The Ravit Show

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

AI is not the problem. The foundation is. That's exactly what we unpacked in this conversation with Bruno BILLY and Rahul Bakhshi at APGAR On Air. Here's what stood out for me. Most organizations are pushing hard on AI. But the data underneath is still fragmented, inconsistent, and not ready for scaleAnd that's where things breakIn this conversation, we go deep on- What a real data foundation actually looks like- Why most AI initiatives fail before they even start- Where things break at scale across governance, ownership, and trust- What teams can realistically do in the next 90 daysIf you are building AI that needs to run in production, this is worth your timeWatch the full conversation here: https://lnkd.in/g-VSBqat#data #ai #apgar #theravitshow

Another solid conversation from Qlik Connect 2026. This time with Gregory Pierce, MBA from Amazon Web Services (AWS), and we went deep into what it actually looks like to build data and AI systems at scale.What I liked about this discussion was how practical it was. There is always a lot of talk about cloud and AI, but this was more about how teams are actually making it work.We talked about the partnership between AWS and Qlik, and how it is helping customers bring everything together. Data integration, analytics, governance, all running on a scalable foundation. Not as separate pieces, but as something that needs to work end to end.One point that really stood out was around growth. Data volumes are increasing fast, AI workloads are getting heavier, and most teams are still dealing with legacy systems. The question is not just how to move to the cloud, but how to do it in a way that sets you up for what comes next.We also got into AI and GenAI in modernization. Where does it actually help? Things like speeding up migrations, reducing manual effort, and making systems easier to understand. But at the same time, Greg was clear about being careful. If your data is not reliable, adding AI on top just increases risk.And that led to another important point. Accuracy and trust. As teams use AI to transform legacy systems, they need strong validation, governance, and a clear understanding of what is happening behind the scenes.The last part of the conversation was about flexibility. This space is changing fast. New tools, new architectures, new expectations. The teams that win are the ones that stay adaptable and do not lock themselves into one way of doing things too early.Overall, this was a very grounded conversation on how cloud, data, and AI actually come together.#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

Another great conversation from Qlik Connect 2026. I sat down with Christopher Powell, and this one was all about customers. Not in a generic way, but what it actually means when you see real use cases in action.What stood out was how much focus Qlik is putting on customer stories. When you hear how teams are actually using data in their day to day work, it just clicks. It is not theory anymore. It becomes something you can relate to and apply.We talked about examples across regions, including teams in places like Japan solving very specific problems, and even sports organizations using data to compete with limited budgets. Those stories make everything feel a lot more real.He also walked through some of the key announcements.The Data Impact Awards stood out. Six customers from around the world being recognized not just for using data, but for actually driving measurable impact.Then the push around agentic AI. You can see where things are going. Less about static insights, more about systems that actually help move things forward.The ServiceNow partnership was another big one. Bringing trusted data into a system of action instead of keeping it separate. That shift is important.And there were updates on the engineering side as well, which felt like a direct response to what customers have been asking for.Looking ahead, there is a lot coming. More agentic AI releases through the year, built by teams across Sweden, India, the US, and Canada. And a continued focus on sharing more real customer stories.Overall, this conversation made one thing clear.Technology matters, but what really brings it to life is how customers are using it.#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

I got a chance to sit with Charlie Farah at Qlik Connect on The Ravit Show and this was one of those conversations that makes you pause and rethink how we look at data and analytics today. We often talk about tools, platforms, and the next big thing in AI. But what stood out to me in this conversation was how different regions are evolving at different speeds, especially across APAC versus the US and Europe. The ambition is the same, but the maturity, priorities, and constraints vary a lot more than we usually acknowledge.We also discussed Qlik's analytics roadmap, and it is interesting how fast things are moving. It feels like Qlik Answers just launched, and now the conversation is already shifting toward how these capabilities actually get used in real business workflows. Not just dashboards or insights, but decisions.And that is where I think the biggest gap still exists. Not between data and analytics, but between analytics and actual business value. Many teams are still very good at generating insights, but not as strong at embedding those insights into day to day operations where decisions are made. That last mile is still broken in many organizations.Another point that stayed with me was how easy it is to overlook the hidden challenges. It is not always about technology. It is about alignment, ownership, and making sure the right people trust and act on the data.Looking ahead, the next few years will not just be about better AI or faster analytics. It will be about making these systems actually usable and reliable in real environments. Less experimentation, more execution.What I liked most about Charlie's perspective is that despite all the changes, the core excitement around analytics has not changed. The opportunity to turn data into something meaningful for the business is still huge.And we are just getting started.#data #qlik #ai #qlikconnect #theravitshow

Most teams do not lose because of lack of talent. They lose because of decisions. At Qlik Connect, last week, I had a great conversation with Andreas Hadelöv, Assistant General Manager and Lead Analyst at Malmö Redhawks on The Ravit Show, and what stood out was how disciplined their approach to data is. This is a team competing in Sweden's top ice hockey league with one of the lowest budgets, so they cannot afford guesswork.Everything they do is grounded in data, but in a very practical way. They bring together data from wearables, game events, and training into a single view using Qlik, and use it to plan practices, give player feedback, prevent injuries, and shape game-day strategy. No overcomplication, just focusing on where data actually impacts performance.Where it gets even more interesting is recruitment. They rely on a few trusted metrics like ice time and performance to make hiring decisions. That clarity helps them stay competitive without overspending.Their approach to AI was also refreshing. They are testing it, but only using it where the data is highly reliable, mainly in scouting. Everything else can wait until the foundation is strong.The biggest takeaway for me was simple. Start small, stay focused, and align data with your strategy. That is what makes data actually useful.#data #qlik #ai #qlikconnect #theravitshow

What if the biggest shift in data security is happening where most teams aren't even looking? I was at the RSA Conference at the NetApp booth, and had a great conversation with Gagan Gulati, SVP/GM of Data Services at NetApp, that really got me thinking. For years, we've focused on visibility like dashboards, alerts, and detection. But the real shift is moving from detecting risk to actually blocking it at the data I/O layer, right where data is accessed. In a world where AI systems are interacting with data at massive scale, this becomes critical. We spoke about how concepts like a Security Knowledge Graph can help govern not just human users but nonhuman identities by understanding relationships between data, systems, and access in real time without slowing things down. Another important point was around AI training. It is no longer just about protecting data, but about knowing if your data is even ready by scoring it early and preventing risks before they show up in model outputs. And with most enterprise data being unstructured, the storage layer itself is evolving into a place where context and control come together. This conversation made me realize that security is no longer just another layer in the stack, it is moving closer to the data itself.Are we ready to rethink where security should actually live?Explore NetApp's CyRes capabilities -- https://www.netapp.com/cyber-resilience/?utm_campaign=cross-cyre-multi-all-ww-digi-ravit_show_influencer_video_interview_at_rsac_2026-1775578050296&utm_source=linkedin&utm_medium=social&utm_content=video&utm_segment=1j_cyre#data #ai #security #storage #agents #api #netapp #theravitshow

