The Ravit Show

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

What if AI is not just powered by master data… but actually improves it? At the DataDriven Conference, Ravit Jain sat down with Anjan Roy from Deloitte, who leads the global life sciences MDM practice, to talk about the real relationship between AI and Master Data Management.One line from our conversation stayed with me: AI supports MDM and MDM supports AI.Most companies talk about preparing data for AI. Deloitte is also focused on the reverse. Using AI to make MDM programs faster, smarter, and more scalable so organizations can realize value sooner.In life sciences, this is not theoretical. It currently takes 10 to 12 years to bring a drug to market. If better data and AI can reduce that timeline by even 10 to 20 percent, patients get access to treatment sooner. That impact is real.They also discussed how agentic AI is starting to change the pace of execution. Life sciences is one of the most regulated industries in the world, yet we saw vaccines come to market in nine months during COVID. The acceleration is possible when data, governance, and AI come together with the right intent.Looking ahead six to eight months, Anjan expects AI to become more operational, data to be more accessible, and organizations to move from gut-driven decisions to data-driven action.This is powerful conversation about speed, scale, and impact in one of the most critical industries.#data #ai #datadriven #reltio #theravitshow

LLMs can accelerate everything. But without context, they accelerate you in the wrong direction. At DataDriven Conference 2026 by Reltio, Ravit Jain had a powerful conversation with Satish Mallavolu, CEO of Blumetra Solutions, about the end-to-end data life cycle and what it really means to monetize data.One message came through clearly. Master data is not optional. It is the glue that holds the enterprise together.Satish described LLM acceleration like oil in an engine. It helps you move faster. But without strong context, and without master data as a core ingredient of that context, that speed will land you in a ditch.They spoke about why enterprise leaders are rethinking their foundations. If the data layer is weak, every AI use case built on top of it will struggle. Garbage in, garbage out is still true, even in the age of generative AI.Looking ahead, Satish believes every enterprise will need a serious metadata practice. He even advocates for a Chief Metadata Officer role. Without ownership of metadata and context, AI initiatives will continue to produce inconsistent results.They also discussed real use cases, from campaign activation and churn prevention to smarter upsell and cross-sell strategies, and better data retention.This was a grounded conversation about turning data into business value, not just dashboards.#data #ai #gartnerda #wisdomai #theravitshow

MDM is back. And this time, it is tied directly to AI outcomes. At DataDriven Conference 2026 by Reltio, I had a great conversation with Avinash (Avi) Deshpande, Field CTO at Workato, about how integration, MDM, and agentic workflows are coming together.One thing was clear. MDM is no longer a back-office data discipline. It is becoming the context engine for AI.Workato plays a critical role in this ecosystem. As a strategic partner with Reltio, they act as the integration hub powering the data layer. That means enabling inbound data, enriching it with external sources like Dun & Bradstreet, and activating it across the business for real consumption.But the bigger shift we discussed was agentic flow.This is where traditional MDM is being reimagined. Instead of static processes, agents operate within workflows to improve automation, user experience, and speed. The industry is moving past experimentation and toward business enablement. By mid-year, Avinash expects most enterprises to be done testing and focused on measurable impact.And that is what leaders care about. Real ROI.The combination of integration, trusted master data, and agentic capabilities is no longer theoretical. It is accelerating outcomes across enterprises.#data #ai #datadriven #reltio #theravitshow

Reltio crossed $185M in ARR and saw 58% growth in the second half of the year. That kind of acceleration in an enterprise platform signals strong market pull. At the DataDriven Conference 2026, I sat down with Alyson Welch, CRO of Reltio, to talk about what customers are actually asking for in the age of AI.Alyson owns the full customer journey, from acquisition to support, and also leads the partner ecosystem. Her lens is simple. If the customer does not see value in their data, nothing else matters.One insight stood out. Enterprises are drowning in applications. One former CIO shared she had 1,500 enterprise apps to manage. That means data is locked everywhere. The real demand today is not just dashboards or models. It is a trusted, unified data layer that sits between enterprise systems and AI.That is where Reltio is focused. Bringing accuracy, trust, and unification so companies can actually use their data across environments.We also discussed agentic workflows. In an agent-driven world, your data foundation cannot be average. Every automated decision depends on it. This is not just a tech shift. It is a cultural shift. Leaders now have to think about how humans and AI agents work together and how to build capability across both.The momentum reflects this shift. This conversation was about trust, scale, and what it really takes to lead in the AI era.#data #ai #datadriven #reltio #theravitshow

