AI- stuck in the petri-dish paradox

Legacy complexity is a big reason for the AI pilot quicksand. Most pilots stall, not because the model is weak, but because the surrounding environment is not ready to operationalise it. Let's get a closer look at this AI dead-end.

- By Pratima H

Jayaprakash Nair, Global Head of Data and AI - Lab and Capability Center, Altimetrik gives us a tour of ALTI AI Adoption Lab- with the lens of AI-production gaps, real data loads, integration points, compliance constraints and performance expectations, stress tests, transfer learning issues, data drift, overfitting/underfitting a model and why AI projects fail. And of course, the Polanyi Paradox.

What spawned this lab and how much does it contribute to the company's overall customer footprint?

The creation of the ALTI AI Adoption Lab came from a clear trend we observed across industries. Many enterprises were experimenting with AI pilots, but only a very small percentage were able to move beyond proof-of-concept into production. The challenge was not model access or data alone, but the engineering discipline, governance, security and integration required to deliver AI safely and at scale. The ALTI AI Adoption Lab was built to close this gap and provide a structured path from idea to production. Today, it plays a central role in our AI first strategy by helping enterprises rapidly test use-cases, validate outcomes in real operating conditions and deploy them with measurable business impact.

How much work is coming out of India? Is it in the similar space as Al-as-a-service solutions offered by some vendors?

India continues to be a major innovation and delivery hub for these engagements. While the lab includes capabilities that may appear similar to AI as a service, it goes beyond simply offering access to models or tools. The ALTI AI Adoption Lab is designed for enterprise readiness, trust, scalability and customisation, which makes it fundamentally different from a plug and play AI service model.

Can you explain the key points as well as challenges in the lab-to-factory cycle?