“Your next employee might not be human… and your security strategy isn't ready for it.”At RSAC, I spoke to Tom Gillis, SVP & GM of Infrastructure & Security Group at Cisco on The Ravit Show, and the conversation quickly moved beyond the usual AI hype into something much more real. We talked about agentic AI not just as a tool, but as a system that can act on its own, make decisions, and operate across enterprise data. That shift is forcing a complete rethink of security, because traditional models were built around humans, not autonomous agents. One thing that stood out was how security teams have always played it safe, often defaulting to “no,” but with agentic AI, that mindset becomes a bottleneck. The real challenge now is enabling this new layer of intelligence without losing control.We also unpacked what it really means to secure an “agentic workforce.” If every employee starts running multiple AI agents, each acting independently, the attack surface grows overnight. So do we start treating these agents like endpoints? Do they need identities, permissions, and governance just like humans? And if that's the case, how do SOC teams even deal with the explosion of alerts and signals? What I found interesting is that this is not some distant future problem, it is already showing up, and companies like Cisco are actively working through how to design security systems that can keep up.This conversation made one thing very clear to me. The AI conversation is no longer about models or capabilities. It is about control, trust, and how we rethink security for a world where humans are no longer the only actors inside the enterprise.#data #ai #rsac #cisco #theravitshow

Most security conversations at RSAC start with visibility. This one did not. I was at the Commvault booth, which by the way is set up like a full wrestling ring, and I sat down with the José Gomez Field CTO Security to talk about something that feels much more real right now. Control.Not dashboards. Not alerts. Actual control over who is accessing data in real time. What stood out to me in this conversation was how much AI is changing the risk surface.It is not just more data. It is more access, more queries, more non human identities touching sensitive systems all the time. And a lot of traditional tools were never designed for this.One point that stuck with me. Structured data is still one of the hardest things to secure properly.We assume it is easier because it is organized. But when access patterns explode, especially with AI, it becomes harder to track who should see what at any given moment.That is where real time access control starts to matter.Not after the fact. Not in a report. Right when the query happens.We also talked about something every team struggles with. How do you enforce governance without slowing people down?Because if security becomes a blocker, people will find a way around it. The interesting shift here is making security part of the flow instead of a checkpoint outside it.And tying that directly back to resilience. Because the more control you have over access, the faster you can respond and recover when something goes wrong.Another great conversation from the Commvault booth.#data #ai #security #rsac #attack #api #commvault #theravitshow

I did not expect to walk into a wrestling ring at RSAC conference. But that is exactly what Commvault built at their booth. And after my conversation there, it made complete sense. I spoke to Michelle Hartley Graff and Michael Fasulo from Commvault right in the middle of that ring, and we got into what this partnership actually means beyond the announcements with Microsoft.Here is the reality I keep hearing from teams. Detection is not the problem anymore. The real struggle starts after that. You detect something. Then what?That gap between detection and clean recovery is where most teams slow down. What stood out in this conversation was how tightly Microsoft and Commvault are trying to close that gap. With Microsoft Sentinel in the mix, the day to day operations start to feel more connected. Signals are not sitting in silos anymore.Then you bring in Security Copilot. Now you are not just seeing alerts, you are actually understanding them faster and deciding what to do next without digging through ten different tools. And the most interesting part for me was this idea of real signal sharing. Not just integrations on paper, but systems actually talking to each other in a way that helps you move faster when it matters.Because in a real attack, speed is everything. But so is getting back to a clean state you can trust. That is where this partnership is focused#data #ai #security #rsac #attack #api #commvault #theravitshow

Just wrapped up a conversation with Vidya Shankaran, CISSP from Commvault here at RSAC, and honestly, this one made me pause and rethink a few things. We talk a lot about resilience, threat detection, and now AI data. But what stood out to me is how the conversation is shifting from just “can you recover” to “can you recover clean”.That's a big difference.With Vidya, we went deep into what's actually broken in traditional recovery models and why “verified clean recovery” is becoming critical. Not just recovering fast, but recovering without bringing the threat back with you.We also got into the real tradeoff teams are dealing with today. Speed vs accuracy in threat detection. Quick scans vs deeper AI inspection. And the answer is not either or, it is how you combine both in practice.Another big takeaway for me was around AI data becoming a new attack surface. Most teams are still thinking about structured data, but AI pipelines, embeddings, and unstructured data are now part of the risk layer.And the blind spots are bigger than most people think. We also touched on something I hear a lot from teams. How do you actually enforce governance without slowing everyone down. There is no perfect answer, but there are better ways to approach it.If you are thinking about resilience, especially in an AI-first world, this conversation is worth your time.Let me know what stood out to you.#data #ai #security #rsac #theravitshow

Building AI for the real world is a very different problem than building AI for text. I sat down with Steve Xie, Founder & CEO of Lightwheel on The Ravit Show, to break down what it actually takes to train systems that operate in the physical world. Steve's journey from Peking University to Columbia University, and then into leadership roles at Cruise, NVIDIA, and NIO, gives him a unique lens into where today's AI systems struggle when they leave controlled environments and face the real world.One of the biggest takeaways from this conversation is that the core bottleneck in AI is no longer models, it is data. While large language models benefited from massive, passive data sources, robotics has no equivalent. There is no scalable way to collect real-world interaction data, no reliable evaluation layer, and very little infrastructure to continuously improve systems once deployed.This is where simulation becomes critical. In autonomous driving, simulation is helpful. In robotics, it is foundational. You cannot run thousands of parallel experiments in the real world, and you cannot reset physical environments at will. Simulation is what makes learning, testing, and iteration possible at scale. But not everything that looks like simulation actually works. As Steve explains, true simulation needs to be physically accurate, reproducible, and capable of generating actionable feedback. Without that, it cannot train real systems.What makes Lightwheel interesting is their approach to solving this problem. Instead of starting with data collection, they start with evaluation. They identify where models fail, generate targeted data to fix those failures, and create a continuous feedback loop. It is a shift from a passive data pipeline to an active data engine built for physical AI.They are already working with teams like DeepMind, ByteDance, and Alibaba, building infrastructure that sits beneath both robotics companies and AI labs.The bigger idea is simple. You cannot scrape your way to physical intelligence. You have to generate, test, and refine data in closed loops.#data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow

