nuanced.In my latest conversation with *Monzy, CEO and Co-Founder of Crogl*, we discuss:* What is actually working with AI in the SOC today* Why alert fatigue continues to overwhelm security teams* Whether AI is reducing the problem or simply changing it* How the role of security analysts is evolving* Where human judgment still matters in an AI-powered SOC* Why Crogl launched a free enterprise-grade AI SOC platformIf you're leading security, building AI products, or simply trying to understand where AI is creating real value in cybersecurity, this conversation is worth your time.#data #ai #security #crogl #theravitshow

I had a blast chatting with Jithendra Vepa, CTO and Co-founder of Observe.AI, on The Ravit Show at MongoDB.local Bangalore. Jithendra has a PhD in speech technology and has spent years deep in speech recognition, NLP, and voice AI. He is not someone who got into AI because it became trendy. He has been building domain-specific AI systems long before the current wave, and it shows in how he thinks about the problem.Here is what we got into.-- We started with Observe.AI itself. What they are building, what problem they set out to solve, and why it matters for enterprises dealing with customer conversations at scale. Observe.AI is a contact center AI platform that helps businesses analyze customer interactions, coach agents in real time, and improve performance across support and sales. More than 300 organizations use it. They process millions of support touchpoints daily.-- That volume is where the database conversation gets real. I asked Jithendra what specifically made MongoDB the right fit for that kind of AI and data workload. When you are running models on millions of unstructured conversations every day, the database decision is not theoretical. His answer was practical and specific.We talked about what changes as enterprises move from AI pilots to real deployment. What MongoDB made easier for the Observe.AI team and for their customers that would have been much harder otherwise. This part is useful for anyone trying to figure out the gap between a working demo and a working product.-- We got into the wins and patterns that have stood out as Observe.AI has scaled. Customer outcomes, operational improvements, how teams are actually using the product once it is embedded. The patterns here tell you a lot about where enterprise AI is actually delivering value versus where it is still a slide deck.-- Jithendra gave the keynote at the event. I asked him what the biggest takeaway he wanted the room to leave with was. His answer came from someone who has built a 40 billion parameter contact center LLM and trains domain-specific models instead of relying on generic ones. That distinction matters more than most people realize.-- We closed on the signal versus hype question. His advice for founders and enterprise teams trying to decide where to place their bets right now was grounded in years of shipping, not months of experimenting.A few things stayed with me.Generic AI is not enough for enterprise. Domain-specific models built on domain-specific data is where the real moat lives.The companies winning in AI are not the ones with the most models. They are the ones with the most structured access to the right data at the right moment.Contact centers are one of the first places where AI is delivering measurable ROI at scale. What is happening there is a preview of what is coming for the rest of the enterprise.#data #ai #mongodb #mongodblocal #theravitshow

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

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 attentionIt was a practical conversation with someone who is building every day, not just talking about AI.#data #ai #mongodb #mongodblocal #theravitshow

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

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

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

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

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 transformationIf 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

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 executionThis 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

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 outcomesIt 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

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 monthsThe 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

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 AIA 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

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 metricI 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

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 forwardIf 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

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 homeHis 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

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

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 accessAs 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

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

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

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

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 activateWhat 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

What happens when enterprise data lives everywhere, but AI needs a single source of truth? That was the focus of my conversation with Mark Lyons from Cloudera at Snowflake Summit on The Ravit Show. As enterprises continue to embrace AI, many are navigating increasingly complex hybrid and multi-cloud environments. The challenge isn't collecting more data. It's making data accessible, governed, and usable across the entire organizationWe also discussed why open architectures and interoperability are becoming so important. Customers want flexibility, not lock-in. They want to leverage the best technologies while maintaining a strong foundation for analytics and AIThe Cloudera and Snowflake partnership is focused on helping customers do exactly that, creating a path toward trusted data, faster innovation, and better business outcomes.#Data #AI #SnowflakeSummit #Snowflake #Cloudera #DataAI #EnterpriseAI #HybridCloud #MultiCloud #TheRavitShow

