The Ravit Show

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 production

One 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 outcomes

One 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 systems

One 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 optimization

The AI conversation often focuses on models.

But the real story may be the infrastructure required to make those models work at scale.

#CiscoLive #AI #Networking #Innovation #TheRavitShow

For years, data engineering has been about building pipelines, warehouses, dashboards, and choosing the right tools. But what if we've been solving the wrong problem? I recently sat down with Sai Sundar from WALT, who has spent decades building data platforms at Apple, Yahoo, LinkedIn, Chime, and GEICO. One idea from our conversation really stood out. Companies don't need more data tools. They need better business outcomes.Sai explained how data teams often work in silos. Engineers build pipelines. Business teams ask questions. Analysts sit in the middle translating requirements. The result is slow decisions, duplicated work, and endless back-and-forth.The next evolution isn't another platform.It's creating systems that understand business goals, work with your existing data stack, and help organizations make trusted decisions faster.Some of the topics we covered:* Why data has historically been treated as a second-class citizen* Why business outcomes matter more than adopting the latest technology* How AI is changing the role of data engineering* Why trust and transparency are becoming essential in enterprise AI* What the future of conversational data engineering could look likeThis conversation isn't just about AI.It's about rethinking how data teams create value for the business.#data #ai #dataengineering #walt #theravitshow

Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show.

He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI.

His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff.

Ranjith calls this the chasm. He left to close it.

A few things from our conversation that stuck with me.

Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering.

Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and production.

Tribal knowledge should not live in one person's head. Why you exclude Q2 returns should not walk out the door when an analyst quits. It should live in the system.

And data quality is where most data projects quietly die. You can build the most elegant pipeline in the world, but if one number is wrong, trust is gone. Once trust is gone, nobody uses the platform.

The part I keep thinking about. Tools give you capability. They do not give you the outcome. The outcome still takes people and months of work. That gap is the real problem, and it is the one Ranjith is now building to solve.

Worth your time if you care about where data engineering is heading.

#data #ai #dataengineering #walt #theravitshow

PostgreSQL is no longer just a database conversation. It's becoming a platform conversation. I had the opportunity to sit down with Claire Giordano, Principal Group PM Microsoft near Stanford University right after POSETTE: An Event for Postgres to discuss the biggest takeaways from one of the largest PostgreSQL events in the world.

A few themes stood out:

* PostgreSQL adoption continues to accelerate across organizations of every size

* The ecosystem around Postgres keeps expanding, making it easier to build modern data and AI applications

* AI was impossible to ignore, but the conversation wasn't about replacing databases. It was about how databases can provide the context, reliability, and foundation AI systems need

* The community remains one of PostgreSQL's biggest strengths, with contributors and companies working together to push innovation forward

One of the most interesting parts of our discussion was where PostgreSQL goes next.

As organizations look to build AI-powered applications, support real-time workloads, and simplify their data architectures, PostgreSQL continues to find itself at the center of those conversations.

The database landscape keeps evolving, but PostgreSQL's momentum shows no signs of slowing down.

In this episode, Claire shares:

* Her biggest takeaways from POSETTE 2026

* The PostgreSQL trends generating the most excitement

* Surprising announcements and discussions from the event

* How AI is influencing the PostgreSQL ecosystem

* What this year's event tells us about the future of PostgreSQL

* What the community should be paying attention to next

#data #ai #postgresql #database #opensource #theravitshow