Just walked through the Lightwheel booth at #NVIDIAGTC and this is one of those moments where you realize physical AI is moving much faster than most people think. I got a booth tour by Jonathan Stephens, Chief Evangelist at Lightwheel, and what stood out immediately is how deep their approach goes. This is not just simulation for the sake of simulation. This is about building real-world intelligence that actually works outside the lab.Lightwheel is solving a problem most teams underestimate. You cannot scale robotics or physical AI without massive amounts of high-quality, physics-accurate data. Not synthetic guesswork. Real-world grounded data. And they are doing this at a completely different scale.We are talking about hundreds of thousands of hours of simulation data, not a few thousand.What I found most interesting is how they break this down into three layers:First, the world itself. They are literally measuring real-world physics. Using robotic systems to capture forces, movements, and interactions. Then rebuilding those environments in simulation so models learn from something that actually reflects reality.Second, behavior. This is where it gets powerful. Their Auto Data Gen layer uses LLMs to break complex robotic tasks into smaller actions. So instead of manually guiding every step, you can scale learning in a much more automated way.Third, evaluation. Most teams stop at training. Lightwheel goes further. They are benchmarking performance with tools like Isaac Lab Arena and pushing models through real-world scenarios like cleaning a kitchen or navigating a grocery setup.This full stack approach is what makes them stand out.Also worth noting the ecosystem they are already working with. Stanford, MIT, Nvidia, Figure, ByteDance. That tells you where this space is heading.If you are thinking about robotics, embodied AI, or world models, this is a company to watch closely.Physical AI is no longer theoretical. It is becoming operational. And Lightwheel is quietly building the infrastructure behind it.#data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow

Everyone is building AI agents… but very few are talking about the biggest blocker: fragmented data. I sat down with Ravi Marwaha, Chief Product & Technology Officer at Arango, on The Ravit Show to unpack what's really going on beneath the surface.This was not a typical “AI hype” conversation. We went deep into why agents that are supposed to reason, decide, and act are still struggling in real enterprise environments. And it comes down to one thing: context.Here's what stood out from the conversation:- Ravi's journey and why he chose to build at the intersection of data and AI- How fast the space has evolved and why most architectures are already outdated- The real problem with fragmented data when it comes to building reliable agents- The rise of the “context layer” and why everyone is suddenly talking about it- Where Arango fits in vs vector databases, graph databases, and data catalogs- Real customer use cases that go beyond theory- And what the next 18 months could look like for this entire spaceOne thing became very clear to me:The definition of a data platform is changing in real time. It is no longer just about storing or querying data. It is about enabling AI systems to actually understand and use that data in context.If you are building anything in AI right now, this is a conversation worth paying attention to.#data #ai #context #arango #theravitshow

Just wrapped a great conversation with Ravi Marwaha on-site at NVIDIA GTC.We talked about what Arango is building and why it is starting to show up more in serious enterprise conversations.If you have not come across them yet, worth a look: https://arango.ai/What stood out to me was how they are thinking beyond just being “another database company” and leaning into something much bigger around context and how enterprises actually use data.Next week, I am sitting down with Ravi for a deeper podcast. We are going to unpack some real questions that I think a lot of people in data and AI are already thinking about: • Why move from enterprise roles to Arango • What makes Arango interesting right now • Are they a database company, a context layer, or something else entirely • How they evolved so quickly • Everyone claims to be an AI company, what actually makes them different • Real enterprise use cases • What the next 2–3 years look likeThis is going to be a candid conversation.Stay tuned.#data #ai #AWSPartner #arango #NVIDIAGTC #theravitshow

One thing that became very clear to me at #NVIDIAGTC. AI is not slowing teams down. Data is. I spoke with Molly Presley, CMO and Mike Bloom from Hammerspace on The Ravit Show, the conversation quickly shifted from models to what actually blocks progress. Most enterprises are still dealing with scattered data across different systems, and the way they handle it today is still very manual. Teams are constantly searching for data, copying it, moving it, and trying to make sense of it before they can even start building AI.What stood out is how Hammerspace is approaching this differently. Instead of forcing companies to move data into new systems, they focus on making existing data usable where it already lives. That means classifying it, organizing it, and preparing it for AI without adding more infrastructure or delay. They also showed how quickly this can happen. Going from fragmented data to something AI-ready in minutes, not weeks.This feels like a shift. AI is not just about models or compute anymore. It is about whether your data is actually ready to be used.#data #ai #nvidiagtc #hammerspace #theravitshow

A lot of AI projects look great in a pilot. Very few make it to production. At NVIDIA GTC, I sat down with Glenn Dekhayser from Equinix to understand why. And the answer is not what most people expect. It is not just about models. It is about everything around them. We talked about why so much enterprise data is still unusable for AI.- Siloed systems- Lack of structure- No clear path from data to deploymentAnd then there is infrastructure. Because scaling AI is not just about training a model once. It is about running it reliably, securely, and close to where the data lives. That is where most projects break. One idea that stood out. Thinking of the data center as an “AI factory.” Not just storage. Not just compute. But a system designed to continuously turn data into outcomes. And that changes how enterprises need to plan.From day one. If you are serious about AI, this is the shift. From experiments to infrastructure.More conversations coming from GTC.#data #ai #equinix #NVIDIAGTC #theravitshow

If you are building anything in AI right now, you need to pay attention to this. I recently sat down with Kirill Gavrylyuk, VP of Azure Cosmos DB, Microsoft on The Ravit Show, and one thing was very clear. The biggest challenge in AI today is not just models. It is the data layer. We talked about where things are actually breaking. Teams are building AI apps, but they struggle with memory, real-time data, and systems that need to scale globally. This is exactly where Cosmos DB is becoming critical.What stood out to me is how practical this is getting. This is not about concepts anymore. It is about how real teams are building AI agents, how they manage state and memory, and how they design systems that actually work in production.That is why Cosmos DB Conf 2026 next week matters.This is not a theory-heavy event. It is focused on what is actually working. Real use cases, real architectures, real lessons. You will hear how teams are building RAG systems, scaling applications, and handling performance and cost in the real world.Also, it is completely free. Which honestly makes it a no-brainer.And you do not just watch. The Skills Challenge gives you a way to actually build and apply what you learn.I will be covering this live on April 28 and sharing the key takeaways.If you are serious about AI, data, or building systems that scale, this is worth your time.Register here: https://lnkd.in/ggNcVK6YApril 28 -- Show up and learn what actually works#data #ai #azure #cosmosDB #microsoft #api #microservices #theravitshow

