Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agent vs Agentic AI

Most people use the terms AI Agent and Agentic AI like they mean the same thing.

They don’t.

The difference isn’t just semantic.
It’s architectural.

Here’s how the tech stack evolves from AI Agent → Agentic AI 👇

1. Intelligence models
- AI Agent typically relies on a single LLM with prompt → response workflows.
- Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems.

2. Architecture & frameworks
- AI Agent often follows a single-agent, linear execution flow.
- Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen.

3. Memory systems
- AI Agent works with session memory, short-term embeddings, and basic caches.
- Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases.

4. Tool usage & actions
- AI Agent uses predefined tools and function calling triggered by users.
- Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries.

5. Knowledge & retrieval
- AI Agent typically uses basic RAG pipelines with static retrieval.
- Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs.

6. Orchestration & workflows
- AI Agent runs sequential flows and simple backend automation.
- Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles.

7. Decision making
- AI Agent is reactive and prompt-driven.
- Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops.

8. Deployment
- AI Agent is often deployed as chatbots, copilots, or API-based assistants.
- Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems.

9. Monitoring & observability
- Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops.

10. Learning & improvement
- AI Agent improves through prompt iteration and occasional fine-tuning.
- Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks.

AI Agent = intelligent responder.
Agentic AI = autonomous system with goals, memory, tools, and orchestration.

One answers questions.
The other executes objectives.

Are you building smarter responses or autonomous systems?

Anthropic made Learning AI easier.

They’ve released a complete set of FREE courses designed to help you understand Claude, AI agents, APIs, and the Model Context Protocol (MCP) - the foundation behind modern AI applications and agent systems.

This learning path covers everything from beginner concepts to advanced agent development workflows.

Here’s what you can learn:

• Claude 101 - Understand core features and everyday AI workflows
• Claude Code in Action - Use Claude to accelerate development tasks
• AI Fluency: Framework & Foundations - Learn how to work effectively with AI
• Building with the Claude API - Develop real AI-powered applications
• Introduction to Model Context Protocol (MCP) - Connect AI with tools and data
• MCP Advanced Topics - Deep dive into advanced implementations
• Claude with Amazon Bedrock - Deploy Claude in AWS environments
• Claude with Google Cloud Vertex AI - Production AI workflows on GCP
• Introduction to Agent Skills - Build reusable AI agent capabilities

These courses are especially useful if you want to learn:
AI Agents • MCP • LLM Applications • AI Automation • Developer Workflows

No fluff.
No paywall.
Just practical AI education directly from the company building Claude.

Get all the courses here:
https://lnkd.in/gAmrrsBK

If you’re serious about understanding how modern AI systems actually work, this is one of the best free starting points available right now.

Save this for later - you’ll want to come back to it.

Google just quietly released an AI tool for every layer of how humans create things.

Research. Design. Code. Video. Agents. Models.

All of it. At once.
Connected. Intentional. Ecosystem-scale. 

The tools nobody is talking about enough:

Jules - an asynchronous coding assistant that works across entire repositories, not just individual files. It understands the broader structure of a project and helps manage complex codebases.

Google A2A - a multi-agent communication protocol designed to let AI agents collaborate across different frameworks. This is infrastructure for the next generation of agent systems.

Stitch - converts prompts directly into complex UI layouts, turning ideas into interface designs within minutes. A major shift for how design workflows could work.

FileSearch API - a configurable RAG pipeline accessible through a single API call. What previously required weeks or months of engineering can now be integrated almost instantly.

Flow - a cinematic storytelling platform that lets creators generate scenes, narratives, and video content with AI-driven workflows.

Gemini 3 Pro - Google’s most advanced reasoning model so far, designed for deeper analysis, complex tasks, and next-generation AI systems.

This isn't a product launch.

It’s a full-stack AI ecosystem.

From foundational models → to research tools → to design → to development → to autonomous agents → to video creation.

Everything sits on top of the same AI foundation.

That means ideas can move from research → design → code → content faster than ever before.

The real strategy here isn’t individual tools.
It’s ownership of the entire creation pipeline.

And when one company controls every layer of that pipeline, the innovation speed compounds.

The AI race isn’t just about better models anymore.

It’s about who builds the ecosystem developers, creators, and companies build on.

People still use AI like a chatbot.
The real power starts when it becomes your execution layer.

Claude Code isn’t just another AI tool.
It acts like an operating system for building, testing, and shipping real work.

Instead of writing prompts and copying outputs, you can actually run workflows, analyze codebases, connect tools, and automate tasks from one place.

That’s the shift most teams haven’t fully understood yet.

