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One of the biggest challenges companies still face in scaling AI and generative AI (GenAI) today? People. Specifically, finding talent with the right combination of AI expertise, business acumen, and the ability to work effectively with AI agents — so they can solve real business problems with speed and at scale. Some companies try to shortcut the process by hiring so-called “unicorns” — professionals who are equally fluent in machine learning models and strategic decision-making.
IT leaders, data executives, and business decision-makers are moving beyond early GenAI experimentation and shifting their focus to operationalizing AI at scale for measurable impact. But with the landscape evolving rapidly, leaders must navigate key challenges: How do we differentiate in a world where generative AI (GenAI) models are becoming commoditized? How do we prove ROI and justify continued investment in GenAI? How do we implement AI governance without stifling innovation?
By Conor Jensen, Americas Field Chief Data Officer, Dataiku In looking at the costs of developing, using, and maintaining AI systems in research and business settings, there's no denying that companies are casting a critical eye on cost analysis to monitor whether costs will increase.
Conor Jensen Conor Jensen is an experienced Data Science executive with over 15 years working in the analytics space. He is the Vice President of AI Consulting and Data Science for the Americas at Dataiku, helping customers with their biggest and most ambitious AI and Data Science projects. He has worked at a couple of Data Science tech companies for the last few years after taking his lumps building out data science teams and platforms at multiple large enterprises.
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