We’ll be running more series like this in the future and will continue to bring on high-level guests all the way up to global chief AI officers at companies as large as IBM. If you’re not already subscribed to The AI in Business Podcast, be sure to subscribe now on your favorite podcast platforms: We distilled actionable insights from true AI experts.Įvery episode centers on one unifying theme: Culture Change for AI Adoption in the Enterprise.
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In the first week of November for the AI in Business podcast, we met with a series of AI luminaries and discussed how to make AI part of a company’s culture and DNA and how to arrive-faster-at AI ROI.
It means setting poor expectations, disappointing leadership, and facing the shut-down of AI projects and initiatives down the road.īut, how-concretely-are AI and IT so different? It comes down to these attributes: To go into AI unprepared means ignoring these differences. Where IT is often predictable, AI sets new predictions. AI means working under a new set of expectations. It’s an entirely new way to work with data, infrastructure, and teams – to open up an entirely new set of capabilities from the ground up. AI is not buying a set of tools for some kind of plug-and-play deployment.
More than lack of data science talent, lack of appropriate culture serves as the largest and most consistent barrier to adoption.įor cultures that have spent so long navigating IT deployments, it’s challenging to switch gears and adopt the changes they need to integrate AI.ĪI is not IT.
Since then, we have found similar frustrations around enterprise culture in every industry. As far back as 2018 when we surveyed over forty banking industry leaders to discover the biggest issues with AI adoption, enterprise culture was already emerging as top of mind.