AI doesn't need more data. It needs more context!!!!That was one of the key themes from my conversation with Josh Good from Qlik at Snowflake Summit on The Ravit Show. As organizations move beyond AI experimentation, the focus is shifting toward governance, trust, and ensuring AI understands the business context behind the data.We also discussed how partnerships between Qlik, Snowflake, and platforms like ServiceNow are helping customers connect data, analytics, and AI into a more unified ecosystem. The future of enterprise AI won't be built by a single platform. It will be built through connected ecosystems working together.#Data #AI #SnowflakeSummit #Snowflake #Qlik #DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow

Spent a day at Snowflake Summit in San Francisco this week, and one theme came up in almost every conversation: AI is only as good as the data behind it. I had the opportunity to sit down with Andy Iyengar from Qlik on The Ravit Show, and our discussion went beyond AI hype.A few key takeaways:* Modernizing the data estate is no longer optional. Organizations need trusted, governed, and accessible data before they can scale AI initiatives* Moving data into Snowflake is only part of the journey. Data quality, integration, governance, and readiness remain some of the biggest challenges for enterprises* Agentic AI is pushing organizations toward connected ecosystems where data, analytics, and AI work together instead of operating in silos* The expanded collaboration between Qlik and Snowflake reflects where the industry is heading: helping customers accelerate AI adoption by making data easier to trust, manage, and activateWhat stood out to me most was the focus on outcomes rather than technology. The conversation wasn't about building AI for the sake of AI. It was about creating a foundation that allows organizations to confidently move from experimentation to real business value.#Data #AI #SnowflakeSummit #Snowflake #Qlik #DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow

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

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

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

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

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

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

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

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