AI is forcing companies to rethink assumptions they’ve had for years. One cloud provider. One place for data. One approved set of tools. That world is changing fast. At Cisco Live, I sat down with Arun Dev from Equinix to discuss what enterprises are getting right and wrong as they scale AI.A few themes stood out:* AI is pushing organizations beyond a single cloud strategy and into a much more connected ecosystem.* As AI becomes part of operations, trust becomes critical. Just because an answer sounds right doesn’t mean it is.* The pace of innovation is so fast that companies can’t afford to rebuild infrastructure every time a new model is released.* Employees are already using AI tools. The challenge isn’t stopping them. It’s creating the right guardrails around security, governance, and cost.* Data is no longer living in one place. As AI workloads spread across clouds, data centers, and edge environments, networks are becoming a strategic asset.One thing that really resonated with me:The AI conversation is often about models. But the bigger challenge may be building an architecture that can adapt as models, data, and business needs continue to evolve.Great conversation with Arun on the realities of enterprise AI adoption and what leaders should be thinking about today.#data #ai #ciscolive #equinix #observability #api #agents #theravitshow

Everyone wants enterprise AI. Very few are talking about where the data lives, who controls it, and how it stays secure. At Cisco Live, I sat down with Rajeev Khanolkar, Chief Strategy Officer at Gruve, to discuss the launch of PulseAI Platform and why the future of enterprise AI may look very different from the public AI services dominating today's conversation.A few key takeaways:* Many organizations are still struggling to move beyond AI experimentation because security, governance, and data control remain unresolved* Enterprise AI cannot be a one-size-fits-all model. Different workloads require different deployment approaches depending on where data resides* The ability to run AI on-premises, in the cloud, or across hybrid environments is becoming increasingly important as enterprises balance innovation with compliance and risk* Security can no longer be bolted on after deployment. It needs to be part of the AI architecture from the start* Pre-integrated platforms can significantly reduce the time and complexity required to move AI initiatives into productionOne point that stood out to me:The AI race isn't just about building better models. It's about giving enterprises the confidence to use AI with their most valuable data while maintaining control, security, and flexibility.Great conversation with Rajeev on private AI, enterprise security, and what organizations should be thinking about as they scale AI adoption.#data #ai #ciscolive #gruve #observability #api #agents #theravitshow

Everyone wants AI in production. Very few organizations know how to get there. At Cisco Live, I spoke to Tarun Raisoni, CEO & Co-founder of Gruve, to discuss the launch of PulseAI Platform and what enterprises are missing in their AI journey.A few takeaways from our conversation:-- The challenge is no longer experimenting with AI. The challenge is operationalizing it at scale-- Enterprises need more than models. They need the infrastructure, governance, security, and workflows required to move AI from pilots to production-- No single vendor can solve the AI stack alone. Ecosystems matter. Partnerships between infrastructure, networking, and AI providers are becoming a competitive advantage-- While consulting can help define a strategy, enterprises ultimately need repeatable platforms and operating models that can deliver business outcomesOne point that stood out to me:The winners in AI may not be the organizations with the most pilots. They may be the ones that build the right foundation to scale AI across the business.Great conversation with Tarun on where enterprise AI is headed and why infrastructure is becoming a bigger part of the AI discussion.#data #ai #ciscolive #gruve #observability #api #agents #theravitshow

Most organizations are focused on deploying AI. But is their network ready for it? At Cisco Live, I sat down with Anurag Dhingra, SVP & GM, Enterprise Connectivity and Collaboration at Cisco on The Ravit Show, to discuss what it really takes to build an AI-ready enterprise.A few key themes from our conversation:* AI is increasing the demands on enterprise networks in ways traditional architectures were never designed for.* Organizations need networks that can operate, secure, and adapt at machine speed as AI workloads continue to grow.* Managing infrastructure across data centers, multiple clouds, and edge environments remains a major challenge for enterprise teams.* Simplifying connectivity is becoming just as important as improving performance.* The next evolution of networking is not just supporting AI workloads. It's using AI to operate, optimize, and secure the network itself.One insight that stood out:There's a big difference between adding AI to an existing network and building a network designed for an AI-first world.As AI becomes embedded across the enterprise, networking is moving from being a supporting function to a strategic foundation.Great conversation with Anurag on the future of enterprise connectivity, multicloud networking, and AI-driven operations.#data #cisco #ciscolive #ai #theravitshow