What happens when AI is applied to real mission problems, not just demos? At Data Driven 2026, I sat down with Lisa Wolff from ForeSite360 to talk about how they are using AI, together with Reltio, to solve complex challenges in highly regulated environments like the federal government.One example stood out immediately. A deployment of their platform, Foresight 360, built on top of Reltio, has already helped a government agency save more than $100M every year. That kind of impact only happens when the data foundation is right.Lisa made an important point during our conversation. AI only works when the context around the data is well understood and properly managed. Without that, even the most advanced models struggle to deliver meaningful outcomes.We also discussed how large organizations are moving away from slow, traditional data modernization approaches. The pace of change is accelerating quickly, and Lisa expects some major shifts in the next six months as enterprises adopt more agent-driven systems.What I also found interesting was how these ideas translate beyond government. Foresight is applying similar approaches to improve experiences for seniors interacting with public programs and even the guest journey in hospitality. The real power of AI shows up when it solves personal, real-world problems.It was a fascinating conversation about how AI, data context, and agent systems are starting to deliver real impact.hashtag#data hashtag#ai hashtag#datadriven hashtag#reltio hashtag#theravitshow

Everyone talks about AI agents. Very few talk about what they actually change inside enterprises. At the DataDriven Conference, Ravit Jain sat down with Rajeev Krishnan from PwC to unpack what the agentic shift really means for data, governance, and Master Data Management.PwC and Reltio have been working together for years, but the conversation is now moving beyond implementations to real business outcomes. Rajeev shared how their Agent OS ecosystem is being integrated with Reltio to deploy agents that handle tasks data teams have struggled with for years. Things like resolving match queues, auto-classifying records, and improving data quality without constant manual effort.What makes this important is simple. Enterprises already know they need trusted master data. The challenge has always been the operational burden required to maintain it. Agentic AI is starting to change that equation by reducing operational cost while strengthening the data foundation needed for AI-driven decisions.We also discussed what leaders are wrestling with in 2026. Two themes stood out.First, governance is no longer just about data. It is about governing data in the age of AI.Second, organizations are facing a real tension between using data to power AI and using AI to fix their data. The right path depends on the use case, not a one size fits all strategy.It was a grounded conversation about how enterprises are moving from AI experiments to operational impact.#data #ai #datadriven #reltio #theravitshow

AI agents are only as good as the data and context behind them. At DataDriven 2026, I caught up with Sushant Rai from Reltio to talk about their latest announcements and where they see enterprise AI heading next. The big theme was clear. Context is everything. Without trusted enterprise data grounding AI systems, outcomes fall short. That is exactly where Reltio is placing its bets with its Context Intelligence Platform and the evolution of Agent Flow.We talked about how Agent Flow is moving beyond experimentation into real execution. From out-of-the-box agents for governance and operations, to a new Agent Builder that lets teams create agents with simple prompting, the focus is on making agentic AI practical for enterprises. Trust was another major point. Susant emphasized the need for observability, transparency, and explainability so organizations understand what agents are doing and why. Without that, adoption stalls. Looking ahead, Reltio is investing heavily in unstructured data processing, agent-driven data quality, expanded zero-copy integrations, and stronger identity resolution to unify enterprise data at scale.The direction is clear. Enterprises are shifting from dashboards to intelligent systems powered by context-rich data.#data #ai #datadriven #reltio #theravitshow