Everyone at NVIDIA GTC is talking about GPUs. But my conversation with Greg Matson from Solidigm was a good reminder. AI does not run on GPUs alone. It runs on data. And how fast you can move that data. What stood out is how much pressure new NVIDIA systems are putting on storage. The demand is not just for capacity anymore, but for performance and efficiency at a completely different level.Solidigm is right in the middle of this shift. They are working with NVIDIA on things like liquid-cooled SSDs inside GPU systems and supporting new innovations like context memory extension. One insight that really stayed with me. If you lose context, GPU utilization can drop from 85% to 40%.That is a massive hit. Which means storage is no longer just a backend problem. It directly impacts performance, cost, and scale. We also talked about where this is going.Bigger drives, better efficiency, and designs that allow more GPUs to run within the same power limits.This is the part of the AI stack that does not get enough attention. But it might be the one that decides how far AI can actually scale.#data #ai #nvidiagtc #solidigm #theravitshow

Everyone in AI is talking about GPUs. Almost no one is talking about storage. At NVIDIA GTC, I sat down with Greg Matson from Solidigm, and this gap became very clear. Because behind every model, every training run, every inference pipeline… there is data. And how you store, move, and access that data changes everything. We talked about what companies are missing when they say they are “all in on AI.”Most are focused on compute. Very few are rethinking their data and storage strategy. And that creates problems later.Slower pipelinesHigher costsBottlenecks that no GPU can fixOne insight that stood out. The companies building serious AI infrastructure today are making storage decisions early. Everyone else is going to feel it later. We also touched on a bigger question. Are we heading toward a point where GPUs keep getting faster, but the data layer cannot keep up? Because if that happens, storage becomes the real limiter of AI progress. This is one of the most overlooked parts of the AI stack.But it might be the most important. More conversations coming from GTC.#data #ai #solidigm #NVIDIAGTC #theravitshow

AI is no longer just a technology conversation. It is becoming a national strategy. I spoke to with Thierry Pienaar, Chief Tech Officer at Hewlett Packard Enterprise at #NVIDIAGTC, and we discussed something that is quickly gaining momentum: Sovereign AI.Countries and organizations are starting to realize that where AI is built, trained, and deployed matters just as much as the models themselves. This is not just about innovation. It is about control, data ownership, and long-term independence.In this conversation, we break down:- What sovereign AI really means and why governments are investing in it- The infrastructure required to support sovereign AI at scale- How sovereign AI workloads differ from traditional AI setups- The idea of a “sovereign AI factory” and how it comes together- Real examples of organizations already building in this direction- Early outcomes and what leaders are starting to seeThis shift is bigger than most people think. AI is becoming part of national infrastructure.Learn more about HPE AI Factory here -- https://www.hpe.com/us/en/ai-factory.html?utm_campaign=FY26_Q2_AI_GE_GO_AMS_AMS_NVIDIA_GTC&utm_medium=DD&utm_source=HIP&utm_content=521216387&crid=%ecid!&plid=%epid!Learn more about HPE Private Cloud AI -- https://www.hpe.com/us/en/private-cloud-ai.html?utm_campaign=FY26_Q2_AI_GE_GO_AMS_AMS_NVIDIA_GTC&utm_medium=DD&utm_source=HIP&utm_content=521216387&crid=%ecid!&plid=%epid!#data #ai #nvidiagtc #sovereign #hpe #api #infra #theravitshow

Most enterprise AI projects are still stuck in pilot mode!!! But the real shift happening right now is not about better models. It is about where AI actually runs. I had a great conversation with Sadeepa Wijesekara from Hewlett Packard Enterprise at NVIDIA GTC, and one thing stood out:Private cloud is becoming the foundation for enterprise AI. Not just for control and security, but for actually scaling AI across the business.In this conversation, we discussed:-- Why private cloud is becoming the preferred model for enterprise AI-- How organizations are scaling AI securely from edge to datacenter-- The rising demand for air-gapped and disconnected AI environmentsWhat it takes to move from pilots to real production workloadsIf you are serious about taking AI beyond experiments, this one is worth your time.Learn more about HPE AI Factory here -- https://www.hpe.com/us/en/ai-factory.html?utm_campaign=FY26_Q2_AI_GE_GO_AMS_AMS_NVIDIA_GTC&utm_medium=DD&utm_source=HIP&utm_content=521216387&crid=%ecid!&plid=%epid!Learn more about HPE Private Cloud AI -- https://www.hpe.com/us/en/private-cloud-ai.html?utm_campaign=FY26_Q2_AI_GE_GO_AMS_AMS_NVIDIA_GTC&utm_medium=DD&utm_source=HIP&utm_content=521216387&crid=%ecid!&plid=%epid!#data #ai #hpe #NVIDIAGTC #theravitshow

I saw something at NVIDIA GTC that genuinely changed how I think about AI. Not another dashboard or abstract demo, but a full AI Factory built inside a bus by DDN. You walk in and suddenly AI is not a concept anymore, it is something you can see, touch, and understand end to end. I spent time on The Ravit Show inside the bus speaking with Jyothi Swaroop and Michelle (Pritoni) Scardino from DDN, and what stood out is how intentional this experience is. They are not just showcasing powerful infrastructure, they are trying to make AI feel accessible whether you are an enterprise, a government, or just starting out. Every layer is there in front of you, from compute to storage to networking, all working together in one place.What really stayed with me was a conversation with VirooPax Mirji, who brought it back to the core problem most teams face today. It is not about building models anymore, it is about dealing with different kinds of data that do not fit neatly into one box. Video, text, multimodal inputs, all of it. The work they are doing with NVIDIA focuses on making that data actually usable, helping teams search it, summarize it, and take action on it. That is where AI starts to deliver value.Thomas Jorgensen from Supermicro explained the idea of an AI factory in a way that just clicks. He compared it to a car factory, where you input raw materials and energy, and the output is something functional. In this case, the input is data and power, and the output is intelligence. Underneath that are layers of software, infrastructure, and storage, all supporting different types of workloads like physical AI, reasoning systems, enterprise RAG, and agentic AI. It made the whole stack feel much more practical.The demos made it even more real. Tim Yung from M2M Tech showed a digital twin of a robotic arm inside NVIDIA Omniverse, where a human interacts with it in VR to generate training data. That data is processed through DDN systems and then deployed into a real-world machine. You can literally see the loop from simulation to reality. And then there was Ameca, a humanoid robot from Engineered Arts, introduced by Leo Chen. It was observing, responding, and interacting in a way that felt surprisingly natural, and yes, we ended up having a dance off.What I appreciated most about this entire experience is how it removes the noise around AI. It gives you a simple, clear picture of how things actually work, from data to outcomes. No overcomplication, just a real look at how AI gets built and deployed. This is the kind of experience more people need if we want to move from talking about AI to actually using it.#data #ai #robot #ddn #machines #aifactory #simulation #theravitshow