For years, IT teams have been forced to manage growing complexity with more tools, more dashboards, and more manual effort. What if AI could help bring all of that together? At Cisco Live, I sat down with DJ Sampath, SVP & GM, AI Software and Platform at Cisco The Ravit Show, to discuss Cisco Cloud Control, AI Canvas, and how AI is changing the way IT teams operate.A few key insights from our conversation:* Operational fragmentation continues to be one of the biggest challenges for enterprise IT teams* Cisco Cloud Control is focused on providing a more unified way to manage increasingly complex Cisco environments* AI Canvas is designed to be more than an assistant. It introduces an agentic workspace where people and AI can work together to solve problems* Some IT challenges are too complex for a single tool or a single person. A collaborative, multiplayer approach can help teams move faster and make better decisions* The future of IT operations may be less about navigating dashboards and more about orchestrating outcomes with AI-powered systemsOne thing that stood out to me:The conversation around AI is shifting from answering questions to helping teams take action. That's a very different future than the one many organizations are planning for today. Great discussion with DJ on what AI-native platforms could mean for enterprise operations over the next few years.#data #cisco #ciscolive #ai #theravitshow

Most AI agent conversations start with what the agent can do. Very few focus on how you manage, monitor, and trust those agents once they're in production. At Cisco Live, I sat down with Kamal Hathi, SVP & GM of Splunk at Cisco on The Ravit Show, to discuss what enterprises need beyond AI models to make agents reliable, secure, and trustworthy.A few key takeaways from our conversation:* Moving AI agents from demos to production requires visibility into how they operate, make decisions, and interact with enterprise systems.* As organizations deploy more agents, observability becomes critical. Without it, AI can quickly become a black box.* Data remains one of the biggest challenges. Enterprises are looking for ways to reduce tool sprawl while maintaining a unified view across their environments.* Security and observability are no longer separate conversations. The faster teams can connect operational issues with security events, the faster they can respond.* Making AI accessible is important, but governance cannot be an afterthought. Innovation and control must go hand in hand.One thing that stood out to me:The future of AI isn't just about building smarter agents. It's about creating the trust, visibility, and governance needed to operate them at enterprise scale. Great conversation with Kamal on the next phase of enterprise AI and the role observability will play in making it successful.#data #cisco #ciscolive #ai #theravitshow

Everyone is talking about bigger AI clusters. What happens when those clusters need to span multiple data centers? At Cisco Live, I sat down with Rakesh Chopra, SVP & Fellow, Common Hardware Group at Cisco on The Ravit Show, to discuss one of the less talked about challenges in AI infrastructure: scaling AI beyond a single data center.A few key themes from our conversation:-- The industry is moving from scale-up and scale-out to scale-across architectures-- Connecting GPUs across data centers is becoming a critical challenge as organizations build larger AI environments-- Power efficiency is now as important as raw performance, driving innovation in silicon and optics-- Network reliability and low-latency communication are essential as AI clusters stretch across geographic boundaries-- Co-designing networking, silicon, and optics is becoming a requirement rather than an optimizationThe AI conversation often focuses on models.But the real story may be the infrastructure required to make those models work at scale.#CiscoLive #AI #Networking #Innovation #TheRavitShow

For years, data engineering has been about building pipelines, warehouses, dashboards, and choosing the right tools. But what if we've been solving the wrong problem? I recently sat down with Sai Sundar from WALT, who has spent decades building data platforms at Apple, Yahoo, LinkedIn, Chime, and GEICO. One idea from our conversation really stood out. Companies don't need more data tools. They need better business outcomes.Sai explained how data teams often work in silos. Engineers build pipelines. Business teams ask questions. Analysts sit in the middle translating requirements. The result is slow decisions, duplicated work, and endless back-and-forth.The next evolution isn't another platform.It's creating systems that understand business goals, work with your existing data stack, and help organizations make trusted decisions faster.Some of the topics we covered:* Why data has historically been treated as a second-class citizen* Why business outcomes matter more than adopting the latest technology* How AI is changing the role of data engineering* Why trust and transparency are becoming essential in enterprise AI* What the future of conversational data engineering could look likeThis conversation isn't just about AI.It's about rethinking how data teams create value for the business.#data #ai #dataengineering #walt #theravitshow

Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show.He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI.His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff.Ranjith calls this the chasm. He left to close it.A few things from our conversation that stuck with me.Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering.Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and production.Tribal knowledge should not live in one person's head. Why you exclude Q2 returns should not walk out the door when an analyst quits. It should live in the system.And data quality is where most data projects quietly die. You can build the most elegant pipeline in the world, but if one number is wrong, trust is gone. Once trust is gone, nobody uses the platform.The part I keep thinking about. Tools give you capability. They do not give you the outcome. The outcome still takes people and months of work. That gap is the real problem, and it is the one Ranjith is now building to solve.Worth your time if you care about where data engineering is heading.#data #ai #dataengineering #walt #theravitshow

PostgreSQL is no longer just a database conversation. It's becoming a platform conversation. I had the opportunity to sit down with Claire Giordano, Principal Group PM Microsoft near Stanford University right after POSETTE: An Event for Postgres to discuss the biggest takeaways from one of the largest PostgreSQL events in the world.A few themes stood out:* PostgreSQL adoption continues to accelerate across organizations of every size* The ecosystem around Postgres keeps expanding, making it easier to build modern data and AI applications* AI was impossible to ignore, but the conversation wasn't about replacing databases. It was about how databases can provide the context, reliability, and foundation AI systems need* The community remains one of PostgreSQL's biggest strengths, with contributors and companies working together to push innovation forwardOne of the most interesting parts of our discussion was where PostgreSQL goes next.As organizations look to build AI-powered applications, support real-time workloads, and simplify their data architectures, PostgreSQL continues to find itself at the center of those conversations.The database landscape keeps evolving, but PostgreSQL's momentum shows no signs of slowing down.In this episode, Claire shares:* Her biggest takeaways from POSETTE 2026* The PostgreSQL trends generating the most excitement* Surprising announcements and discussions from the event* How AI is influencing the PostgreSQL ecosystem* What this year's event tells us about the future of PostgreSQL* What the community should be paying attention to next#data #ai #postgresql #database #opensource #theravitshow

Everyone wants better AI models. A few days back at Data Citizens on the Road by Collibra, I sat down with Reece Griffiths, Field CTO at Collibra on The Ravit Show, to discuss one of the biggest challenges facing enterprise AI today: unstructured data.For years, data governance focused primarily on structured data.But AI is changing the game.Today, enterprise knowledge lives across PDFs, presentations, images, documents, emails, and shared drives. If that content isn't properly governed, AI systems can quickly run into problems:* Generating answers from outdated or draft documents* Exposing sensitive information due to missing confidentiality labels* Missing relevant content because of poor metadata and classificationOne concept from our discussion really stood out:Knowledge decay.Even the most advanced AI models will struggle if the underlying knowledge base is stale, incomplete, or poorly maintained.We also discussed why enterprises are moving toward unified semantic models that connect structured and unstructured data, allowing AI systems to understand business context consistently across the organization.The takeaway?The future of enterprise AI won't be determined solely by model performance.It will be determined by the quality, freshness, and governance of the data behind it.#Data #DataCitizens #Collibra #AI #GenerativeAI #DataGovernance #AIGovernance #EnterpriseAI #Metadata #DataManagement #TheRavitShow

What if the biggest obstacle to AI success isn't the technology? It's the way organizations are structured. At Data Citizens on the Road by Collibra, I sat down with Joyce Snelders Senior Manager at Deloitte on The Ravit Show to discuss what organizations are experiencing as they move from AI experimentation to enterprise-wide adoption.A few key takeaways from our conversation:* Data governance has gone from a "nice to have" to a business priority because AI is only as good as the data behind it.* Many organizations are building AI agents without common standards, creating duplicate efforts and inconsistent outcomes across teams.* Chief Data Officers are increasingly becoming AI leaders, taking responsibility for both data and AI strategies.* The next phase of enterprise AI is not just about technology. It is about governance, operating models, and change management.* Leaders should start preparing for a future where digital FTEs work alongside human employees.One statement from Joyce stood out:Organizations don't have an AI problem. They have a governance and operating model problem.The companies that solve that challenge first will be the ones that scale AI successfully.#DataCitizens #Collibra #AI #DataGovernance #AIGovernance #EnterpriseAI #DataLeadership #TheRavitShow

