The Ravit Show

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

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

What happens when the database becomes an active participant in AI applications instead of just a place to store data? In this session of The Ravit Show, I sat down with Jay Gordon and Patty Chow to unpack the biggest announcements and takeaways from CosmosDB Conf!!!!One theme stood out throughout the conference:AI is not just changing applications. It's changing the database itself.We discussed:- How OpenAI scales from zero to millions of queries per second- Why Walmart relies on globally distributed architectures to keep checkout systems running during failures- How vector search, full-text search, and hybrid search are becoming native database capabilities- The rise of agent memory architectures and AI-native applications- Why developers need real-time visibility into query costs- How to think about CosmosDB vs Azure DocumentDB based on workload requirements- What the Azure CosmosDB Agent Kit means for developers building AI-powered systemsOne of my biggest takeaways was that retrieval is increasingly moving into the database layer itself. Instead of stitching together multiple services, developers can now work with a more unified approach to search, AI, and data.If you're building AI applications, working with data infrastructure, or trying to understand where databases are headed next, this conversation is worth watching.The full interview is now live.What was your biggest takeaway from CosmosDB Conf this year?#data #ai #azure #cosmosDB #microsoft #api #microservices #theravitshow

BREAKING from Rubrik!!!! They just made the most aggressive bet I have seen on where enterprise security is heading. I interviewed Anneka Gupta, their Chief Product Officer, right as it all went public at Rubrik FORWARD on The Ravit Show.Two announcements came out of Las Vegas this week.First, Rubrik AI. The platform itself is now an agent. You define the outcome, recover clean, contain the blast radius, restore the business, and Rubrik AI reasons over your data, identities, and deployed agents to deliver it. Recovery sequences that took human teams weeks now finish in minutes. Every action stays auditable, attributable, and reversible.Second, Rubrik Agent Cloud for Anthropic's Claude Code and Claude Cowork. Claude is being adopted faster than any agentic technology Rubrik has seen. These agents write, push, and deploy code on their own, while enterprise security was built assuming a human stays in the loop. RAC closes that gap with real-time governance through SAGE and the industry's only Agent Rewind, which reverses an agent's actions and recovers the codebase even when a mistake outruns version control.Here is why these two launches are really one story.Rubrik's Zero Labs research found 86 percent of firms expect AI agents to outpace their existing security capabilities. Most vendors respond to that stat by selling more visibility. Rubrik's answer is different: if threats and agents move at machine speed, defense and recovery have to move at machine speed too. So they built an agent to protect you from agents.That framing is what I pushed Anneka on in our conversation.We got into what a runaway AI risk actually looks like inside a security environment, and how Agentic Guardrails stop one before it spreads. Which parts of a multi-week recovery workflow are genuinely automated and which still need a human call. How one agent reasons across Rubrik Security Cloud and Rubrik Agent Cloud at the same time, spanning data, identity, and third-party agents. The role of identity in agentic resilience. And why Databricks Unity Catalog was chosen as the first native lakehouse integration for RAC, with more connectors coming.My take after 750 plus interviews in this space: every enterprise I talk to is racing to deploy agents, and almost none of them can answer one question. What happens when an agent does something wrong?Observability tells you what happened. Rewind lets you undo it. That difference is going to define the next phase of enterprise AI, because the companies that win with agents will not be the ones that deployed fastest. They will be the ones that stayed in control.#data #ai #cybersecurity #theravitshow

Most teams think they have an AI strategy. But what they actually have… is fragmentation. At Atlassian Team ‘26, I sat down with Molly Sands, PhD, Head of Teamwork Lab on The Ravit Show to talk about what's really happening inside teams today.Her work at the Teamwork Lab is different. They're not just studying the future of work. They're actively testing how AI changes the way teams operate.A few takeaways that stood out:– The biggest problem isn't lack of AI tools. It's the “AI fragmentation tax.”Too many tools, not enough alignment.– Top teams are not just adopting AI.They're redesigning how they set goals, collaborate, and make decisions.– A lot of “work about work” still exists.Status updates, coordination, chasing context. This is where AI should be making the biggest impact.– The best approach to AI adoption is not top-down mandates.It's embedding AI into everyday workflows so teams naturally use it.One thing I appreciated… Even at Atlassian, there isn't a perfect answer yet.And that's the point. This is still being figured out in real time.If you're thinking about AI for your team, don't start with tools. Start with how your team actually works.#data #ai #team26 #atlassian #theravitshow

