Enterprise AI startups don’t fail because the tech is weak. They fail because they sell the wrong way.

In this episode of the Alchemist Influencer Series, Ravi Belani sits down with Gnani Palanikumar, former Head of Product at Apigee (acquired by Google for $725M) and chair of Alchemist’s GenAI track.

Gnani shares a tactical, operator-level playbook for turning AI pilots into real enterprise adoption.

They discuss:

• Why traditional enterprise org structures break AI deployments
  

• The mismatch between 2-year AI cycles and 12-month buying cycles
  

• Why founders should sell at the department level, not enterprise-wide
  

• How to find pre-approved budgets and avoid multi-stakeholder gridlock
  

• The “4-week proof” tactic for voice AI in financial services
  

• Why domain expertise and fractional operators matter
  

• How to identify whether a company is AI-native or stuck in SaaS thinking
  

• Why AI projects must deliver value within a quarter
  

Gnani’s 5-word takeaway says it all:

Lead with trust. Deliver proof.

If you’re building enterprise AI, this episode is pure tactical insight.

Agentic AI is everywhere right now, but most teams are still struggling to price it, deploy it, and make it work in production.

In this Fireside Chat, Ravi Belani sits down with Vibhor Rastogi, who leads AI investing at Citi Ventures, to unpack what’s actually happening inside enterprise AI adoption and what founders often get wrong.

They dive into why pricing agentic software is still an unsolved problem, what’s genuinely overhyped in the “agent” wave, and how startups can build defensible AI businesses in a world of reskinned workflows and runaway costs.

Topics covered include:

• Why seat-based, consumption-based, and outcome-based pricing all break in different ways
  

• How CIOs think about predictability, guardrails, and runaway AI costs
  

• What investors really want to see from agentic AI startups
  

• Why many “agents” today are just rebranded APIs and workflows
  

• The importance of memory, learning, and real autonomy in agentic systems
  

• How founders can solve the data cold-start problem through design partnerships
  

• Why an “Agentic Ops” layer may be the missing piece for enterprise adoption
  

If you’re building, investing in, or deploying agentic AI, this episode will help you separate signal from noise.

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.