Everyone is talking about AI governance. Almost nobody is talking about the part that actually decides whether it works. I had a blast chatting with Gaurav Bhandari, AVP and Head of Data and Analytics consulting at Infosys, on The Ravit Show at Data Citizens on the Road by Collibra. One line stuck with me. Roughly 80% of AI governance is just governing the data that feeds your models. We have been here before. Data governance started as a compliance and privacy problem in regulated industries. Then data became the asset everyone wanted to mine for value. Now AI has raised the stakes again, because a model is only as good as the context behind it.Gaurav broke that context down into five things every enterprise has to get right:- Trust. Can you rely on the output.- Ethics. Even when you trust it, is it the right answer to put in front of people.- Regulations. Are you staying compliant as the rules keep shifting.- Privacy. Do people still control their own data.- Security. Is everything safe once it sits inside your workflow.Miss one of these and your AI agents are running on shaky ground.What stood out to me was how the Infosys and Collibra partnership fits this moment. Ten plus years working together, and not just in finance. Retail, manufacturing, life sciences too. Collibra brings the platform. Infosys weaves the policies, controls, and structure into one governance story instead of a pile of disconnected tools.His advice for the next 12 months was refreshingly simple. Stop thinking about data governance. Start building data plus AI governance.The companies that treat these as one problem will move faster than the ones still treating them as two.Full interview is live now.Follow The Ravit Show for more conversations from across the Data and AI world, and subscribe to the newsletter to stay ahead.#data #ai #collibra #governance #infosys #api #datacitizen #theravitshow

Most enterprise conversations around AI start with models, copilots, and agents. This conversation started somewhere else: the data foundation. Last week at Informatica World, I had the opportunity to sit down with Martí Ganduxé Pregona from Schneider Electric and Emilio Valdés from Informatica to discuss what it really takes for enterprises to move from AI experimentation to AI at scale on The Ravit Show!!!!One theme came up repeatedly throughout our discussion:AI is only as good as the data behind it.We explored how the combination of Informatica and Salesforce is expanding the role of data management beyond traditional integration and governance into areas such as agent governance, workflows, and APIs.We also talked about one of the most talked-about announcements from the event: Informatica Headless.The idea is simple but powerful. As enterprises deploy more AI agents, they need a trusted layer that ensures those agents are working with accurate, governed, and compliant data.A few insights from the conversation:* Why trusted data is becoming the foundation of every AI strategy* How enterprises are preparing for an agent-driven future* Why data governance is becoming more important, not less, in the age of AI* The growing need to balance innovation speed with compliance and security requirements* What enterprise leaders are learning from one another as they navigate AI transformationOne thing was clear: the future isn't just about building smarter AI.It's about building an organization that can trust the outputs AI produces.The full interview is below.#data #ai #InformaticaWorld #theravitshow

One of the most interesting conversations I had at Informatica World was with Theodora Bakker, Vice President of Data at Hearst, and Gaurav Pathak, SVP & GM Product Management, DGP and AI at Informatica/Salesforce on The Ravit Show.What stood out to me was how practical this discussion was.We talked about why enterprise leaders continue to bring Informatica IDMC into multiple organizations across industries, what actually makes a company “AI-ready” versus truly “AI-leading,” and how the new Headless announcements could change the way teams think about modern data architectures.Theodora shared a strong perspective from the customer side, especially around scaling data foundations across very different environments. Gaurav also broke down how Informatica is thinking about the next phase of AI and enterprise data management.A few key themes from the conversation:* Why strong data foundations still decide whether AI initiatives succeed or fail* The difference between experimenting with AI and operationalizing it at scale* How enterprises are thinking about flexibility, governance, and modernization with Headless capabilities* What enterprise leaders should prioritize right now to move from AI-ready to AI-leadingIf you’re working in data, AI, analytics, governance, or enterprise architecture, this is a conversation worth watching.#data #ai #InformaticaWorld #theravitshow