Can Postgres become the foundation for the next generation of AI applications? As we get closer to POSETTE 2026 by Microsoft in partnership with AMD, I sat down with Charles Feddersen from Microsoft for a curtain raiser conversation about one of the most important events in the Postgres community.Over three days, POSETTE 2026 will bring together 50 speakers and 44 sessions, covering everything from the future of Postgres to its growing role in AI, vector search, and modern application development.In our conversation, we discussed:* Why Postgres continues to gain momentum across the industry* What many people still underestimate about Postgres and AI* The evolution beyond pgvector and RAG* The sessions and speakers Charler is most excited about* How POSETTE creates value for both beginners and Postgres expertsIf you work with data, AI, analytics, or application development, this is a conversation you won't want to miss.#Data #AI #Postgres #POSETTE2026 #AI #DataEngineering #Database #OpenSource #TheRavitShow

Most AI tools are still just indexing documents. The Teamwork Graph has 150 billion connections. That difference is the whole game. I sat down with Jamil Valliani, the Head of AI Product at Atlassian, during Team '26 on The Ravit Show to understand why they are betting the next decade on this approach. Twenty years in Search before this role. Long before vector databases were trendy. Long before RAG was an acronym.A few things we got into:What the Teamwork Graph actually is. Why this architectural choice separates Atlassian's AI from everything else in the market.150 billion connections vs document indexing. Most enterprise AI tools search your text. This connects people, work, decisions, and outcomes across systems. The gap is bigger than I realized.Why connected data wins on accuracy. Atlassian's internal benchmark: 44% more accurate results using 48% fewer tokens. We broke down what is actually happening under the hood.A Search veteran's read on this moment. What makes this AI shift different from every other one. The most grounded take I heard at any conference this year.The line that stayed with me: in the next era of work, the company with the best context will win. Not the company with the best model. Models are getting commoditized. Context is not.If you work on retrieval, RAG, or graph-based AI inside an enterprise, this one is for you.#data #ai #atlassian #team26 #theravitshow

At SAS Innovate, I had the chance to speak with Reggie Townsend about one of the most important topics in AI right now.Trust. What stood out from our conversation is how the market is evolving. Enterprises are moving quickly with AI, but the real challenge is no longer building models. It is ensuring visibility, transparency, and control as these systems start influencing real decisions.We also discussed SAS' new AI Navigator and why it matters. It is not just another layer. It is a way for organizations to understand where they are in their AI journey and how to move forward in a structured, governed way. This becomes critical, especially in industries where accountability is not optional.My biggest takeaway.If trust does not scale, AI will not scale.Learn more from the interview below!!!!#data #ai #SASInnovate #SASVisionary #theravitshow

From SAS Innovate, continuing conversations with leaders shaping how enterprise AI actually gets deployed. I spoke to Marinela Profi from SAS and this one cut straight to where the industry really is right now.There's a lot of excitement around AI. But most enterprises are still not ready to scale it. We talked about why. The gap is no longer about models. It's about systems, governance, and how AI fits into real enterprise workflows.One of the most interesting parts of the discussion was around MCP (Model Context Protocol) inside SAS Viya. This is about giving AI systems the right context, control, and structure so they can operate reliably in production environments.Because without that, AI stays stuck in experimentation. We also went deep into why SAS is building dedicated agent infrastructure instead of just layering AI on top of existing tools. That decision matters.It allows enterprises to move faster, while still maintaining control, auditability, and trust. That balance is what most organizations are struggling with today.My biggest takeaway. The industry is moving from generative AI experimentsTo governed, production-ready intelligence. And that shift requires a completely different approach to architecture.#data #ai #SASInnovate #SASVisionary #theravitshow

Last week at SAS Innovate, I spoke with Dan Soceanu about something every enterprise is talking about, but very few have solved. AI-ready data.The conversation was very practical. AI needs data, but not just more data. It needs data that is trusted, governed, and fit for purpose, especially as automation and agents start making decisions. We also went into digital sovereignty, which is becoming a key concern. Organizations are thinking deeply about where their data lives, how it is controlled, and how it aligns with regulations across regions.What stood out is that this is not a future problem. It is a current one. And looking ahead, the focus is shifting from collecting data to making it usable and reliable for AI systems. My biggest takeaway.AI success will depend more on data discipline than model sophistication.#data #ai #SASInnovate #SASVisionary #theravitshow

