The IT Pro Podcast: Can AI and IT teams coexist?

It may seem like a match made in heaven, but the two functions often wind up butting heads

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The IT Pro Podcast: Can AI and IT teams coexist?

AI is increasingly working its way into organisations’ main IT environments, and as a cutting-edge field that relies heavily on technology infrastructure to run effectively, you might assume that IT would be enthusiastic supporters - but this isn’t always the case.

In fact, far from being bosom buddies, in many instances IT and AI practitioners can find themselves locked in turf wars and disputes over support and resourcing. In this episode of the IT Pro Podcast, we sit down with Nvidia’s director of technical product marketing Luke Wignall, to discuss some of the root causes of these conflicts, and how the two groups can learn to play nicely together.

Highlights

“Keep in mind that historically, large data analytics things have happened in clusters that have existed more in the corner of the data centre, and tended to operate around special budgets, special teams, whatever. And now we're seeing this democratisation of AI and data science, which means it's coming over into the core data centre, which is a very different IT team.”

“I had a senior IT person at an automotive manufacturer tell me, ‘when I saw this meeting on my calendar this morning, I had no idea I would leave this meeting excited about core data centre 2U servers again’. Like, it's just not something they think about. Right? That's a decision that was made, I don't know how long ago, and so it's it's an awakening that's occurring even at the leadership level. And then they wouldn't be interested in something as fundamental as that core server building block, if it weren't for the fact that the rest of this is an awakening for them as well. They see the value, they hear the value, they witness it. And so they know the importance.”

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“At Nvidia, there's a team that combines both IT infrastructure and AI. And, in fact, the reason we've merged these two teams was to try to solve some of these problems. And early on, one of the metrics that came out of that team was, from ideation through to a functioning solution… 70% of the of the total project time was spent in exchanging helpdesk tickets to get the right sized workbench for that AI practitioner to work on.”

Read the full transcript here.

Footnotes

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