Traditional AI tools help you think faster.
Claude Code helps you execute faster.

It moves through a structured loop:
analyze → plan → build → automate → scale.

You can connect repositories, files, databases, and internal tools.
Run commands.
Generate code.
Debug systems.
Automate repetitive work.
And build reusable skills that keep improving over time.

It also works across environments, which means you’re not jumping between ten different tools to get one workflow done.

The biggest difference?
It behaves less like a chatbot and more like a technical teammate that can actually take action.

When used properly, it becomes a working layer on top of your stack — not just a place to ask questions.

Most people experiment with AI.
Few build repeatable systems with it.

That’s where the real leverage is.

AI becomes valuable when it moves from answering questions to executing workflows.

Start treating it like an operating system for work, not just a prompt tool.

The agent gave a brilliant answer.
Then someone asked, “Where did that data come from?”
Silence. That’s when you realize intelligence needs infrastructure.

Agentic AI isn’t just reasoning.
It’s retrieval, validation, lineage, governance, and monitoring - all working quietly in the background.

Here’s what that foundation looks like:

- Data Ingestion
Pulls in structured and unstructured sources, standardizes formats, and attaches context before anything reaches an agent.

- ETL / ELT Pipelines
Transforms, validates, and loads data into usable systems so downstream workflows aren’t built on chaos.

- Data Versioning
Tracks changes, creates reproducible snapshots, and enables rollback when something breaks or drifts.

- Vector Pipelines
Chunks content, generates embeddings, and indexes it for semantic retrieval in RAG and agent workflows.

- Metadata Management
Registers datasets, records ownership, and enables search and observability across the ecosystem.

- Data Governance
Enforces policies, access controls, and compliance rules to prevent misuse and ensure accountability.

- Data Warehouse / Lakehouse
Stores refined datasets that power analytics, feature engineering, and intelligent querying.

- Streaming Data
Processes real-time events so agents can react, trigger actions, and operate dynamically.

- Data Lineage
Maps where data originated, how it transformed, and who depends on it.

- Data Quality & Reliability
Applies validation checks, detects anomalies, and keeps pipelines healthy under pressure.

At the center is the agent itself -
retrieving context, reasoning over data, planning actions, learning from feedback.

But without the layers beneath it,
even the smartest answer becomes a liability.

Intelligence is impressive.
Traceable, governed intelligence is enterprise-ready.

If someone asks where your agent’s answer came from, can you show them?

If you learned AI even three years ago, you’re already studying history.

The conversation has moved far beyond models and prompts. What we’re witnessing now isn’t just incremental improvement, it’s a fundamental shift in how intelligence is built, deployed, and experienced inside real systems.

AI didn’t suddenly appear with ChatGPT.
It evolved through decades of breakthroughs, each solving the limitations of the era before it - gradually transforming software from rule execution into adaptive intelligence.

Understanding that evolution is what reveals where AI is actually heading next.

From Rules → Learning
AI moved from hard-coded logic toward systems capable of learning patterns directly from data instead of predefined instructions.

From Learning → Representation
Deep learning and transformers introduced contextual understanding, enabling language comprehension, perception, and multimodal intelligence at scale.

From Models → Platforms
Foundation models and Retrieval-Augmented Generation turned AI into reusable platforms connected to dynamic, real-world knowledge sources.

From Assistants → Agents
AI progressed from answering questions to planning actions, reasoning through problems, and executing complex multi-step workflows autonomously.

Where AI Is Heading (2026+)
Intelligence is moving toward edge computing, embodied AI, persistent memory, and systems embedded seamlessly into everyday environments.

Every generation of AI didn’t replace the previous one, it stacked on top of it.

Many organizations struggle today because they attempt to adopt the newest layer without understanding the foundations underneath. Real advantage comes from recognizing how these layers connect:

Rules → Learning → Reasoning → Agents → Autonomous Systems

We’re not reaching the end of AI evolution.

We’re entering the phase where intelligence stops being a feature and becomes a system layer woven into everything we build.

#AI

If you think an AI agent is just an LLM connected to tools, you’re only seeing the surface.

What actually powers a production-grade AI agent is a layered intelligence architecture - where memory, reasoning, governance, and infrastructure work together to transform responses into reliable autonomous behavior.

Modern AI systems are no longer single models.
They are orchestrated systems designed to think, plan, adapt, and operate safely at scale.

This visual breaks down the internal anatomy of a real-world AI agent across three evolving layers:

From Execution → Coordination
The Basic Layer handles core operations — processing inputs, orchestrating prompts, invoking models, executing tools, managing errors, and delivering structured responses.

