Enterprise AI isn’t failing because of the models.It’s failing because of data.

In this episode of the Alchemist Accelerator Influencer Series, Ravi Belani sits down with Sidney Rabsatt, Chief Product Officer at MindsDB and former AI and infrastructure leader at Google Cloud, AnyScale, and F5 Networks.

Sidney breaks down why connecting AI to enterprise data is far harder than most teams expect and where the real unlocks are hiding. From messy, fragmented data systems to the false promise of endless ETL projects, this conversation explores what it actually takes to get real value from AI inside organizations.

You’ll learn:

• Why AI struggles when enterprise data is fragmented
  

• The hidden cost of traditional ETL and data clean-up
  

• How AI can work directly on messy data without years of prep
  

• Where startups can find real opportunities at the AI + data layer
  

• Why focusing on the use case matters more than the technology
  

If you’re building, deploying, or investing in enterprise AI, this episode will change how you think about data, workflows, and where real value is created.

Manjeet Singh lives where AI hype meets real enterprise reality. He is the Senior Director of Product Management at Salesforce AI and Agentforce, and previously led product teams at Iron Mountain and ServiceNow. He spends his days shipping AI products that millions of people actually use.

In this episode, Manjeet sits down with Ravi Belani to talk about what is really working in enterprise AI adoption, and where a lot of teams are still stuck in POC land.

They explore:

• The real state of enterprise AI and agentic adoption in 2025

• Why so many AI projects stall at proof of concept

• Where agents are already driving real KPIs, from software development to customer support

• How to tell if an AI opportunity is likely to deliver real ROI for an enterprise

• Why constrained, well defined workflows win first

• Vertical vs horizontal AI, and how to build more than “just an LLM wrapper”

• Why regulated and high cost of failure domains can be great markets for startups

• The missing “toolchain” for agents, from testing and evals to observability and tracing

• How to think about moats, domain expertise, and speed in the AI platform era

• Why Manjeet is excited about the intersection of AI, healthcare, and preventative fitness

If you are building, buying, or investing in enterprise AI, this conversation will give you a grounded view of what works, what does not, and where the next opportunities are likely to appear.

Marcelo Calbucci has been building tech for over 30 years. He’s a serial entrepreneur, multi patent holder, former engineering leader at Microsoft and Amazon, and someone known for having bold, contrarian takes on where AI is actually heading.

In this episode, Marcelo sits down with Ravi Belani to challenge some of the biggest assumptions people have about AI right now.

They dig into:

• Why layoffs and job freezes might have nothing to do with AI

• Why AI isn’t replacing software engineers anytime soon

• Where AI is being massively overused

• The areas where AI is actually underused and full of opportunity

• Why founders need to stop outsourcing strategy to LLMs

• How to use AI to disconfirm your ideas instead of chasing confirmation bias

• Why the next big wave of opportunities may come from “multiplayer AI”

• What roles AI is likely to replace, and which roles will remain essential

• Why adaptability will matter more than anything else in the next decade

Marcelo also shares which startup opportunities he’d pursue today, how he thinks teams will collaborate with AI agents in the future, and the one piece of advice he’d give founders in 6 words: We’re entering the world of adaptability.

If you want fresh, honest, and thought provoking perspectives on AI and the future of work, this conversation is a must watch.