Ep 91 | Beyond the POC: AWS's Playbook for Enterprise AI Success

Cloudera, Inc.

Sep 17, 2026

Most AI pilots never make it past the demo phase.

AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.

In this episode of The AI Forecast, Paul Muller sits down with Praveen Jayakumar, who leads Machine Learning Solution Architecture for Amazon Web Services (AWS) across Asia Pacific and Japan, to explore how enterprises can give AI projects a realistic path to production.

Praveen shares what he’s learned working with organizations on machine learning and generative AI deployments, including why teams should build for production while they’re still proving the concept. Observability and evaluation become particularly important as models change, costs scale, and AI systems begin interacting with enterprise data.

Paul and Praveen talk about:

Why promising AI proofs of concept stall before production
How to define success and kill criteria before an AI project begins
Why observability should be built into the proof of concept
How evaluation frameworks make it easier to test different AI models
Why the most powerful frontier model may be the wrong choice
How model selection affects the unit economics of enterprise AI

Recorded at EVOLVE26 Singapore, this episode offers a practical look at what happens after the AI demo, when teams have to decide what’s ready to scale and what’s better left behind.

If you’re responsible for enterprise AI, this episode will help you build a path to production and keep zombie projects from consuming resources long after their expiration date.

Stay in touch with Praveen:*
Praveen Jayakumar on LinkedIn: https://www.linkedin.com/in/pjpraveen/

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