I'm here at SAS Innovate, continuing conversations on what's next for enterprise AI. I had a blast chatting with Amy Stout, and this one was focused on something many enterprises are curious about but not fully ready for yet. Quantum AI.The discussion was very grounded.We talked about the real barriers enterprises face today:- Access to quantum systems- Lack of expertise- And uncertainty on where it actually fits in business problemsWhat SAS is doing with Quantum Lab is interesting because it is trying to remove that friction. Making quantum more accessible, more practical, and connected to real use cases.The key takeaway for me was this. Quantum is not about replacing AI. It is about expanding what problems we can solve.And while it may still be early, the groundwork being laid now will define who is ready when it scales.That's the kind of long-term thinking I'm seeing here at SAS Innovate.More content coming from SAS Innovate on The Ravit Show.#data #ai #SASInnovate #SASVisionary #theravitshow

I'm here at SAS Innovate, speaking with leaders who are shaping how enterprise AI actually gets deployed. Just spoke to Alyssa Farrell from SAS on The Ravit Show, focused on how SAS is accelerating AI across industries like financial services, public sector, and life sciences.What stood out was how clearly this is not about generic AI anymore. We talked about pre-packaged agents and industry-specific models, and why they matter. Most enterprises don't struggle to build models. They struggle to make them work in real environments.Regulation, workflows, and domain complexity are not edge cases. They are the system.SAS is leaning into this by embedding that context directly into AI systems, which is what makes agentic AI actually usable at scale.This is the shift I'm seeing here. From building AI. To deploying AI that understands the businessStay tuned for more content on The Ravit Show.#data #ai #SASInnovate #SASVisionary #theravitshow

AI conversations are everywhere, but what stood out to me in my chat with Harmeen Mehta from Equinix at Google Cloud Next '26 was how grounded their approach is. They did not start with big external announcements. They started inside.Harmeen shared a simple idea. If AI is going to change how a company works, it has to show up in how employees work first. Not as a side experiment, but as part of daily workflows. That shift is what moved AI from a pilot to something core to the business.At Equinix, AI is not sitting on the edges. It is being used to remove real friction from day-to-day work. Helping teams move faster, reduce repetitive tasks, and focus on higher value problems. That is where the impact starts to become real.But what stood out even more was how they approached trust.Employee hesitation is real. Questions around accuracy, reliability, and job impact come up quickly. Instead of ignoring that, they leaned into it. Clear use cases, transparency, and gradual rollout made a big difference in adoption.The biggest takeaway from this conversation was simple.Do not try to scale AI before you make it work internally.If your own teams are not using it, trusting it, and seeing value from it, scaling it across the business will not work.And looking ahead, the shift is already happening. Not years from now, but right now. AI is starting to change how work gets done inside enterprises, one workflow at a time.#data #ai #equinix #security #googlecloudnext #api #google #theravitshow

Just wrapped a great conversation with Woon Ho Jung, CTO - Cloud Native, Commvault, at Google Cloud Next 2026 and this one hit a nerve. Everyone is talking about multi-cloud, AI pipelines, and scaling data.But almost no one is talking about what's quietly breaking underneath it all. Data protection. We got into what's really happening inside enterprises today.Teams assume replication and retention policies are enough. They're not.At scale, across billions of objects, things get messy fast. Gaps show up where you least expect them.That's where the big announcement comes in. Clumio is going deeper with Google Cloud. Clumio for GCP is not just another backup solution. It's a rethink of how you protect cloud-native data, especially inside Google Cloud Storage where most AI and analytics pipelines live today.What stood out to me:- Protecting data at massive scale is still an unsolved problem for many teamsNative tools give a false sense of security- Resilience in the AI era needs a completely different approachIf you're building on Google Cloud right now, this is something you need to pay attention to. This is not about backup. This is about trust in your data layer.#data #ai #commvault #security #googlecloudnext #api #google #theravitshow

AI sounds exciting… until you actually try to use it inside a company. That was my biggest takeaway from my conversation with Michael Fasulo from Commvault at Google Cloud Next '26 on The Ravit Show.Everyone wants AI, but when it comes to real deployment, things break. Data is messy, systems are disconnected, and trust is missing. The gap is not ambition, it is readiness.One line that stayed with me. If your data is compromised, your AI is compromised.And with agentic AI, it gets even more real. These systems are not just answering anymore, they are taking actions. That means mistakes can have real impact.My takeaway is simple.The companies that win will not be the ones trying the most AI. They will be the ones fixing their data and putting the right guardrails in place first.#data #ai #commvault #security #googlecloudnext #api #google #theravitshow