AI will fail without strong data foundations. At the DataDriven Conference 2026 by Reltio, I sat down with Bronwen Schumacher from ZS to talk about what it really takes to make enterprise data ready for AI.Bronwen leads the Reltio partnership at ZS, a global consulting firm deeply rooted in life sciences and healthcare. ZS has been one of Reltio's earliest partners, working together for 13 years to build modern Master Data Management solutions for global pharma companies.This year, they won Innovation Partner of the Year for their work integrating ZS's platform, ZAIDYN, with Reltio. The goal is clear. Deliver an out-of-the-box data and analytics solution that helps organizations, including smaller pharmaceutical companies, master their data faster.We also spoke about what 2026 really demands from enterprises. The focus is shifting toward Agentic MDM, where strong data quality, governance, and change enablement become the backbone for AI. Without organizational buy-in and clean, trusted data, large-scale AI programs simply do not scale.Beyond pharma, ZS is also expanding into travel and hospitality, bringing similar data discipline to customer loyalty and experience use cases.This was a grounded conversation about how real enterprises are preparing their data before chasing AI.Sharing the full interview here.#data #ai #datadriven #reltio #theravitshow

Most companies think they are building data platforms. What they are actually building is the foundation for AI agents to make decisions!!!! At DataDriven 2026, I sat down with Manish Sood, Founder and CEO of Reltio, and our conversation made one thing clear: the role of data platforms is changing fast.Reltio just crossed $185M in ARR with strong growth, but the bigger story is how they are redefining master data management for the AI era. Instead of focusing only on data storage, they are pushing toward Context Intelligence, where AI systems operate on real-time, curated, and governed data.We discussed their new AgentFlow approach, where prebuilt AI agents automate stewardship work like match resolution, profiling, and data exploration. This moves data teams from manual cleanup to intelligent automation.We also talked about speed and access. Their Lightspeed data delivery network aims to make enterprise data globally available in milliseconds so customer-facing and agentic AI systems can actually function in real time.What stood out from customers at the event is simple:- The conversation is no longer about adopting AI- It is about whether their data foundation can support itSharing the full interview here where Manish breaks down the announcements, the strategy behind AgentFlow, and what enterprises should be preparing for next.#data #ai #datadriven #reltio #theravitshow

Most AI systems look impressive in controlled environments.But the real test begins when they meet production data.In my latest conversation, I sat down with Antonio Bustamante, Co-Founder and CEO of bem on The Ravit Show, to unpack one of the hardest problems in enterprise AI today: making unstructured data reliable enough to power real decisions.We talked about why extraction alone is not the problem anymore. The harder challenge is what comes after. Validation. Business rules. Schema enforcement. Exception handling. And making sure the system does not quietly fail when confidence drops.Antonio shared how bem approaches this differently by treating unstructured data pipelines more like critical infrastructure than experimental AI workflows. Instead of relying on prompts and probabilistic outputs, their focus is on deterministic systems that enforce constraints, match against existing data, and flag uncertainty rather than guessing.We also explored why unstructured pipelines tend to fail silently, how teams should rethink data quality in a probabilistic world, and where the real intellectual property lies when building AI-powered products today.What I found most interesting is how this shifts the conversation from model capability to operational trust. Enterprises are not just asking whether AI works. They are asking whether they can depend on it when money, compliance, or customers are involved.If you are building AI products, data platforms, or automation workflows, this discussion goes deeper than the usual AI hype cycle.Watch the full interview below and share your biggest takeaway.#data #ai #bem #theravitshow