Breaking right from Informatica World 2026!!!! Rahul Auradkar, President & GM, Data & Context | AI Foundations,, Salesforce just came off the keynote stage and joined me on The Ravit Show to break down everything that was announced today around:- Headless Data Management- Trusted enterprise context for AI- Agentic AI workflows- Multi-cloud interoperability- The future of enterprise data architectureOne thing that stood out from our conversation:Enterprise AI is no longer just about building models. It is now about building trusted systems that AI agents can actually operate on.We also discussed why metadata, governance, and interoperability are becoming the foundation for the next generation of AI systems across enterprises.A lot of important insights in this one.#data #ai #InformaticaWorld #theravitshow

Are dashboards becoming irrelevant in the age of Agentic AI? I recently sat down with Clarence Rozario from Zoho on The Ravit Show for an in-depth conversation on one of the biggest shifts happening in Data & AI right now: Agentic Analytics!!!!For years, business intelligence has focused on helping people understand what happened. Now we're entering a new era where analytics can help recommend actions, support decisions, and even automate parts of business workflows.In this conversation, we explored:* How BI has evolved from reporting and dashboards to Agentic Analytics* Why enterprises are shifting from insights to outcomes* Whether dashboards still have a role in the AI era* How Agentic AI is changing decision-making inside organizations* Why Context Engineering may become one of the most important capabilities for enterprise AI* The growing importance of semantic layers, business context, and trusted data foundations* Why Data & Analytics platforms must evolve to support agentic systemsOne theme stood out throughout our discussion:AI is only as good as the context and data foundation behind it. Without the trusted business context, even the smartest agents will struggle to deliver reliable decisions.What role do you think dashboards will play in a world increasingly driven by AI agents?#data #ai #agentic #ai #dashboards #api #semanticlayer #theravitshow

Some conversations stay with you because there is no hype in them, just real answers. That is how my interview on The Ravit Show with Thomas Benjamin, SVP of Product Development and Engineering at Boomi, felt at Boomi World 2026.Thomas was clear about why most companies cannot get past their first pilot. They treat scaling as a model problem, when the real issue is everything underneath. The data. The context. The way agents are governed. That is where pilots quietly fall apart.We also talked about what agentic context actually means inside a real enterprise. Thomas explained it in a way that made it obvious why putting AI on top of messy data will keep giving you unreliable answers, no matter how strong the model is.The part I keep thinking about was on partnerships. No single company owns the full stack today. Thomas was honest about what makes a partnership real versus what makes it just a logo on a slide. That difference matters more than most people admit.My takeaway. The winners in this next phase will not be the ones with the flashiest agents. They will be the ones who took the time to get the boring layers right.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

I learn the most from people who can explain hard things simply, and my conversation on The Ravit Show with Patricia Moore, AI Field CTO at Boomi, was one of those at Boomi World 2026.Patricia was direct about why so many AI agent pilots stall. Most teams rush to deployment before doing the work on context, data readiness, and governance. That is the gap between the enterprises getting real value and the ones still running experiments.We spent real time on context engineering. Everyone uses the phrase, but very few can explain it. Patricia made it obvious why context is not just another feature. It is what decides whether an agent can be trusted inside a real business. The same thinking applies to hallucinations. The fix is not bigger models. It is better grounding, cleaner data, and tighter checks around the agent.The part I enjoyed most was her view on the shift happening inside enterprises moving from experiments to real outcomes. The leaders getting it right are not chasing AI for the sake of AI. They are tying every initiative to outcomes that actually matter.My takeaway. The companies winning with agents are treating context, governance, and outcomes as the real product. The model is just one part of the story.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

At Boomi World 2026, I spoke with the amazing Nicole Bradley from Amazon Web Services (AWS) for a conversation on The Ravit Show, and it kept coming back to one idea. Most enterprises are not failing at agentic AI because of the models. They are failing because they are trying to stand up data management and agents without the right partnership underneath!!!!Nicole walked me through the patterns AWS is seeing across customers right now, why Boomi became the partner that made sense, and the use cases where this combination is genuinely hard to beat. We also talked about the roadmap for the next 12 months, and there is a lot coming that customers should be paying attention to.The line that stuck with me. Customers do not need more tools. They need fewer broken seams between them.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