From Coordination → Reasoning
The Intermediate Layer introduces structured intelligence. Agents decompose tasks, plan execution strategies, validate outputs, manage context, monitor costs, and maintain observability for reliable performance.

From Reasoning → Autonomy
The Advanced Layer enables true production intelligence through long-term memory, multi-agent collaboration, governance guardrails, adaptive learning, self-reflection, and scalable deployment infrastructure.

Each layer doesn’t replace the previous one — it builds on top of it.

Organizations struggle with AI adoption not because models are weak, but because architecture is incomplete. Real AI capability emerges only when these layers operate as a connected system:

Execution → Reasoning → Governance → Autonomy

We’re moving toward a future where AI isn’t an application feature.

It becomes an operational layer embedded into every digital system we build.

I'm Ravit Jain — the Founder & Host of The Ravit Show, a platform dedicated to meaningful conversations with the people shaping the future of Data and AI.

What started as a passion for learning from industry leaders has grown into a global media platform that reaches millions. Today, I spend my time interviewing experts, covering major tech events, and working closely with companies to help them share their stories in a way that truly connects with the community.

Over the past few years, I’ve had the chance to:

Host 550+ interviews with data and AI leaders, founders, engineers, and product thinkers

Collaborate with 200+ companies — from fast-moving startups to nearly every major tech giant

Cover 100+ conferences across the world, sharing real-time insights and backstage conversations

Support 200+ partner campaigns to amplify their product launches, webinars, reports, and community efforts

My focus is always on creating content that is useful, grounded, and easy to understand — no hype, just real value. Whether it's discussing GenAI, data quality, modern architecture, or what it takes to scale AI responsibly, my goal is to keep the conversation practical and community-first.

If you’re building in this space or want to share what you’re working on, I’d love to connect and learn more.

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Ravit Jain

Most companies think they are building data platforms. What they are actually building is the foundation for AI agents to make decisions!!!! At DataDriven Conference 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 for customers at the event is simple:

- The conversation is no longer about adopting AI
- It is about whether their data foundation can support it

Sharing 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

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

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 Orlando

One 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

BREAKING announcements from the front row at New Relic Advance 2K26. Today’s announcements from Ashan Willy (CEO, New Relic) and Brian Emerson (CPO, New Relic) made one thing clear: This is not just a product update. It is a shift toward intelligent, agent-driven operations. Here are the biggest announcements from the stage 👇

1. The Agentic Platform for operational AI
New Relic introduced a platform designed to let teams build and orchestrate AI agents that can investigate incidents, automate workflows, and reduce manual toil across production environments. The idea is to move from passive monitoring to systems that actively execute tasks

2. SRE Agent to automate investigation and response
A new AI-driven SRE agent uses telemetry signals across logs, metrics, traces, and dependencies to prioritize alerts, identify root causes, and guide remediation workflows

3. Intelligent Root Cause Analysis and smarter alerting
The platform now focuses on reducing noise and narrowing down the most probable failure paths quickly so teams can respond with clarity instead of chasing alerts

4. Intelligent Workloads linking performance to business impact
New capabilities aim to connect technical telemetry directly to user experience and revenue impact so organizations can understand not just system health, but business risk

5. Stronger OpenTelemetry and telemetry pipeline simplification
New Relic is also investing heavily in making telemetry ingestion and pipeline health more transparent, helping customers scale observability without increasing operational overhead

But what stood out beyond the announcements was hearing customers talk about this shift.

I joined the session “Customer Panel: Intelligent Observability in Action” with leaders from Nordstrom, Turo, and New York Life alongside New Relic’s team.

The conversation was less about dashboards and more about outcomes.

Customers talked about:

• Managing the growing complexity of AI-driven systems
• Moving faster without increasing operational risk
• And using observability as a way to connect engineering work to business impact

It was clear that for them, observability is becoming a strategic capability, not just a monitoring tool.

Why this matters

Engineering teams today lose a huge amount of time to firefighting, alert noise, and manual investigation. The promise of these announcements is simple:

- Reduce operational toil
- Speed up decisions
- And let teams focus on innovation instead of maintenance

If New Relic can execute on this vision, observability platforms will stop being dashboards and start becoming operational decision systems

From what I am hearing in sessions and hallway conversations, this shift toward agent-assisted operations is arriving faster than most organizations expected.

More updates coming live from the floor.

#Data #AI #NewRelic #Observability #AI #SRE #DevOps #PlatformEngineering #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

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

Some events are about trends. Others signal where the industry is actually going next. I am in San Francisco today, getting ready for New Relic’s Advance tomorrow, and this one feels like the latter.

