The Ravit Show

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

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