They built one of the fastest growing newsletter platforms for creators.I just published my conversation with Tyler Denk, Co-Founder & CEO of beehiiv on The Ravit Show and this one is fully practical.Instead of talking about newsletters in theory, we went inside his own newsletter, Big Desk Energy, and broke down how he actually runs it.On screen.Step by step.No fluff.We covered:* How Big Desk Energy crossed 118,000 subscribers* What is automated vs what still needs direct input* Website builder from landing page to archive* What happens the second someone signs up* How signup data powers smarter automations* Welcome journeys and re-engagement campaigns explained simply* Mistakes new creators make with automations* The post editor and what actually improves open rates* Growth tools like Boosts, Marketplace, referrals, and recommendations* Monetization through digital products, paid subscriptions, and ads* Why beehiiv charges 0% platform fees on paid subscriptionsPersonally, this conversation meant a lot to me.I use beehiiv myself to run our newsletter with 137,000 subscribers. It powers everything behind the scenes for us. Publishing, segmentation, automations, monetization.This episode felt like opening the backend of a real newsletter business and understanding how to think about it as a system, not just emails.If you are serious about building a newsletter in 2026, this is not inspiration content. It is execution content.We also have a special offer for The Ravit Show community:30% off for your first three monthsCode: RAVIT30This link auto-applies the discount:https://beehiiv.link/7twzg8I built 137K subscribers on beehiiv.Now you can see exactly how the CEO himself runs it.#data #ai #newsletter #beehiiv #creators #community #automations #theravitshow

Let's go 2026!!!! This Enterprise series is huge. Loved recording this one with real leaders who are using GenAI and leveraging right Data. Everyone is talking about GenAI in customer service. Very few are talking about what actually works in production. Next week, we are going live with a real, honest conversation on how enterprises are using GenAI with customer data to transform customer service.This is not about theory.This is not about shiny demos.We will talk about:- What GenAI chatbots and virtual agents are actually doing in production- Why most pilots fail when they hit real enterprise data- The kinds of customer questions that create the most value- How teams think about security, privacy, real-time access, and scaleThe business impact leaders really care about like resolution time, agent efficiency, and customer satisfactionJoining me are Ronen Schwartz and Miguel Navarro, who are working hands-on with these challenges inside large financial institutions.If you are building GenAI for customer-facing use cases, or planning to, this session will help you avoid costly mistakes and focus on what actually matters.Customer service is being redefined.But only for teams that get data right.#data #ai #genai #agents #governance #dataquality #k2view #theravitshow

Once teams start trusting AI to explain systems and guide decisions, expectations change. The conversation shifts from understanding to action. In Part 2 of the podcast, we go deeper into what happens next in the AI journey on the mainframe.We talk about trust and why it is the real blocker to scaling AI.How organizations move from alerts and recommendations to agentic workflows.And what safe autonomy actually looks like in real enterprise environments.Liat Sokolov from BMC Software shares how trust in AI develops over time and why it cannot be rushed.Anthony DiStauro from BMC Software breaks down how AI agents and orchestration change day-to-day mainframe operations without removing human accountability.This episode is about delegation, scale, and using AI in a way that strengthens reliability rather than putting it at risk.#data #ai #mainframe #agents #bmi #mainframe #skills #bmc #theravitshow

AI agents did not suddenly appear this year. What changed is that enterprises are finally ready to use them. At AWS re:Invent, I spoke with Ronen Schwartz, CEO of K2view, to talk about what has really shifted in the agent space from last year to this year and why this moment feels different!!!!Here is what we covered • What changed in the agent landscapeLast year was about ideas and experiments. This year is about execution. Ronen shared why agents are moving from demos into real enterprise workflows • K2View on the AWS MarketplaceWe talked about what it means for K2View to be available on the marketplace and how customers can make the most of it, from faster onboarding to simpler adoption • The AWS and K2View partnershipWe discussed how the partnership with AWS helps customers deploy agent driven use cases faster while keeping control over their data • Agentic AI and the re:Invent keynotesWe reflected on the keynote announcements and why agentic AI has become a central theme for AWS and its ecosystemIf you are trying to separate agent hype from what is actually working in production, this conversation brings a grounded perspective!#data #ai #awsreinvent #agents #agenticai #aws #enterprise #k2view #theravitshow

