Techerati asked two data and AI leaders what comes before launch. 

This article began with a simple question: What happens inside an organisation before an AI tool goes live? 

The obvious assumption is that there’s a preparation phase. Months spent cleaning data, settling governance questions, aligning teams, and building technical foundations before a product reaches customers or employees. But does it actually work that way? We put that assumption to two experts, one in data engineering, the other in enterprise data and AI architecture.  

“I don’t see that, at least within our organisation, as one big preparation beforehand”, says Artur Yatsenko, Director of Data Engineering at Urban Sports Club, a Germany-based sports and wellness platform operating across Europe. “It’s mostly a continuous discovery of the things we can do better and the capabilities we currently have. And those capabilities could be either resources, people or expertise.” 

“AI is not approached differently”, he adds. “It is approached the same way as the product would be approached.” 

Naveen Kanneganti is a data, analytics and AI leader who has spent more than two decades working across consulting, implementation, enterprise architecture, and global platform strategy. For him, the preparation phase is less a checklist than a leadership discipline. 

“As the MVPs (minimum viable products) run in parallel, the conversation should shift from ‘Can we build this?’ to ‘Can we operate this reliably, repeatedly and economically at scale?’”, he says. “And that’s where many organisations underestimate the real work.” 

Read the full piece here to find out where they land and why “readiness” might be the wrong word for it. 

Editor’s note: Naveen Kanneganti contributed to this article in a personal capacity. His comments draw on 20 years of experience in data, analytics and AI and do not represent the views of his employer.