Over the past year, almost every conversation with engineering and platform teams has circled back to the same theme. AI is helping teams move faster, but it is also making systems harder to manage. More signals, more dependencies, more pressure to react quickly.

Register here, you can join virtually too:
https://lnkd.in/dsCfKaFC

That is why this event matters. We are expecting one of the biggest waves of product releases New Relic has announced so far, all focused on helping teams move from simply seeing issues to actually resolving them faster and with more confidence.

I will be there with Aditi Jain, meeting teams, attending sessions, and sharing what actually matters once the announcements land.

If you have been thinking about joining, this is your last chance to register. And if you cannot make it in person, the virtual option makes it easy to follow everything as it happens.

Register here, you can join virtually too:
https://lnkd.in/dsCfKaFC

Should be an interesting one. Stay tuned.

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Let's do this next week!!!! If you are coming to the Gartner Data & Analytics Summit in Orlando, make sure you add this to your calendar. I will be at the Data & Analytics After Hours gathering on Monday, March 9th from 5:00 to 6:30 PM in the Exhibitor Showcase, and it is shaping up to be one of the best opportunities to connect with the community outside the sessions.

What I like about these meetups is that the conversations are more open. People share what is actually working, what is not, and what they are trying next. I am looking forward to meeting fellow practitioners, creators, and leaders who are building in the data and AI space.

Several familiar faces from the industry will be there as well, including Aditi Jain, SCOTT TAYLOR, Kate Strachnyi, George Firican, and Andreas Welsch. It should be a great mix of perspectives and energy.

If you have not registered yet, you can still join using the link and code below for a discount.

You can learn more about the conference here:
https://lnkd.in/gdGzXXf5

If you plan to register, you can also use my code THERAVITSHOW at checkout to receive 450 dollars off.

If you will be there, come say hello. Always happy to meet people from the community in person.

#data #ai #gartnerda #gartner #gartnerpress #analytics #agents #theravitshow

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

Bangalore always brings great energy when it comes to data, cloud, and AI conversations. Excited to be heading there the second edition of the Commvault Cloud Summit 2026 on March 5th. More details here
https://lnkd.in/daHXC4KT

I will be hosting a special panel with Tim Zonca, VP Product and Portfolio Marketing, and Woon Ho Jung, CTO Cloud Native at Commvault, where we will focus on something many teams are actively dealing with today protecting AI workloads, data pipelines, and building true cloud native resilience.

As AI adoption grows, Cloud, Platform, DevOps, and SRE teams are under pressure to ensure systems are not just fast but reliable, recoverable, and secure. This session will unpack what AI ready infrastructure actually requires and how teams can design resilience into their platforms from day one.

If you are working on modern cloud platforms or data systems, this summit will be worth your time.

More details here
https://lnkd.in/daHXC4KT

#data #ai #cloud #shift2026 #aiprotection #devops #sre #theravitshow

Most conversations about AI and analytics stay at the surface. They talk about tools, trends, and predictions. But very few talk about how work is actually changing for data teams. That is why the upcoming Alteryx Virtual Summit on March 10 looks interesting.

The theme of the live session says it all:
Will AI Take Your Job? Not If You Build It First.

This is not just a catchy title. It reflects what many analysts and data professionals are dealing with right now. AI is not replacing roles overnight, but it is changing how work gets done. The real shift is that analysts are moving from reporting numbers to building automated workflows, decision systems, and AI-powered processes.

The summit brings together practitioners and leaders discussing how this shift is playing out in real teams. The focus is on how analysts can adapt, how organizations are integrating AI into workflows, and what skills actually matter as automation becomes more common.

Beyond the live event, the summit hub also includes a large library of on-demand sessions. These sessions show how analytics and AI are being applied in real workflows, not just demos. You get examples of how teams are automating processes, scaling analytics across the business, and turning insights into actions faster. That is what makes this conversation useful. It is less about predicting the future of AI and more about understanding how the role of the analyst is evolving today.

Love the lineup —

- Phil Simon, Award-winning author and workplace technology expert

- Marouf Malik, AI Lab Leader, RSM UK

- Kate Butcher, Director of Product Management, Alteryx

- Michael Peter, Director, Solutions Marketing, Alteryx

If you work in analytics, data, or AI, this is the kind of discussion that helps you stay ahead of the shift instead of reacting to it later.

You can explore the summit and register here:
https://lnkd.in/euySwvAN

#data #ai #analytics #sql #api #business #alteryx #theravitshow

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