Cloudera and AWS Partnership is extraordinary!!!! At AWS re: Invent, I stopped by the Cloudera booth to sit down with Michelle H., who leads Global Alliances and Channels worldwide for Cloudera. She sits right at the intersection of Cloudera, AWS, and the customers who are trying to move faster with data and AI.A few highlights from our conversation • Cloudera x AWS as a strategic betThis is not a surface-level partnership. There is a Strategic Collaboration Agreement in place with AWS that goes deep into joint roadmaps and hands-on POC workshops, all focused on delivering more value for customers. • AI, but with control and flexibilityCustomers want AI, but they also want to de-risk it. Cloudera is helping with offerings like AI Studios, while still giving customers an open base and control over their data in their own AWS environment. • Hybrid as a real differentiatorCloudera is becoming a true “bridge to the cloud” for many teams, helping them move workloads between cloud, on-prem, and even the edge instead of locking them into a single pattern. • Real impact, not just slidesWe talked about work with Mercy Corps, where data and AI are helping save lives in crisis zones like Sudan and Gaza, and about a major pharma company running 100-plus GenAI use cases on Cloudera AI to bring drugs to market faster. • Innovation and sovereigntyCloudera is also leaning into Sovereign Cloud, including being a launch partner for the EU Sovereign Cloud in AMIA and working in regions like the Kingdom of Saudi Arabia.Michelle's advice to enterprise leaders was simple and practical: lean on your partners as trusted advisors, and do not wait for the perfect plan. Get started today with one use case and learn from it.#data #ai #awsreinvent #aws #agents #awspartners #agenticai #theravitshow

At AWS re: Invent this month, I spoke to one and only Ed Huang, Co-Founder and CTO of TiDB, powered by PingCAP, to talk about something many teams are quietly struggling with.The last few years in data were all about unbundling.- One database for transactions.- One for analytics.- One for vectors.That worked when workloads were separate.But AI changed the rules.As Ed put it, today the same application, or even a single AI agent, jumps across transactions, analytics, and vector search in milliseconds. No developer wants to manage five databases. Agents cannot do that at all.That is why PingCAP is betting on unification with TiDB X.One system that can:- Scale like an analytical warehouse- Behave like a transactional database- Support vectors and search natively- Run on object storage with elastic computeKeep a single SQL interface with strong guaranteesThis does not mean specialized databases disappear. Many of them will continue to win in narrow use cases.But the center of gravity is clearly shifting.As AI agents become first-class users of data systems, platforms that remove boundaries matter more than platforms that optimize for one workload in isolation.This was a sharp, honest conversation on where databases are heading in the AI era.#data #ai #awsreinvent #qlik #agenticai #sovereigncloud #aws #theravitshow

Big companies do not switch databases unless something is broken. At AWS re: Invent I spoke to Max Liu, Co-Founder and CEO of TiDB, powered by PingCAP, and Bo Liu, Head of Online Infrastructure at Pinterest, to talk about why teams like Pinterest are moving core systems back to SQL with TiDB.This was a deep, honest conversation about scale, reliability, and where databases are headed next.We covered:- Why Pinterest finally hit a wall with NoSQL after years on HBase- What really breaks first at massive scale: consistency, operations, or developer velocity- How Pinterest migrated critical graph systems to TiDB with no downtime and five nines consistency- The tension CIOs face between rock-solid stability and fast-moving AI teams- Whether embeddings and AI workloads should live inside the same database as transactions- How agentic workloads are changing what enterprises expect from a databaseThis was not about hype. It was about real systems, real migrations, and real trade-offs.#data #ai #awsreinvent #agents #agenticai #aws #database#enterprise AWS Partners #pingcap #theravitshow