One of the sharpest architecture conversations I had at Boomi World 2026 on The Ravit Show was with Kenneth Maglio, Principal Architect at World Wide Technology. His view on agentic AI was refreshingly honest. Can't wait to do this again!!!!A lot of teams are still debating whether to prioritize data management or agentic AI. Ken's answer was simple. That debate is the problem. If you separate them, you end up with agents that look impressive in a demo and fall apart in production.We talked about what his team was spending too much time on before Boomi, and how much of that work was not moving the business anywhere. The bigger shift was around data freshness. Ken made the case that this is the single biggest factor in whether an agent can actually be trusted. Stale data is not a small issue. It is the difference between a system that scales and one that quietly erodes confidence across the business.We also got into the measurable outcomes WWT has seen since adopting the Boomi architecture, and what would break if that layer was removed. His answer made it clear how foundational this has become.The takeaway for me. Agentic AI in the enterprise will not be won by whoever has the best model. It will be won by whoever has the cleanest, freshest, most governed data feeding those agents in real time.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

Quick conversation on The Ravit Show from Boomi World 2026 with John Baker, CIO and CISO at Lexitas. One of the most grounded customer perspectives I have heard this year. Thanks for the amazing insights, John :)John was clear about why Lexitas refused to treat data management and agentic AI as separate projects, what his team stopped wasting time on after Boomi, and why agent governance is the part most enterprises underestimate. Agents are only predictable when the layer beneath them is.My takeaway. The architecture decision is the AI decision.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

Enterprise software is changing. I sat down with Brian Landsman, CEO of AgentExchange at Salesforce, to talk about what an agent-first future actually looks like. #salesforcepartnerThis wasn’t a surface-level conversation. We went deep into what’s coming next.Here’s what stood out:* AgentExchange evolved from a marketplace directory into a commerce and discovery layer, is rethinking how enterprises deploy software* Headless architectures could fundamentally reshape how people interact with enterprise systems since traditional UIs matter less* Agents are moving from assistants to becoming the primary interface* Workflows need to be redesigned from the ground up for an agent-first world* Success will be defined by agents executing end-to-end tasks, not just supporting humans* The gap between AI pilots and production is finally starting to closeWe also discussed how individuals can go to market faster with $50M AgentExchange Builders Initiative.Watch the full conversation and let me know what you think.#data #ai #tdx26 #salesforce #workflows #api #headless360 #agentexchange #apps #theravitshow

Some conversations stay with you for days. This one did. Last week at Team '26 in Anaheim, I spoke to my favourite Tamar Yehoshua, Chief Product and AI Officer at Atlassian. A week later, I'm still thinking about three things she said.Here's the thing about Tamar. I always learn something new every time we talk. She's one of those rare leaders who can zoom from a product detail to a 5-year vision in the same breath without missing a beat.What makes her perspective so useful: Tamar has shipped product at Google Search, led product at Slack through their tenfold growth and IPO, and ran product and technology at Glean. Three different eras of how knowledge workers find what they need at work. And now she's leading Atlassian's AI strategy at the moment the entire category is being redefined.Team '26 was her first Team event as CPO and AI Officer. You could feel the weight of that moment in the room.Here's what we got into:- Day one through her eyes. What it actually felt like to walk on stage as the new CPO and announce the biggest set of AI launches in Atlassian's history.- The connective thread. Atlassian covered massive ground in the keynote. AI for developers, service teams, product teams, agents in Jira. I asked Tamar how she wants people to think about Atlassian's AI strategy as one story instead of five. Her answer reframed the whole keynote for me.- How customers are actually using Rovo. Not the marketing version. The real version. What's working, what's surprising, where the patterns are forming.- The shifts that matter. Tamar has lived through search becoming the default interface, then SaaS becoming the default workplace, then chat-based collaboration becoming the default for distributed teams. I asked what excites her most about this moment. Her answer wasn't what I expected.- The next 5 years. How teams will actually work differently. Not predictions. Patterns she's already seeing inside Atlassian's own teams.The throughline across everything she shared: context is the moat. Models will keep getting better and cheaper. What separates the winners is what your AI knows about how your company actually works.Big thank you to Tamar for the time and the candor, and for being so generous with her thinking every time we connect. And to the Atlassian team for hosting me at Team '26.#data #ai #atlassian #team26 #theravitshow