Everyone at NVIDIA GTC is talking about AI factories. But most people still don't fully understand what that actually means. I got a chance to sit down with Alex Bouzari, Chairman, Co-Founder and CEO of DDN on The Ravit Show, inside their AI factory bus to break this down in simple terms.What stood out to me from our conversation is this. An AI factory is not just about stacking GPUs or building infrastructure. It is about creating a system where data flows efficiently, models get trained and deployed faster, and outcomes are measurable.We also spoke about why DDN chose to bring this concept to life through a physical, mobile experience instead of a traditional booth. When you walk through it, you see the full pipeline in action, not just slides or demos. It makes the idea of an AI factory much more real.Another key part of the conversation was around real-world use cases. From inference and RAG to genomics and video analytics, one thing became clear. The biggest bottleneck is not models. It is how data is handled, moved, and made usable across systems.And then there is the shift toward physical AI. With robotics, simulation, and real-world systems, AI is no longer just producing digital outputs. It is starting to interact with the physical world, which changes how we think about infrastructure, latency, and reliability.If there is one takeaway, it is this. Scaling AI is not about adding more compute. It is about building systems that can turn data into outcomes efficiently.#ai #data #aifactory #nvidiagtc #ddn #api #aifactory #theravitshow

Everyone at #NVIDIAGTC is talking about production AI. I found where to actually see it.Amazon Web Services (AWS) invited their AI Competency partners to demo real world solutions at their booth. My plan is to sit down with each of them and find out what they are building and how enterprises are using it.First up: Hareesh Kommepalli from Cognizant showing a computer vision solution for retail loss prevention.As more retailers move to self-checkout, losses from theft and scanning errors are going up. Cognizant is combining #NVIDIA computer vision with #AWS to help retailers catch those losses as they happen.Cognizant is one of the world's leading professional services companies and they have 25 listings on AWS Marketplace. That tells you something. Retail is just one example. They are working with enterprises on use cases like real time call translation for contact centers, AI agents for SAP, intelligent document processing for insurance, data clean rooms for sports sponsorship, and agentic AI for travel.Check out their listings: https://aws.amazon.com/marketplace/seller-profile?id=2a641c19-3444-4890-9738-e82a8ea39302&trk=5004eff9-71db-4806-9d01-fd7db0dd46bb&sc_channel=elMore AWS AI Competency partners coming throughout the show. Stay tuned.#data #ai #awspartner #cognizant #awsmarketplace #retail #theravitshow

AI adoption is not failing because of models. It is failing because of everything around them. At NVIDIA GTC, I spoke with Brian Blanchard from Thoughtworks, on The Ravit Show and this came through clearly.Most enterprises are still missing the basics.- AI-ready data- A proper control plane- Clear operating modelsThoughtworks is tackling this through modernization.From transforming legacy systems to building foundations for agentic AI with strong governance. What stood out is how they are simplifying complexity. Instead of stitching together multiple tools, they are bringing it into one platform. And all of this is built on Amazon Web Services (AWS), using services like SageMaker and Bedrock, with solutions available on AWS Marketplace.This is the real shift. AI is not just a technology problem. It is a systems and operations problem.Check out the video and learn more!!!!#data #ai #awspartner #nvidiagtc #theravitshow

AI models are easy. Making them useful is hard. At NVIDIA GTC, I had a blast chatting with Roberto Barroso-Luque from Fireworks AI at the Amazon Web Services (AWS) booth. Fireworks helps teams fine-tune open-source models and run them in production. Built on AWS, it gives you scale, speed, and reliability without managing infrastructure.Use cases are already real. Coding assistants. Customer support that resolves issuesYou can access it through AWS. Marketplace or start with an API. From models to real applications.#data #ai #AWSPartner #NVIDIAGTC #fireworks #api #models #theravitshow

Everyone is talking about AI. But what actually powers it? At NVIDIA GTC, I spoke with Travis White from Zilliz at the Amazon Web Services (AWS) booth. Exciting conversation!!!!We talked about vector databases. The layer that helps AI search through images, videos, and text, not just tables. Zilliz offers Milvus (open source) and a managed version on AWS. That means you can scale, stay secure, and avoid managing infrastructure. Real use cases are already here. Autonomous vehiclesDrug discovery, and a few moreYou can also find Zilliz on AWS Marketplace and get started quickly. Simple idea. AI is not just models. It is the data layer underneath. Learn more from the conversation!!!!#data #ai #awspartner #nvidiagtc #theravitshow

Everyone is building RAG. But the real question is what comes next. At NVIDIA GTC, I spoke with Jay Wilder from deepset, makers of Haystack, and the focus was clear. Moving from RAG to agents. Deepset, the team behind Haystack, is helping developers build flexible AI systems where you can choose your models, data, and guardrails.What stood out was their demo. They showed how RAG pipelines can evolve into agent-based systems by integrating across ecosystems like AWS, NVIDIA, and tools like Weights & Biases. This is where things get real.In banking, workflows that took weeks are now being done in hours.In pharma, teams are testing and iterating on complex data pipelines much faster.And all of this is built with flexibility in mind. Not locked into one stack. With Amazon Web Services (AWS) as a long-time partner and availability through AWS Marketplace, it is becoming easier for teams to get started and scale.This is the shift I am seeing. From static pipelines to adaptive AI systems.#data #ai #awspartner #NVIDIAGTC #deepset #api #haystack #theravitshow

One thing I keep thinking about from hashtag#NVIDIAGTC. We are moving too fast in building AI, but not fast enough in validating it. I spoke with David Arakelyan from Deepchecks, and the conversation really came down to trust. Once you move beyond demos, the question is no longer what your AI can do, but whether you can rely on it in production. Deepchecks is focused on this exact problem. Their LLM evaluation and monitoring solution helps teams test and stress their systems before they go live. What stood out was their new feature, Know Your Agent, which lets you generate a full report on your AI system with just an API key and understand where it can break.They are also one of the few solutions integrated directly into AWS SageMaker as a partner app and available on AWS Marketplace, making it easier for teams to bring this layer into their workflows.What do you think about it?#data #ai #awspartner #NVIDIAGTC #deepcheck #api #evals #theravitshow

Everyone at NVIDIA GTC is talking about AI. But my conversation with Vinay Sridhar from Snowflake came down to one thing. AI is only as good as the data behind it.Snowflake is building Cortex, an AI layer that runs directly on governed enterprise data. So instead of moving data to AI, teams bring AI to their data. We also touched on how tools like Cortex Code and text-to-SQL are changing how fast teams can build.And behind all of this is Amazon Web Services (AWS), powering the infrastructure and scale.This is the shift. From models to data-driven AI systems. Learn more from the conversation!!!!#data #ai #AWSPartner #NVIDIAGTC #snowflake #NVIDIAGTC #api #models #theravitshow