AI agents sound exciting. But my conversation with A. Ravi M., CIO at Box at Google Cloud Next '26 on The Ravit Show was not about excitement.It was about risk. We are moving from AI that answers to AI that acts. And that shift introduces a completely new set of challenges. Not just accuracy, but control, access, and accountability. Ravi pointed out that most enterprises are not struggling with AI capability. They are struggling with governance. Who has access to what data, what an agent is allowed to do, and how you track those actions. Those gaps become very real once agents start operating on sensitive enterprise content.And that is where security needs to evolve. It is no longer enough to protect data at rest. You have to think about how AI agents interact with that data in real time, and what guardrails are in place when they take action.The partnership with Google Cloud plays a big role here. With platforms like Vertex AI and BigQuery, the focus is not just on building agents, but on building them with the right controls and visibility from day one.The biggest takeaway for me was simple. If you are a CIO thinking about AI agents, do not start with deployment. Start with trust. Because without that, none of this scales.#data #ai #box #security #googlecloudnext #api #google #theravitshow

Everyone is talking about AI agents, but after my conversation with Ben Kus, CTO at Box at Google Cloud Next 2026 on The Ravit Show, one thing became very clear. Agents are useless without "context". #boxpartnerBen kept coming back to that word. Not just data, not just models, but context. In an enterprise setting, context means understanding the full picture around your data. Who created it, where it lives, who can access it, and how it should be used. Most companies already have massive amounts of content, but it is fragmented and static, and that is the real problem.What stood out is how Box is approaching this. They are not just storing enterprise content, they are structuring it in a way that AI agents can actually use, turning content into something agents can reason on, not just retrieve. And this is where the partnership with Google Cloud comes in. With models like Gemini and platforms like Vertex AI, they are able to operationalize that context at scale in real workflows.The biggest takeaway for me was simple. If you want to become AI-first with agents, do not start with the agent. Start with your data. Structure it, govern it, and make it usable. That is what actually makes AI work.#data #ai #box #security #googlecloudnext #api #google #theravitshow

BREAKING: Kore.ai launches Artemis — a new generation Agent Platform for enterprise AII just sat down with Prasanna Arikala at their San Francisco office right after this launch.And here's what stood out.For years, most enterprises have been stuck in the same loop:-- Build AI pilots-- Struggle to productionize-- Lose control over governance-- Start overArtemis is Kore.ai's answer to that problem.This is not just another AI platform.It is a ground-up rebuild focused on one idea:AI should not just assist. It should build, govern, and optimize itself.Prasanna shared something interesting during the conversation.They didn't evolve the platform.They rebuilt it from scratch around what enterprise AI actually needs in 2026:-- AI building AI-- Built-in governance, not bolted on-- Optimization as a continuous loop, not an afterthought-- Designed for regulated industries from Day 1And this is where it gets real.Most enterprises today already have Amazon Web Services or Microsoft.But the gap is not infrastructure.The gap is:How do you go from AI experiments to reliable, governed, production systems at scale?That's the layer Kore.ai is going after.Also, one insight from Prasanna that stayed with me:The biggest mistake is thinking AI is a model problem. It is actually a systems problem.This launch is a signal.We are moving from:“Let's try AI”To:“Let's run the business on AI systems we can trust”I'll be dropping the full interview soon on The Ravit Show where we go deeper into:-- Why they rebuilt everything-- What “AI building AI” actually means-- Where enterprise AI is headed in the next 18 monthsThis one is worth paying attention to.#data #ai #koreai #agents #theravitshow

AI infrastructure conversations usually stay very technical. But my chat with , Santosh Erram, VP Partnerships DDN at Google Cloud Next '26 on The Ravit Show went in a different direction.He kept bringing it back to one thing. Business value. Yes, compute is growing. Yes, GPUs are everywhere. But that is not the real bottleneck anymore. Data is. If you cannot move it fast, access it easily, and actually use it, your AI investment does not translate into outcomes.What stood out was how fast things are moving. Their partnership with Google Cloud went from idea to launch in under six months. And now they are pushing things like 10 terabytes per second performance and hybrid tiering to meet real enterprise demands.But the real proof was in the use cases.- Salesforce pushing GPU utilization from around 48% to over 90%.- Resemble AI driving cost savings.- Sony Honda Mobility using it for autonomous driving.Even financial firms bursting massive workloads into the cloud, hitting petabyte scale in a single day. This is not experimentation anymore. We are moving from AI pilots to real production. And the shift from training to inferencing is going to define the next phase. My biggest takeaway. AI is no longer limited by models. It is limited by how fast and how well you can work with your data.#data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow

AI is moving fast, but after my conversation with Alex Bouzari, Co-Founder and CEO at DDN, at Google Cloud Next '26, one thing became clear.The bottleneck is no longer the model.It is the infrastructure behind it. Alex broke it down in a very real way. Today's AI systems are powerful, but the way data moves through them is still inefficient. You train these large models, but when it comes to actually running them at scale, things slow down. Latency increases, costs go up, and performance becomes unpredictable.That is what is broken.He shared how this shows up in real scenarios. When enterprises deploy AI, especially with large models, they struggle with speed and consistency. It is not that the model cannot perform, it is that the infrastructure cannot keep up with the demand.At Next, DDN focused on solving exactly this. Building what Alex called a new foundation for AI, designed for high-performance workloads where data access and speed matter just as much as the model itself.One concept that stood out was KV cache.It sounds technical, but the idea is simple. Instead of recomputing everything every time a model runs, you reuse key pieces of information. That reduces latency and makes systems faster and more efficient. In large-scale AI systems, that becomes a big deal.The bigger shift here is clear.We are moving from experimenting with AI to operationalizing it at scale. And that means infrastructure is becoming the deciding factor.What makes DDN different is their focus on this layer. Not just enabling AI, but making sure it actually performs in real-world environments.My takeaway. The future of AI will not just be defined by better models. It will be defined by better infrastructure.#data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow

The man, the legend, CTO of Qlik, Sam Pierson. Always love chatting with him and this time I asked him some hard questions. I like the depth of this conversation. Thanks Sam for always being such a great sport and sharing some enterprise gaps in the Data & AI World :)#data #qlik #ai #qlikconnect #theravitshow

I had the chance to discuss key takeaways with Mike Capone, CEO of Qlik at Qlik Connect 2026 on The Ravit Show. What made this conversation stand out was how real it felt. No hype, no buzzwords. Just a clear view of where things are actually going. The shift is happening fast. We are moving from AI that gives answers to AI that takes action. And that sounds simple, but when you unpack it, it changes everything. It changes how data is prepared, how systems are designed, and how much trust you need before letting AI operate inside real workflows.We talked about what is making this possible now, and why bringing analytics, data engineering, and trust together is no longer optional. It was also interesting to hear how companies like UPS, Schneider Electric, and HelloFresh are already moving from insights to execution.One point that really stayed with me was this. Perfect models are not the goal. Impact is. And the teams that understand this are the ones moving faster.We also spoke about trust, which is becoming the foundation for everything. Because once AI starts taking actions, you cannot afford to get it wrong.And I ended with a simple question. What is Qlik's role in the AI stack today. The answer was sharp and very telling..#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

Spent time at Qlik Connect this week and one thing became very clear to me. Everyone is talking about AI, but very few are talking about what actually makes AI work. I had a great conversation with Sean Stauth and Kyle Jourdan from Qlik, on The Ravit Show and we went beyond the usual AI hype. What stood out to me is that most teams are not failing at AI because of models. They are getting stuck on data. Not because they don't have data, but because they don't trust it, can't access it easily, or simply can't operationalize it fast enough.That gap between “we have data” and “we can actually use it for AI” is where most projects slow down. We also spoke about the constant tension between speed and foundations. Everyone wants to move fast with GenAI, but if your data layer is weak, you are just scaling confusion. The real challenge is not choosing between speed or building the right foundation. It is figuring out how to do both at the same time.Another point that stayed with me was around agentic AI. Grounding LLMs in enterprise data is no longer optional. It is the difference between something that looks good in a demo and something that actually works in production. And again, it all comes back to data quality, governance, and accessibility.My biggest takeaway from this conversation is simple. AI is no longer the hard part. Data is. The teams that figure this out will move ahead very quickly. The rest will keep experimenting without real impact.Conversations like this are exactly why I enjoy being on the ground at events like Qlik Connect. Learn from them below!!!! #data #qlik #ai #qlikconnect #theravitshow