300 to 600 hours reclaimed every single week. Ticket creation cut by 75%.These are not projections. This is what DocuSign is actually seeing from Atlassian's Rovo rollout right now.New episode of The Ravit Show is live with Shivi Singh Verma, MBA, PMP®, CSM®, PMI-ACP®, ITIL®, Senior Manager of Engineering at Docusign, recorded at Team '26 in Anaheim.If you have been waiting for an enterprise AI deployment story that goes past pilots and demos, this is the one to watch.Shivi leads GenAI and AI Agentic strategy at DocuSign. They have actually done the hard work most companies are still talking about. Phased rollout, real guardrails, measured ROI, and a clear plan for what comes next. Their philosophy on this is sharp: adopting AI at scale requires foundational trust, robust governance, and clear guardrails. Not optional, not later, on day one.What we got into:- The tipping point. What finally convinced DocuSign to move forward with Rovo. There is a specific moment Shivi described that I think every engineering leader weighing this decision needs to hear.- The phased rollout. What the pilot looked like, what surprised Shivi as they expanded beyond it, and the guardrails they put in place that they would recommend to other enterprises starting today. This is the playbook section.- How they actually measured ROI. Most companies struggle to prove AI value to leadership. DocuSign did not. I asked Shivi how they measured the 300 to 600 hours weekly and the 75% ticket reduction, and what convinced their leadership these gains were real and sustainable. The answer is more disciplined than I expected.- What comes next. DocuSign is planning to let non-technical teams build their own governed agents through Rovo Studio, and shift from reactive AI to proactive AI. We spent time on what that future looks like, and what they are doing now to prepare for it.- The line from Shivi that stayed with me: AI at enterprise scale is not a model problem. It is a trust problem. Get the governance right first and the productivity gains follow. Skip that step and the project will not survive its first incident.If you are an engineering leader, a CIO, or anyone trying to build the business case for enterprise AI inside your own company, watch this one. Shivi gives you the playbook.Big thank you to Shivi for the openness about what worked and what was harder than expected. And to the Atlassian team for the front-row access at Team '26.#data #ai #atlassian #team26 #theravitshow

I had a blast chatting with Sherif Mansour, Head of AI at Atlassian, at Team '26 in Anaheim. If you want to understand what Atlassian actually shipped this year and why it matters, this is the conversation to watch.Sherif is the person inside Atlassian who has been thinking about AI longest and hardest. He runs Atlassian Intelligence, the generative AI platform that powers Rovo, the Teamwork Graph, and the agent experiences across Jira, Confluence, and Loom. When the entire company stage talks about AI for two hours, Sherif is one of the people who actually built what they are talking about.That made this conversation different from most AI interviews you will hear this year.What we covered:The keynote in his own words. Atlassian announced AI for developers, service teams, product teams, agents in Jira, and a brand new Product Collection. I asked Sherif what excites him most across all of it. His answer surprised me.Teamwork Graph, opened up. The 150 billion connection graph is now accessible to any agent through MCP, CLI, and Forge connectors. I asked Sherif what "opening it up" actually means in practice, and what changes for builders outside Atlassian who want to plug in.Agent orchestration in Jira. What it looks like when an agent is not just answering questions but coordinating work across an entire project. Sherif walked through how Atlassian thinks about keeping humans in the loop where it matters, and where to get out of the way.AI mythbusting. Sherif came in with strong opinions on the myths he is tired of hearing. We spent real time here. If you work in or around enterprise AI, this section alone is worth the watch.The line that stayed with me: the hardest problem in enterprise AI is not making models smarter. It is making them aware of how your company actually works. Everything Atlassian shipped at Team '26 traces back to that one bet.Big thank you to Sherif for the depth, the candor, and the patience with my follow-up questions. And to the Atlassian team for the front-row access at Team '26.#data #ai #atlassian #team26 #theravitshow