AI agents are everywhere at NVIDIA GTC. But here is what stood out in my conversation with Rudy Chetty from Amazon Web Services (AWS) on The Ravit Show. AWS is leaning heavily into this with Marketplace. A growing hub where you can search, purchase, and deploy AI solutions in minutes. From foundation models to vector databases to monitoring tools, everything is starting to come together in one place.We also talked about the role of partners. Because scaling AI is not a one-company job. It takes an ecosystem. AWS provides the infrastructure.More from the AWS Marketplace Kiosk at GTC.#data #ai #awspartner #nvidiagtc #nvidiagtc2026 #api #awsmarketplace #theravitshow

Data quality is still one of the most underestimated problems in AI. Everyone talks about models. Very few talk about whether the data behind those models can actually be trusted. At Gartner D&A, I sat down with Nick Oldham, COO at Equifax, and the conversation was very real. Equifax operates at the center of the global financial ecosystem. That means data integrity is not just a technical problem. It is a business risk. A regulatory risk. A trust problem.What stood out was this shift:- They are not treating data quality as a one-time cleanup anymore.- They are building systems to monitor, detect, and fix issues continuously.That is where platforms like Anomalo come in.Instead of reacting to broken dashboards or failed pipelines, the goal is simpleCatch issues before they impact the business. And this becomes even more critical as AI enters the picture. Because AI systems do not fail loudly. They fail quietly when the data is wrong.Nick also shared how their journey was not just about tools. The harder part was organizational.- Aligning teams on what “good data” actually means- Moving from siloed ownership to shared accountability- Bridging the gap between data strategy and real executionLooking ahead, with investments in Google Cloud and tools like Dataplex, the direction is clearMore automationMore observabilityLess manual firefightingIf you are building AI without solving for data integrity first, you are building on weak foundations.If this topic interests you, Nick will be going deeper into this at an upcoming session: The Road to Self-Driving Data, April 2 go for ithttps://engage.anomalo.com/the-road-to-self-driving-dataThis is one of those conversations that is less about hype and more about what it actually takes to make data work at scale.#data #ai #anomalo #dataquality #theravitshow

We are surrounded by the biggest Data and AI leaders at Gartner. Yet one question keeps coming up. Why are so many AI initiatives still failing? I asked Rich Hoyland, President, Global Field Operations, Anomalo to answer that directly on The Ravit Show at Gartner Orlando.His take was simple.AI does not fail because of ambition.It fails because of bad data.We spoke about:-- Why agentic AI raises the stakes for data quality-- Why speed without trust is a risk for executives-- What data quality failure actually looks like inside large enterprises-- Why traditional rule-based approaches are not enough anymore-- And where Anomalo sees the future of data management goingOne part of the conversation stood out. When AI moves from insight to action, data quality stops being a reporting issue. It becomes a business risk issue.Old approaches were built for dashboards. Now we are feeding data into agents that make decisions. That changes everything. Rich also shared how their long term vision goes beyond just catching bad data to providing enterprises with an agentic “self-driving data” system. It is about building continuous trust in data across the enterprise from ingestion to decision so AI agents can operate and scale safely.If you care about AI that actually works in production, this is one to watch.#data #ai #anomalo #dataquality #theravitshow

At Gartner D&A Day 1, I sat down with Antonio, CEO and Co-Founder of BEM, to talk about a problem many enterprises quietly struggle with.Messy data.While everyone is excited about agents, Antonio made one thing clear.Agents do not work well with unstructured, inconsistent inputs. That is where hallucinations and failures begin.BEM focuses on turning messy inputs, from PDFs and contracts to voice and video, into clean, structured outputs that enterprises can actually trust.We discussed:* Why so many AI pilots fail before reaching production* How BEM acts as the foundation layer before agents* Why regulated industries like healthcare and finance need production-grade accuracy* How some teams deploy in minutes by starting with one painful workflowThe message was simple.If you want agents to work, fix the data first.#data #ai #bem #gartnerda #theravitshow

“Your data is fine. Your AI isn't good enough.” That is the bold statement behind TextQL, and it immediately caught my attention here at Gartner. I sat down with Ethan Ding, Co-Founder, CEO & Head of Product, TextQL, to unpack what he means by that and why they are challenging many assumptions around BI and analytics.Most enterprises have spent years building ETL pipelines, cleaning data, and preparing dashboards. The belief has been that AI will only work once data is perfectly structured.Ethan disagrees.He believes the real limitation has been the AI systems themselves.We talked about:-- What enterprises are misunderstanding today about AI and data quality-- Why traditional BI tools like Tableau or Power BI were built for a different era-- How TextQL enables AI analytics even when data is messy or not fully ETL'd-- Why they believe seat-based pricing for dashboards is broken-- How their approach focuses on trust and verification so enterprises can validate AI-generated answersOne idea stood out during the conversation.Executives do not just want answers.They want conviction that the answer is correct.That is where their “Query to Conviction” concept comes in. AI does not just generate an answer. It shows the reasoning, the data path, and the verification behind it.For CIOs walking the Gartner floor, Ethan had a simple suggestion. Do not ask vendors how good their AI looks. Ask them how their AI proves it is right.#data #ai #textql #gartnerda #theravitshow

Most AI analytics platforms assume one thing. Your data lives in one place. Kapil Chhabra, Co-Founder and CPO at WisdomAI on The Ravit Show challenged that assumption immediately. Enterprises are distributed. Their data is fragmented across clouds, warehouses, operational systems, and business units. Forcing everything into a single layer before AI can work is slow and expensive.That is why WisdomAI introduced Federated Agentic Intelligence.Instead of centralizing first and analyzing later, the system works across distributed sources. It assembles context at runtime. We spent time on what they call the Enterprise Context Layer. Without context, AI gives generic answers. With context, AI understands how metrics connect, what definitions mean, and how governance rules apply.Kapil was clear that federation is not a feature. It is a design principle for the modern enterprise.We also talked about what is next on their roadmap and what capabilities they are most excited about over the coming year. The focus is less on flashy features and more on depth, reliability, and scale.If your organization is struggling to move from AI experiments to AI that executives actually trust, this conversation goes deep into architecture decisions that matter.#data #ai #gartnerda #wisdomai #theravitshow

We have been building dashboards for 20 years. Now everyone is adding AI on top of them. But what if the real issue is the stack itself? That is where my conversation with Soham Mazumdar, Co-Founder and CEO, WisdomAI went at Gartner D&A on The Ravit Show!!!!WisdomAI calls what they are building “Agentic Analytics.” Not a chatbot on top of BI. Not a copilot that still depends on humans to interpret everything. We talked about what is fundamentally broken in today's analytics world:- Dashboards answer questions you already thought of- Executives need answers to questions they did not know to askSoham shared how enterprises are moving from static reporting to agents that reason across metrics, detect issues, and explain why something happened. The trust problem came up quickly. Most AI analytics tools look impressive in a demo. Very few hold up under real enterprise scrutiny.We also discussed a real customer story with Cisco and what changed after deploying WisdomAI. The shift was not just faster answers. It was decision confidence.Looking ahead, Soham believes analytics teams will not disappear. They will evolve into designers and supervisors of intelligent systems that operate continuously across the business.For enterprise leaders rethinking the future of BI, this was a forward-looking and very practical discussion.#data #ai #gartnerda #wisdomai #theravitshow

#IBMPartner Governance, responsible AI implementation and delivering measurable value —these are top of mind for Jordan Byrd, AI/ML Ops Product Marketing Lead at IBM.AI adoption feels different this year—faster with more framework. Watch our conversation from Gartner D&A where we caught up to discuss what's really changing inside enterprises and what that means for the next phase of AI.If you are building AI at scale inside an enterprise, this one will resonate.