Most cloud conversations ignore one simple truth. AI only moves as fast as your connectivity. At AWS re:Invent, I spoke to Ed Baichtal, Principal Engineer at Equinix, to talk about what actually sits underneath modern cloud and AI architectures!!!!A few takeaways from the conversation- Equinix operates more than 270 data centers worldwide and acts as the physical home for servers, networks, and cloud on ramps- Equinix is the largest AWS on-ramp provider, with native AWS connectivity built directly into its data centers- The Equinix Fabric Cloud Router is now available on the AWS Marketplace, making multi-cloud connectivity much simpler- Customers can virtually connect up to 25 Gbps to AWS across different regions without deploying physical hardware- AI workloads are pushing serious demand for higher throughput and more reliable connectivity between cloudsOver 3,000 customers are already connected through the Equinix Fabric, which means new connections can happen virtuallyOne customer moved massive volumes of data to AWS in under 30 days using the Fabric Cloud RouterLooking ahead, Equinix plans to introduce Fabric Intelligence in Q1 2026 to make multi-cloud and NeoCloud connectivity even smarterIf you are building AI or multi-cloud systems, this layer matters more than most people realize.#data #ai #awsreinvent #aws #agents #awspartners #agenticai #theravitshow

Developers are not just writing code anymore. They are starting to run a virtual team. At AWS re:Invent, I had a conversation with Jemiah Sius, VP, Market Strategy and Developer Relations, from New Relic about how AI is changing the day-to-day life of developers. This was one of those chats that makes you pause and rethink how software will be built very soon.Here is what stood out-- Agentic AI is becoming real for developers Teams are excited about agents that behave like a digital team or a virtual SRE, taking care of reliability and performance while developers focus on building features-- Developers are becoming orchestrators Over the next 6 to 8 months, the role of the developer is shifting. Less time writing every line of code, more time directing agents and tools. This shift is already driving a big jump in productivity-- Observability matters more than ever As agents start working across multiple LLM servers and interacting with other agents, visibility becomes critical. Without observability across the full agent layer, things can quickly create more work instead of less-- New Relic and AWS coming together We talked about the New Relic integration with AWS Q, which brings observability data directly into AWS DevOps workflows, and the new security agent that surfaces real production data on vulnerabilitiesIt was great catching up with Jemiah again and hearing how New Relic is thinking about the future of developers and reliability.#Data #AI #AWSRecipes #NewRelic #AgenticAI #Security #MCP #reinvent #NewRelic #TheRavitShow

Fantastic catching up with Camden Swita, Head of AI at New Relic at AWS re:Invent last week to discuss the biggest changes in the world of Agentic AI over the last year! We talked about how New Relic is connecting the dots at AWS re:Invent with major announcements including:- The Security RX agent, an AI agent that automates the process of finding and remediating security vulnerabilities in runtime, saving engineers a huge amount of time- Integrating the Model Context Protocol (MCP) server with AWS DevOps agents to ensure agents can access live context and make informed decisions from production- Camden also shared advice for leaders entering the agent tech space: don't treat observability as a secondary concern, as end-to-end tracing will be "fundamentally essential" for compliance and performanceWatch the video to learn more!#Data #AI #AWSRecipes #NewRelic #AgenticAI #Security #MCP #reinvent #NewRelic #TheRavitShow

Most companies want AI agents talking to their customers. Very few have the data to back it up. I had a blast chatting with Ali Behnam, Founder of Tealium on The Ravit Show at AWS re:Invent. If you work with customer data or AI, this one is worth watching.Tealium helps organizations bring customer data together in one place and use it in real time across marketing, product, and AI use cases. I keep hearing their name when people talk about getting data ready for AI, so I sat down with Ali to go deeper.We spoke about- Who uses Tealium and the main problem they solve for enterprises that are serious about customer data- Why agentic AI is such a big theme this year and what is actually changing inside large companies- Why so many AI agent projects get stuck in pilots and what is missing to make them work in the real world- How Tealium works with AWS, and what being built on AWS unlocks for customers- What Ali wants customers to be able to do by next re:Invent that they cannot easily do todayIf you are trying to make AI agents useful for real customers, not just in demos, I think you will find this helpful.#data #ai #aws #reinvent2025 AWS Partners #databases #agents #agenticai #tealium #theravitshow