Everyone says AI is the priority. Yet many projects are quietly failing. At Gartner D&A, I asked Christopher Moore, Global Sr. Director, AI & Platform at Alteryx, to be direct about why.His answer was not about models. It was about execution. Too many AI initiatives are disconnected from real business workflows. They look good in a lab. They struggle in operations. We then got into a bigger tension inside enterprises. Business teams understand the problem best. But they rarely build the AI solutions themselves.Why? Because the tooling has been too technical. Too fragmented. Too dependent on centralized teams. Christopher explained how Alteryx is trying to close that gap. Not by lowering standards, but by enabling governed, production-grade AI where business users already work. We also talked about what MCP server and Agentspace unlock for long time Alteryx users. In simple terms, it is about moving from isolated workflows to orchestrated AI systems. From analytics automation to agent-enabled automation.And then we addressed the elephant in the room. Alteryx was once labeled shadow IT. That perception has shifted. In a world where AI governance is critical, the focus is now on controlled enablement. Visibility. Auditability. Guardrails built in.The message was clear. Empowering business users does not mean losing governance. It means designing platforms that balance speed with control. If you are navigating the tension between innovation and oversight, this is a conversation you will want to watch.#data #ai #gartnerda #alteryx #theravitshow

Synthetic data is everywhere in AI conversations!!!! But what does it actually solve? I had an amazing conversation with Michael Eckhoff on The Ravit Show at Gartner he brought this down to reality. We spoke about when synthetic data makes more sense than masking or subsetting production data.It shines when:• Compliance makes moving production data into lower environments a bottleneck• Teams need data that simply does not exist• Rare edge cases are missing from real datasetsSynthetic data lets teams generate fit-for-purpose datasets on demand without copying real customer records across environments.We also tackled the big concern. Is synthetic realistic enough?Realistic does not mean copied. It means the relationships hold. The distributions look right. The system behaves the same way.And you prove it.You compare statistical properties.You validate patterns.You ensure no record is traceable to a real individual.Finally, where does synthetic fit in AI and GenAI?It removes the compliance friction.It helps balance datasets.It enables experimentation without exposing sensitive information.For AI teams trying to move fast and stay compliant, this is a serious lever.#data #ai #gartner #k2view #theravitshow

I had a blast at Gartner last week, here's my discussion with Hod Rotem from K2view on The Ravit Show, diving into one of the most important topics right now. What does AI-ready data architecture actually look like when it is running in production? We will break down:* How real-time, entity-level data gets assembled across dozens of systems* What it takes to support thousands of AI agents working in parallel* Why architecture, not just models, determines whether AI actually worksIf you are thinking about agentic AI beyond demos, this will be a practical and direct conversation.#data #ai #gartnerda #K2View #theravitshow

Mainframes. Synthetic data. AI-ready foundations. This was one of the most practical conversations we had at Gartner on The Ravit Show. I sat down with Ronen Schwartz, CEO at K2view and Michael Curry, President, Data Modernization, Rocket Software to talk about their partnership and why it matters right now.Here is the reality.A lot of enterprise data still lives in mainframes and core systems of record. At the same time, teams are racing to automate development, generate code with AI, and move faster than ever.That creates a real gap.We discussed:-- Why customers building data products, test data management, and synthetic data pushed K2View and Rocket Software to collaborate-- How modernization of legacy systems creates opportunities to generate and manage test data at scale-- Why synthetic data is critical when you cannot simply move production data into lower environments-- How teams can now generate code from a product story and also generate the data needed to test it-- Why governance is the layer leaders must get right before scaling AIOne point stood out.The technology leap toward AI is not the hardest part. Getting the data foundation, quality, and governance right is. If AI agents are going to act on enterprise data, that data must be trusted, protected, and consistent across systems of record.Their advice to leaders was simple.- Build AI-ready data environments.- Partner with vendors who are deep in what they do.- Carry your governance investments forward into your agentic AI strategy.If you are modernizing mainframes, thinking about synthetic data, or preparing your enterprise for AI in production, this one is worth watching.#data #ai #rocketsoftware #gartnerda #k2view #api #mainframes #enterprise #theravitshow

Five years advising CDAOs at Gartner D&A. Now in the field helping enterprises actually implement AI and governance. That shift gives Austin Kronz, Head of AI & Data Strategy, Atlan, a rare lens. And this conversation was honest. We talked about the gap between what we say on stage at big events and what really happens inside companies once everyone flies home.Here is what we unpacked:• What surprised him most moving from analyst to operator• The real signals coming out of this summit around AI governance, metadata, and context for AI agents• The controlled experiments around context layers at companies like Workday and Fox, and what actually drove up to 5x improvement in AI accuracy• Where Fortune 500 teams get stuck when they say “we need AI governance”• The patterns he sees in companies like Cargill and PPG that succeed with context at scaleOne theme kept coming up.The winners are not the ones talking the most about AI. They are the ones operationalizing context, ownership, and governance in very practical ways.Whether you attended Gartner D&A or not, this one is worth watching.#data #ai #gartnerda #atlan #theravitshow

Your AI has a context problem. It's not the model. It's context!!!! That was the mic drop from Prukalpa, Co-Founder & Co-CEO, Atlan on The Ravit Show when we kicked off this conversation. And honestly, it set the tone for everything that followed.We spoke about why so many AI projects stall. Not because the model is weak. Not because the team is not smart. But because the data lacks shared meaning. No common definitions. No clear ownership. No business context.Here is what we unpacked:• What a “context layer” actually means in simple terms• Why this idea is suddenly everywhere at Gartner this year• Where the context layer fits in the modern data stack• What Atlan is building to make context usable, not theoretical• The one demo every data leader should see before leaving OrlandoOne big takeaway:If your AI does not understand your business context, it will confidently give you the wrong answer.If you are at Gartner this week, stop by Booth 313. See how context is being turned into something real, usable, and operational.#data #ai #gartnerda #atlan #theravitshow

"On-prem is the new cloud.” That statement is not just a headline. It reflects what many enterprises are quietly experiencing. I sat down with David Dichmann, VP Product Marketing and Evangelism, Cloudera, to unpack what is really driving this shift and how Cloudera's Cloud Anywhere vision fits into the bigger picture.Here is what we discussed:-- Why rising cloud costs, data gravity, and regulatory pressure are pushing companies to rethink all-in cloud strategies-- What Cloud Anywhere actually means beyond marketing-- How enterprises can run advanced AI use cases without forcing massive data movement into one environment-- Why security, governance, and Private AI are central to this resurgence of on-prem-- The biggest roadblocks teams face when deploying AI across hybrid and multi-cloud environments-- How portability and consistency reduce friction for data and AI teams-- How Cloudera continues to lean into its open-source roots while evolving its platformOne theme stood out. AI does not require you to centralize everything into one cloud. It requires control, flexibility, and a consistent experience wherever your data lives. For enterprises balancing cost, compliance, and AI ambition, this conversation goes beyond trends. It is about architecture decisions that will shape the next few years.#data #ai #cloudera #gartnerda #theravitshow"On-prem is the new cloud.” That statement is not just a headline. It reflects what many enterprises are quietly experiencing. I sat down with David Dichmann, VP Product Marketing and Evangelism, Cloudera, to unpack what is really driving this shift and how Cloudera's Cloud Anywhere vision fits into the bigger picture.Here is what we discussed:-- Why rising cloud costs, data gravity, and regulatory pressure are pushing companies to rethink all-in cloud strategies-- What Cloud Anywhere actually means beyond marketing-- How enterprises can run advanced AI use cases without forcing massive data movement into one environment-- Why security, governance, and Private AI are central to this resurgence of on-prem-- The biggest roadblocks teams face when deploying AI across hybrid and multi-cloud environments-- How portability and consistency reduce friction for data and AI teams-- How Cloudera continues to lean into its open-source roots while evolving its platformOne theme stood out. AI does not require you to centralize everything into one cloud. It requires control, flexibility, and a consistent experience wherever your data lives. For enterprises balancing cost, compliance, and AI ambition, this conversation goes beyond trends. It is about architecture decisions that will shape the next few years.#data #ai #cloudera #gartnerda #theravitshow

The mainframe is not going anywhere. It is evolving. I just wrapped up a powerful conversation with Matt Whitbourne, VP of Product Management & Design for the BMC Software AMI portfolio.We spoke about something many enterprises are quietly navigating right now:How do you modernize the mainframe without breaking what already works?Here's what stood out to me from our discussion:* Modernization is no longer optional. It is expected.* Cyber resilience is now a board-level conversation.* Data accessibility on the mainframe is becoming critical for AI and enterprise-wide analytics.* Automation is helping teams move faster without compromising stability.Matt also shared how AMI Cloud is evolving, especially around resilience, operational efficiency, and AI-driven capabilities.What I appreciated most was this balance: Enterprises want innovation.But they also want reliability.The mainframe still runs some of the most critical systems in the world. The challenge is not replacing it. The challenge is transforming it. If you work in enterprise IT, data, or platform engineering, this conversation is worth your time.

Observability is no longer just about dashboards. It is about systems that can act. I caught up with Brian Emerson, Chief Product Officer at New Relic, at Advance 2026, and one thing was clear. Brian shared a bold view of where software is headed. More applications will be built in the next five years than in the last fifty. That creates a scale problem no team can solve manually. His answer is intelligence and automation, led by what New Relic calls the S agent. What stood out to me is how this shifts incident response. Instead of pulling people into late night war rooms, the idea is a digital war room where agents constantly watch behavior, surface issues, and even help resolve them. For customers, the starting point is simple. Turn it on and let it begin optimizing alerts and recommendations.We also talked about trust, which is the real conversation right now. Application health is no longer just uptime. It is behavior, outcomes, and user experience. AI systems can fail quietly, with wrong answers or poor decisions. That is why New Relic is pushing toward agentic monitoring, so teams can see not just the infrastructure but the actual experience their users are getting.One insight Brian shared surprised me. New Relic chose to lean into fast-improving general models instead of building niche ones, because the pace of change makes specialization short lived. That says a lot about how quickly this space is moving.His message for leaders was simple. The shift is from recommendations to action. The real opportunity now is letting agents handle the last mile of operations and moving closer to self-healing systems.#Data #AI #NewRelic #Observability #AI #SRE #AgenticAI #TheRavitShow

At New Relic Advance 2026 in San Francisco, one theme was impossible to miss. Observability is no longer just about seeing problems. It's about fixing them. I caught up with Ashan Willy, CEO of New Relic, to talk about what this shift really means for customers.The biggest highlight was the launch of their SRE Agent and the broader move toward an agentic platform. What stood out in our conversation was not just the technology, but the reason behind it.Customers are overwhelmed by signals, alerts, and dashboards. They don't need more data. They need faster action.That is exactly what New Relic is aiming to solve. Closing the gap between insight and execution so engineers spend less time diagnosing and more time delivering.Compared to last year, this feels like a real strategic inflection point. The conversation is no longer about adding AI features. It is about redesigning workflows around AI-native operations.The big question now is not whether agents will assist engineers. It is how much responsibility they will take on as trust grows. If this launch works, by next year we should see fewer alerts, faster resolutions, and teams spending more time building instead of firefighting.#Data #AI #NewRelic #Observability #AI #SRE #AgenticAI #TheRavitShow

Everyone is talking about AI agents. But very few conversations are grounded in real data.Databricks just released their new State of AI Agents report, and it gives a clear picture of how enterprises are actually using AI today, what is working, and where things are headed next.I sat down with Kunal Marwah, Mason Force, and Chengyin Eng from Databricks to break down what stood out to them from the report and what they are seeing directly with customers.We talked about why companies are moving from to multi-agent systems, how teams are choosing their first real business use cases, and how agents are driving the need for a new type of database called Lakebase.We also discussed what separates teams that get AI into production from those stuck in endless pilots. Governance, evaluation, and clear alignment to business outcomes came up again and again.If you are leading data, AI, or product initiatives, this conversation gives a practical look at what enterprise adoption actually looks like today and what leaders should focus on next.I have shared the link to the full report in the comments if you want to dig into the data yourself.Learn more about ---- Databricks Mosaic Research: https://www.databricks.com/blog/category/ai/mosaic-research-- Databricks Industry Solutions: https://www.databricks.com/solutions/accelerators#data #ai #report #agents #chatbots #api #business #databricks #theravitshow